Patent Publication Number: US-2022236704-A1

Title: Control system, server, apparatus and control method

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     This application is a U.S. bypass application of International Patent Application No. PCT/JP2019/031638 filed on Aug. 9, 2019, the disclosure of which is incorporated herein by reference. 
     The present disclosure relates to a control system, a server, a device and a control method. 
    
    
     BACKGROUND 
     In recent years, device control systems have been proposed that utilize machine learning in which a neural network is used. For example, an air conditioning system has been proposed that includes a plurality of air conditioning devices, a cloud server that is connected to the plurality of air conditioning devices via an internet and that processes, by machine learning, environment information acquired from the air conditioning devices to construct control rules for individually controlling the air conditioning devices (for example, see Patent Literature 1). After constructing the control rules, the cloud server uses the control rules to calculate command values for controlling optimal operating states of the air conditioning devices, and sends the command values to the air conditioning devices via the internet. 
     PATENT LITERATURE 
     
         
         Patent Literature 1: Unexamined Japanese Patent Application Publication No. 2018-123998 
       
    
     However, with the air conditioning system proposed in Patent Literature 1, the environment information and the command values are frequently exchanged between the cloud server and the air conditioning devices via the internet. Consequently, when the communication traffic on the internet increases and the communication speed decreases, maintaining the air conditioning devices in the optimal states may become difficult. 
     The present disclosure is made with the view of the above situation, and an objective of the present disclosure is to provide a control system, a server, a device, and a control method whereby, when executing calculations using a neural network in the device and/or the server, the effects, on the operations of the device, of communication traffic on the network are reduced. 
     SUMMARY 
     A control system according to the present disclosure that achieves the objective described above includes: 
     a server; and a device; wherein 
     the server includes
         a history information acquirer that acquires history information including operation history information expressing a history of a device setting parameter of the device, environment history information expressing a history of an environment in which the device operates, and user information expressing a user of the device,   a coefficient determiner that determines, based on the history information, a first neural network coefficient of a first neural network for calculating a future device setting parameter of the device, the first neural network having a predetermined number of nodes and a predetermined number of layers,   a neural network calculator that calculates the future device setting parameter of the device from an environment parameter, included in the environment history information, indicating an environment at present by using the first neural network, for which the first neural network coefficient is determined by the coefficient determiner, and   a schedule generator that generates, based on the device setting parameter calculated by the neural network calculator, schedule information expressing a future operation schedule of the device, and       

     the device includes
         a device controller that controls the device in accordance with the operation schedule expressed by the schedule information.       

     According to the present disclosure, in the server, the neural network calculator uses the first neural network, for which the first neural network coefficient is determined by the coefficient determiner, to calculate the future device setting parameter of the device from the environment parameter indicating the environment at present included in the environment history information. Additionally, the schedule generator generates, on the basis of the device setting parameter calculated by the neural network calculator, the schedule information expressing the future operation schedule of the device. Moreover, the device controller of the device controls the device in accordance with the operation schedule expressed by the schedule information. As a result, the device can be controlled in accordance with the operation schedule expressed by the schedule information by the device merely sending the history information to the server and acquiring the schedule information from the server every period corresponding to the operation schedule expressed by the schedule information. Therefore, the frequency at which the history information and the schedule information are exchanged between the device and the server is reduced, which leads to the benefit of a reduction of the effects, on the operations of the device, of the communication traffic on the network. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       A more complete understanding of this application can be obtained when the following detailed description is considered in conjunction with the following drawings, in which: 
         FIG. 1  is a schematic configuration drawing of a control system according to Embodiment 1 of the present disclosure; 
         FIG. 2  is a block diagram illustrating the hardware configuration of an air conditioner according to Embodiment 1; 
         FIG. 3  is a block diagram illustrating the functional configuration of the air conditioner according to Embodiment 1; 
         FIG. 4A  is a drawing illustrating an example of a temperature history of a room in which the air conditioner according to Embodiment 1 is installed; 
         FIG. 4B  is a drawing illustrating an example of the temperature history of the room in which the air conditioner according to Embodiment 1 is installed; 
         FIG. 5  is a drawing illustrating an example of information stored in a history information storage according to Embodiment 1; 
         FIG. 6  is a block diagram illustrating the hardware configuration of a water heater according to Embodiment 1; 
         FIG. 7  is a block diagram illustrating the functional configuration of the water heater according to Embodiment 1; 
         FIG. 8A  is a drawing illustrating an example of a temperature history of a bathroom in which the air conditioner according to Embodiment 1 is installed; 
         FIG. 8B  is a drawing illustrating an example of the temperature history of the bathroom in which the air conditioner according to Embodiment 1 is installed; 
         FIG. 9A  is a drawing illustrating an example of information stored in the history information storage according to Embodiment 1; 
         FIG. 9B  is a drawing illustrating an example of the information stored in the history information storage according to Embodiment 1; 
         FIG. 10  is a block diagram illustrating the hardware configuration of a cloud server according to Embodiment 1; 
         FIG. 11  is a block diagram illustrating the functional configuration of the cloud server according to Embodiment 1; 
         FIG. 12  is an operation explanation drawing of a neural network calculator according to Embodiment 1; 
         FIG. 13  is a sequence drawing illustrating an example of the operations of the control system according to Embodiment 1; 
         FIG. 14  is a drawing illustrating an example of history attribute information according to Embodiment 1; 
         FIG. 15  is a flowchart illustrating an example of the flow of device control processing executed by the air conditioner according to Embodiment 1; 
         FIG. 16  is a flowchart illustrating an example of the flow of schedule generation processing executed by the cloud server according to Embodiment 1; 
         FIG. 17  is a flowchart illustrating an example of the flow of coefficient determination processing executed by the cloud server according to Embodiment 1; 
         FIG. 18  is a flowchart illustrating an example of the flow of device setting calculation processing executed by the cloud server according to Embodiment 1; 
         FIG. 19  is a block diagram illustrating the functional configuration of a cloud server according to Embodiment 2; 
         FIG. 20  is a drawing illustrating an example of preference feature amount information according to Embodiment 2; 
         FIG. 21  is a block diagram illustrating the functional configuration of an air conditioner according to Embodiment 2; 
         FIG. 22  is a drawing illustrating an example of information stored in a schedule storage according to Embodiment 2; 
         FIG. 23  is a sequence drawing illustrating an example of the operations of a control system according to Embodiment 2; 
         FIG. 24  is a drawing illustrating an example of history attribute information according to Embodiment 2; 
         FIG. 25  is a flowchart illustrating an example of the flow of preference feature amount information generation processing executed by the cloud server according to Embodiment 2; 
         FIG. 26  is a flowchart illustrating an example of the flow of coefficient determination processing executed by the cloud server according to Embodiment 2; 
         FIG. 27  is a flowchart illustrating an example of the flow of preference feature amount calculation processing executed by the cloud server according to Embodiment 2; 
         FIG. 28  is a block diagram illustrating the hardware configuration of an air conditioner according to Embodiment 3; 
         FIG. 29  is a block diagram illustrating the configuration of a neuro engine according to Embodiment 3; 
         FIG. 30  is a block diagram illustrating the functional configuration of the air conditioner according to Embodiment 3; 
         FIG. 31  is a block diagram illustrating the functional configuration of a cloud server according to Embodiment 3; 
         FIG. 32  is a sequence drawing illustrating an example of the operations of a control system according to Embodiment 3; 
         FIG. 33  is a drawing illustrating an example of coefficient attribute information according to Embodiment 3; 
         FIG. 34  is a flowchart illustrating an example of the flow of device control processing executed by the air conditioner according to Embodiment 3; 
         FIG. 35  is a flowchart illustrating an example of the flow of coefficient information generation processing executed by the cloud server according to Embodiment 3; 
         FIG. 36  is a block diagram illustrating the functional configuration of an air conditioner according to Embodiment 4; 
         FIG. 37  is a block diagram illustrating the functional configuration of a cloud server according to Embodiment 4; 
         FIG. 38  is a sequence drawing illustrating an example of the operations of a control system according to Embodiment 4; 
         FIG. 39  is a flowchart illustrating an example of the flow of device control processing executed by the air conditioner according to Embodiment 4; 
         FIG. 40  is a flowchart illustrating an example of the flow of coefficient information generation processing executed by the cloud server according to Embodiment 4; 
         FIG. 41  is a block diagram illustrating the functional configuration of an air conditioner according to Embodiment 5; 
         FIG. 42  is a block diagram illustrating the functional configuration of a cloud server according to Embodiment 5; 
         FIG. 43  is a sequence drawing illustrating an example of the operations of a control system according to Embodiment 5; 
         FIG. 44  is a sequence drawing illustrating an example of the operations of the control system according to Embodiment 5; 
         FIG. 45  is a flowchart illustrating an example of the flow of device control processing executed by the air conditioner according to Embodiment 5; 
         FIG. 46  is a flowchart illustrating an example of the flow of coefficient information generation processing executed by the cloud server according to Embodiment 5; 
         FIG. 47  is a block diagram illustrating the functional configuration of an air conditioner according to Embodiment 6; 
         FIG. 48  is a block diagram illustrating the functional configuration of a cloud server according to Embodiment 6; 
         FIG. 49  is a sequence drawing illustrating an example of the operations of a control system according to Embodiment 6; 
         FIG. 50  is a flowchart illustrating an example of the flow of device control processing executed by the air conditioner according to Embodiment 6; 
         FIG. 51  is a flowchart illustrating an example of the flow of teacher information sending processing executed by the cloud server according to Embodiment 6; 
         FIG. 52  is a schematic configuration drawing of a control system according to Embodiment 7 of the present disclosure; 
         FIG. 53  is a block diagram illustrating the functional configuration of an air conditioner according to Embodiment 7; 
         FIG. 54  is a block diagram illustrating the hardware configuration of the air conditioner according to Embodiment 7; 
         FIG. 55  is a block diagram illustrating the functional configuration of an air conditioner according to Embodiment 7; 
         FIG. 56  is a sequence drawing illustrating an example of the operations of a control system according to Embodiment 7; 
         FIG. 57  is a flowchart illustrating an example of the flow of device control processing executed by the air conditioner according to Embodiment 7; 
         FIG. 58  is a schematic configuration drawing of a control system according to Embodiment 8 of the present disclosure; 
         FIG. 59  is a block diagram illustrating the functional configuration of an air conditioner according to Embodiment 8; 
         FIG. 60  is a block diagram illustrating the functional configuration of a cloud server according to Embodiment 8; 
         FIG. 61  is a sequence drawing illustrating an example of the operations of the control system according to Embodiment 8; 
         FIG. 62  is a sequence drawing illustrating an example of the operations of a control system according to Embodiment 8; 
         FIG. 63  is a flowchart illustrating an example of the flow of device control processing executed by the air conditioner according to Embodiment 8; 
         FIG. 64  is a flowchart illustrating an example of the flow of the device control processing executed by the air conditioner according to Embodiment 8; 
         FIG. 65  is a sequence drawing illustrating an example of the operations of the control system according to Embodiment 8; 
         FIG. 66  is a flowchart illustrating an example of the flow of device control processing executed by the air conditioner according to Embodiment 8; 
         FIG. 67  is a sequence drawing illustrating an example of the operations of the control system according to Embodiment 8; 
         FIG. 68  is a schematic configuration drawing of a control system according to a modified example; 
         FIG. 69  is a block diagram illustrating the functional configuration of a cloud server according to the modified example; 
         FIG. 70  is a block diagram illustrating the functional configuration of a storage server according to the modified example; 
         FIG. 71  is a drawing illustrating an example of information stored in an NN related information storage according to the modified example; 
         FIG. 72  is a sequence drawing illustrating an example of the operations of a control system according to the modified example; 
         FIG. 73  is a drawing illustrating an example of a display image displayed on a terminal device according to the modified example; 
         FIG. 74  is a sequence drawing illustrating an example of the operations of a control system according to the modified example; 
         FIG. 75  is a block diagram illustrating the configuration of a cloud server according to a modified example; 
         FIG. 76  is a block diagram illustrating the configuration of an air conditioner according to a modified example; 
         FIG. 77  is a block diagram illustrating the configuration of a cloud server according to a modified example; 
         FIG. 78  is a block diagram illustrating the configuration of an air conditioner according to a modified example; and 
         FIG. 79  is a block diagram illustrating the configuration of a cloud server according to a modified example. 
     
    
    
     DETAILED DESCRIPTION 
     Hereinafter, a control system according to embodiments of the present disclosure is described in detail while referencing the drawings. The control system according to each embodiment is a control system that uses a neural network to calculate, on the basis of user information related to a user of a device, a future device setting parameter of the device from an environment parameter indicating an environment of a location at which the device is installed and weather prediction information expressing a future weather condition. 
     Embodiment 1 
     With the control system according to the present embodiment, a server uses a neural network to calculate a future device setting parameter of a device from an environment parameter of a location at which the device is installed and weather prediction information expressing a future weather condition. Here, the neural network has a predetermined number of nodes and a predetermined number of layers, and is for calculating the future device setting parameter of the device. Additionally, the server generates, from the calculated future device setting parameter of the device, schedule information expressing a future operation schedule of the device. The server includes a history information acquirer that acquires, from the device, history information including operation history information expressing a history of the device setting parameter of the device, environment history information expressing a history of an environment in which the device operates, and user information expressing a user of the device; and a weather information acquirer that acquires, from a weather server, weather information including weather record information expressing a past weather condition, and weather prediction information expressing a future weather condition. Additionally, the server includes a coefficient determiner that determines a weighting coefficient of the neural network on the basis of the history information and the weather record information that are acquired; and a neural network calculator that uses a first neural network, for which a first neural network coefficient is determined, to calculate the future device setting parameter of the device from the weather prediction information and an environment parameter, included in the environment history information, indicating an environment at present. Furthermore, the server includes a schedule generator that generates, on the basis of the device setting parameter calculated by the neural network calculator, schedule information expressing a future operation schedule of the device. Moreover, the device includes a device controller that controls the device in accordance with the operation schedule expressed by the schedule information. 
     As illustrated in  FIG. 1 , the control system according to the present embodiment includes air conditioners  4 ,  52  and a water heater  51  that are installed in a house H, and a cloud server  2  capable of communicating via an external network NT 1 . In one example, the external network NT 1  is the internet. Additionally, a weather server  3  is connected to the external network NT 1 . The weather server  3  distributes the weather record information expressing the past weather condition, and the weather prediction information expressing the future weather condition. Operation devices  6 ,  72  for operating the air conditioners  4 ,  52 , the water heater  51 , and an operation device  71  for operating the water heater  51  are installed in the house H. In this case, the air conditioner  4  is installed in a room such as a living room in the house H, and the air conditioner  52  is installed in a bathroom in the house H. Additionally, a router  82  connected to an internal network NT 2 , and a data line terminal device  81  connected to the router  82  and the external network NT 1  are installed in the house H. The internal network NT 2  is implemented as a wired local area network (LAN) or a wireless LAN, for example. The data line terminal device  81  is implemented as a modem, a gateway, or the like. 
     As illustrated in  FIG. 2 , the air conditioner  4  includes a controller  400 , a measuring device  461  that measures a temperature of the room, and an imaging device  481  that images a user of the air conditioner  4 . Note that the measuring device  461  is not limited to a device that measures the temperature of the room, and may be a device that measures an environment parameter indicating another environment of the room such as humidity, brightness, or the like of the room. In one example, a camera that captures an image expressing a temperature distribution of a surface of the user is used as the imaging device  481 . Additionally, the air conditioner  4  includes a compressor (not illustrated in the drawings) and a blowing fan (not illustrated in the drawings) that operate on the basis of command signals input from the controller  400 . 
     The controller  400  includes a central processing unit (CPU)  401 , a main storage  402 , an auxiliary storage  403 , a communication interface  405 , a measuring device interface  406 , a wireless module  407 , an imaging interface  408 , and a bus  409  that connects these components to each other. The main storage  402  is constituted from volatile memory, and is used as a working area of the CPU  401 . The auxiliary storage  403  is configured from non-volatile memory such as a magnetic disk, semiconductor flash memory, or the like, and stores a program for realizing the various functions of the controller  400 . The communication interface  405  is connected to the internal network NT 2 , sends various information notified from the CPU  401  to the internal network NT 2 , and notifies the CPU  401  of various information received from the internal network NT 2 . The wireless module  407  wirelessly communicates with the operation device  6  and, when the wireless module  407  receives, from the operation device  6 , operation information expressing operation content that the user performs on the operation device  6 , the wireless module  407  notifies the CPU  401  of that operation information. When a measurement value signal is input from the measuring device  461 , the measuring device interface  406  generates temperature information corresponding to that measurement value signal, and notifies the CPU  401  of the temperature information. When an image signal is input from the imaging device  481 , the imaging interface  408  generates image information corresponding to that image signal, and notifies the CPU  401  of the image information. Note that the air conditioner  52  has the same hardware configuration as the air conditioner  4 . Additionally, in the case of the air conditioner  52 , the measuring device  461  measures an environment parameter such as temperature, humidity, brightness, or the like of the bathroom of the house H. 
     The CPU  401  reads out the program stored in the auxiliary storage  403  to the main storage  402  and executes the program to function as an environment information acquirer  411 , an image acquirer  412 , an operation receiver  413 , a device controller  414 , a time keeper  415 , a history information generator  416 , a history information sender  417 , a schedule acquirer  418 , a device setting updater  419 , an operation mode setter  420 , and a user identifier  421 , as illustrated in  FIG. 3 . Note that the air conditioner  52  has the same functional configuration. As illustrated in  FIG. 3 , the auxiliary storage  403  illustrated in  FIG. 2  includes a device setting storage  431  that stores device setting information expressing the device setting parameter of the air conditioner  4 , and a user information storage  432  that stores user information about the user of the air conditioner  4 . In one example, the user information storage  432  associates information expressing a position of a region of the surface of the user where heat dissipation is great with user identification information that identifies the user, and stores the associated information. Here, the position is calculated from a temperature distribution of the surface of the user expressed by the image information of each user captured by the imaging device  481 . Additionally, the auxiliary storage  403  includes a history information storage  434  that stores the environment history information and device history information of the air conditioner  4 , a schedule storage  435  that stores schedule information expressing an operation schedule of the air conditioner  4 , and an operation mode storage  433  that stores operation mode information of the air conditioner  4 . 
     The history information storage  434  stores, for every user of the air conditioner  4 , a history of the device setting information of the air conditioner  4  and a history of environment information expressing the environment parameter including the temperature information. In one example, as illustrated in  FIG. 4A , it is assumed that a user that resides at the house H returns to the house H in the winter and, at a date and time T 10  (for example, Jan. 1, 2018 10:00), while the operation mode of the air conditioner  4  is in a manual operation mode, sets a setting temperature to Th 11  (for example, 28° C.), and an air flow level to “high” to operate the air conditioner  4 . At this time, the temperature of the room in which the air conditioner  4  is installed is assumed to be Th 10  (for example, 19° C.). In this case, the room is warmed by the air conditioner  4  and, at a date and time T 11  (for example, Jan. 1, 2018 10:15), which is after the date and time T 10 , the temperature of the room reaches the setting temperature Th 11 . Here, it is assumed that the chilled body of the user is warmed and, as such, the user sets the setting temperature of the air conditioner  4  to Th 12  (for example, 25° C.), which is lower than Th 11 , and sets the air flow level to “low.” In this case, the room is cooled by the air conditioner  4  and, at a date and time T 12  (for example, Jan. 1, 2018 10:20), which is after the date and time T 11 , the temperature of the room reaches the setting temperature Th 12 . Meanwhile, as illustrated in  FIG. 4B , it is assumed that the user that resides at the house H returns to the house H in the summer and, at a date and time T 20  (for example, Jul. 1, 2018 10:00), while the operation mode of the air conditioner  4  is in the manual operation mode, sets the setting temperature to Th 21  (for example, 23° C.), and the air flow level to “high” to operate the air conditioner  4 . At this time, the temperature of the room in which the air conditioner  4  is installed is assumed to be Th 20  (for example, 28° C.). In this case, the room is cooled by the air conditioner  4  and, at a date and time T 21  (for example, Jul. 1, 2018 10:15), which is after the date and time T 20 , the temperature of the room reaches the setting temperature Th 21 . Here, it is assumed that the body of the user is cooled and, as such, the user sets the setting temperature of the air conditioner  4  to Th 22  (for example, 26° C.), which is higher than Th 21 , and sets the air flow level to “low.” In this case, the room is warmed by the air conditioner  4  and, at a date and time T 22  (for example, Jul. 1, 2018 10:20), which is after the date and time T 21 , the temperature of the room reaches the setting temperature Th 22 . In this case, as illustrated in  FIG. 5 , the history information storage  131  associates the operation history information expressing the history of the setting temperature and the air flow level of the air conditioner  4  and the environment history information expressing the history of the indoor temperature of the room with date and time information, and stores the associated information. Here, the history information storage  131  associates the operation history information and the environment history information with user identification information IDU [1] and device identification information IDA [1] identifying the air conditioner  4 , and stores the associated information. 
     Returning to  FIG. 3 , the environment information acquirer  411  acquires, via the measuring device interface  406 , the environment information that is the environment parameter indicating the temperature of the room measured by the measuring device  461 . Note that, when the measuring device  461  measures another environment parameter of the room such as the humidity, the brightness, or the like of the room, the environment information acquirer  411  acquires environment information expressing this other environment parameter. The environment information acquirer  411  stores the acquired environment information chronologically in the history information storage  434 . The image acquirer  412  acquires the image information of the user imaged by the imaging device  481 . 
     When the operation receiver  413  is notified, by the wireless module  407 , of operation information sent from the operation device  6 , the operation receiver  413  receives the notified operation information. Then, when the operation information is related to an update of the device setting parameter of the air conditioner  4 , the operation receiver  413  generates device setting information expressing the device setting parameter corresponding to the operation information, and stores the device setting information in the device setting storage  431 . When the operation information is related to a change of the operation mode of the air conditioner  4 , the operation receiver  413  notifies the operation mode setter  420  of operation mode information expressing the operation mode corresponding to the operation information. The device controller  414  controls the operations of the compressor and the blowing fan on the basis of the device setting information stored in the device setting storage  431 . 
     The user identifier  421  identifies, from the temperature distribution of the surface of the user expressed by the image information acquired by the image acquirer  412 , the region of the surface of the user where heat dissipation is great, and identifies the user of the air conditioner  4  on the basis of the information about the user and the position of the identified region stored in the user information storage  432 . Additionally, the user identifier  421  stores the user identification information of the identified user of the air conditioner  4  in the user information storage  432 . The schedule acquirer  418  acquires, from the cloud server  2 , the schedule information expressing the operation schedule of the air conditioner  4 . and stores the acquired schedule information in the schedule storage  435 . 
     The device setting updater  419  references the operation mode information of the air conditioner  4  stored in the operation mode storage  433  and, when the operation mode is set to an automatic mode, generates the device setting information of the air conditioner  4  on the basis of the schedule information stored in the schedule storage  435  and a time at present measured by the time keeper  415 . Then, the device setting updater  419  stores the generated device setting information in the device setting storage  431 . The device setting updater  419  periodically stores the device setting information, stored in the device setting storage  431 , chronologically in the history information storage  434 . 
     In one example, the time keeper  415  includes a software timer, and measures a date and time at which the environment information acquirer  411  acquires the environment information, a date and time at which the device setting updater  419  stores the device setting information in the history information storage  434 , and a date and time at present. Here, the environment information acquirer  411  associates the acquired environment information with the date and time measured by the time keeper  415  and stores the associated information in the history information storage  434 . Additionally, the device setting updater  419  associates the device setting information acquired from the device setting storage  431  with the date and time measured by the time keeper  415 , and stores the associated information in the history information storage  434 . 
     The history information generator  416  generates history information that includes the environment history information including the plurality of temperature information stored in the history information storage  434 , the user identification information of the user of the air conditioner  4  stored in the user information storage  432 , and the operation history information including the plurality of device setting information stored in the history information storage  434 , and history attribute information corresponding to the history information. In one example, the history information generator  416  generates history attribute information for which the file format is JSON schema, and generates attribute information for which the file format is JSON. The history information sender  417  sends the history information and the history attribute information generated by the history information generator  416  to the cloud server  2 . The history information sender  417  performs reversible information compression processing on the history information and the history attribute information and then sends the processed information. When the operation mode setter  420  is notified of the operation mode information by the operation receiver  413 , the operation mode setter  420  stores the notified operation mode information in the operation mode storage  433 . 
     As illustrated in  FIG. 6 , the water heater  51  includes a controller  500  that controls the water heater  51 , and a measuring device  561  that measures the temperature of the hot water. The controller  500  includes a CPU  501 , a main storage  502 , an auxiliary storage  503 , a communication interface  505 , a measuring device interface  506 , an operation device interface  507 , and a bus  509  that connects these components to each other. The CPU  501 , the main storage  502 , the auxiliary storage  503 , the communication interface  505 , and the measuring device interface  506  are the same as in the air conditioner  4 . The operation device interface  507  is wiredly connected to the operation device  6 , and when the operation device interface  507  receives, from the operation device  6 , operation information expressing operation content performed by the user on the operation device  6 , the operation device interface  507  notifies the CPU  501  of that operation information. 
     The CPU  501  reads out the program stored in the auxiliary storage  503  to the main storage  502  and executes the program to function as an environment information acquirer  511 , an operation receiver  513 , a device controller  514 , a time keeper  515 , a history information generator  516 , a history information sender  517 , a schedule acquirer  518 , a device setting updater  519 , an operation mode setter  520 , and a user identifier  521 , as illustrated in  FIG. 7 . As illustrated in  FIG. 7 , the auxiliary storage  503  illustrated in  FIG. 6  includes a device setting storage  531  that stores device setting information expressing a device setting parameter of the water heater  51 , and a user information storage  532  that stores user information about the user of the water heater  51 , that is, the user of the bathroom. Furthermore, the auxiliary storage  503  includes a history information storage  534  that stores device history information and environment history information of the water heater  51 , a schedule storage  535  that stores schedule information expressing an operation schedule of the water heater  51 , and an operation mode storage  533  that stores operation mode information of the water heater  51 . 
     The history information storage  534  stores, for every user of the water heater  51 , a history of the device setting information of the water heater  51  and a history of environment information expressing an environment parameter including temperature information. In one example, as illustrated in  FIG. 8A , it is assumed that, at a bath time at a date and time T 30  in winter (for example, Jan. 1, 2018 10:00), another user that resides at the house H sets the setting temperature to Th 31  (for example, 27° C.), and the air flow level to “high” while the operation mode of the air conditioner  52  is in the manual operation mode to operate the air conditioner  52 . At this time, the temperature of the bathroom in which the air conditioner  52  is installed is assumed to be Th 30  (for example, 19° C.). Additionally, it is assumed that the bathtub of the bathroom is filled with hot water supplied from the water heater  51 , and that the temperature of the hot water is 42° C. In this case, the bathroom is warmed by the air conditioner  52  and, at a date and time T 31  (for example, Jan. 1, 2018 10:15), which is after the date and time T 30 , the temperature of the bathroom reaches the setting temperature Th 31 . It is assumed that, at this time, the hot water cools with the passage of time, and the temperature thereof has decreased to 40° C. Here, it is assumed that the user feels hot and, as such, sets the setting temperature of the air conditioner  52  to Th 32  (for example, 25° C.), which is lower than Th 31 , and sets the air flow level to “low.” In this case, the bathroom is cooled by the air conditioner  52  and, at a date and time T 32  (for example, Jan. 1, 2018 10:20), which is after the date and time T 31 , the temperature of the bathroom reaches the setting temperature Th 32 . It is assumed that, at this time, the hot water cools with the passage of time, and that the temperature thereof has decreased to 39° C. Meanwhile, as illustrated  FIG. 8B , it is assumed that, at a bath time at a date and time T 40  in autumn (for example, Sep. 1, 2018 10:00), another user that resides at the house H sets the setting temperature to Th 41  (for example, 23° C.), and the air flow level to “high” while the operation mode of the air conditioner  52  is in the manual operation mode to operate the air conditioner  52 . At this time, the temperature of the bathroom in which the air conditioner  52  is installed is assumed to be Th 40  (for example, 29° C.). In this case, the bathroom is cooled by the air conditioner  52  and, at a date and time T 41  (for example, Sep. 9, 2018 10:15), which is after the date and time T 40 , the temperature of the bathroom reaches the setting temperature Th 41 . Here, it is assumed that the body of the user is cooled and, as such, the user sets the setting temperature of the air conditioner  52  to Th 42  (for example, 26° C.), which is higher than Th 41 , and sets the air flow level to “low.” In this case, the bathroom is warmed by the air conditioner  52  and, at a date and time T 42  (for example, Sep. 1, 2018 10:20), which is after the date and time T 41 , the temperature of the bathroom reaches the setting temperature Th 42 . In this case, as illustrated in  FIG. 9A , the history information storage  434  of the air conditioner  52  installed in the bathroom associates the operation history information expressing the history of the setting temperature and the air flow level of the air conditioner  52  and the environment history information expressing the history of the indoor temperature of the bathroom with the date and time information, and stores the associated information. Moreover, as illustrated in  FIG. 9B , the history information storage  534  of the water heater  51  associates the operation history information expressing the history of the setting temperature of the water heater  51  and the environment history information expressing the history of the temperature of the hot water with the date and time information, and stores the associated information. Here, the history information storage  131  associates the operation history information and the environment history information of each of the air conditioner  52  and the water heater  51  with user identification information IDU [ 2 ], device identification information IDA [ 2 ] identifying the air conditioner  52 , and device identification information IDA [ 3 ] identifying the water heater  51 , and stores the associated information. 
     Returning to  FIG. 7 , the environment information acquirer  511  acquires, from the measuring device interface  506 , the temperature information expressing the temperature of the hot water measured by the measuring device  561 . In one example, the user identifier  521  identifies the user by acquiring, from the controller  400  of the air conditioner  52 , the user identification information stored in the user information storage  432  of the controller  400 . Moreover, the user identifier  521  stores, in the user information storage  532 , the user identification information of the identified user of the bathroom. 
     The operation receiver  513  is the same as the operation receiver  413  described above. The device controller  514  controls the water heater  51  on the basis of the device setting information stored in the device setting storage  531 . The schedule acquirer  518  acquires, from the cloud server  2 , the schedule information expressing the operation schedule of the water heater  51 , and stores the acquired schedule information in the schedule storage  535 . 
     When the operation mode of the water heater  51  is set to the automatic mode, the device setting updater  519  generates the device setting information of the water heater  51  on the basis of the schedule information stored in the schedule storage  535  and the time at present measured by the time keeper  515 . Moreover, the device setting updater  519  stores the generated device setting information in the device setting storage  531 . The device setting updater  519  periodically stores the device setting information, stored in the device setting storage  531 , chronologically in the history information storage  434 . The time keeper  515  measures a date and time at which the environment information acquirer  511  acquires the environment information, a date and time at which the device setting updater  519  stores the device setting information in the history information storage  534 , and the date and time at present. Here, the environment information acquirer  511  associates the acquired environment information with the date and time measured by the time keeper  515 , and stores the associated information in the history information storage  534 . Additionally, the device setting updater  519  associates the device setting information acquired from the device setting storage  531  with the date and time measured by the time keeper  515 , and stores the associated information in the history information storage  534 . 
     As illustrated in  FIG. 10 , the cloud server  2  includes a CPU  201 , a main storage  202 , an auxiliary storage  203 , a communication interface  205 , and a bus  209  that connects these components to each other. In one example, the CPU  201  is a multi-core processor. The main storage  202  is constituted from volatile memory, and is used as a working area of the CPU  201 . The auxiliary storage  203  is configured from non-volatile memory that has large capacity, and stores a program for realizing the various functions of the cloud server  2 . The communication interface  205  is connected to the external network NT 1 , and is capable of communicating with the weather server  3  via the external network NT 1 . The CPU  201  reads out the program stored in the auxiliary storage  203  to the main storage  202  and executes the program to function as a history information acquirer  211 , a weather information acquirer  212 , a coefficient setter  213 , a neural network calculator  214 , a coefficient determiner  215 , a schedule generator  216 , and a schedule sender  217 , as illustrated in  FIG. 11 . Additionally, as illustrated in  FIG. 11 , the auxiliary storage  203  illustrated in  FIG. 10  includes a history information storage  231  that stores the history information acquired from the air conditioner  4 , a weather information storage  232  that stores the weather prediction information and the weather record information acquired from the weather server  3 , a neural network storage  233 , and a schedule storage  234  that stores the schedule information to be sent to the air conditioner  4 . 
     The neural network storage  233  stores information expressing a hereinafter described structure of the neural network, and the weighting coefficient of the neural network. The information expressing the structure of the neural network includes information expressing a shape of an activation function at each node, number of layers information, information about the number of nodes in each layer, and the like. Additionally, the neural network storage  233  stores information expressing an initial coefficient that is an initial value of the weighting coefficient used when determining the weighting coefficient of the neural network from the operation history information, the environment history information, and the weather record information of the air conditioners  4 ,  52  and the water heater  51  described above. 
     The history information acquirer  211  acquires, from the air conditioners  4 ,  52  and the water heater  51 , the history information including the operation history information, the environment history information, and the user information. The history information acquirer  211  executes information expansion processing on the history information, which has been subjected to the reversible information compression processing, acquired from the air conditioners  4 ,  52  and the water heater  51  and, then, acquires the operation history information, the environment history information, and the user information included in the history information. The history information acquirer  211  stores, in the history information storage  231 , the operation history information, the environment history information, and the user information that are acquired. The weather information acquirer  212  acquires, from the weather server  3  and via the external network NT 1 , the weather information including the weather record information expressing the past weather condition and the weather prediction information expressing the future weather condition. Here, the weather information acquirer  212  acquires the weather information from the weather server  3  by sending weather information request information requesting, to the weather server  3 , sending of the weather information. 
     The neural network calculator  214  uses a neural network having a predetermined number of nodes and a predetermined number of layers to calculate, from the environment parameter such as the indoor temperature, the hot water temperature, or the like, a numerical value indicating the date and time, and information obtained by quantifying a weather condition, the device setting parameter, such as the setting temperature and the air flow level of the air conditioners  4 ,  52 , the setting temperature of the water heater  51 , and the like, for each time frame of a single day. In this case, the neural network is the first neural network for calculating the future device setting parameter of each of the air conditioners  4 ,  52  and the water heater  51 . As illustrated in  FIG. 12 , this neural network includes an input layer L 10 , a hidden layer L 20 , and an output layer L 30 . The input layer L 10  inputs, to the hidden layer L 20 , the environment parameter such as the indoor temperature, the hot water temperature, or the like, the numerical value indicating the date and time, and the information obtained by quantifying the weather condition. In this case, when, for example, there are four types of weather conditions, namely “sunny”, “cloudy”, “rain”, and “snow”, it is sufficient that a method for quantifying the weather conditions is used in which numerical values NUM 1 , NUM 2 , NUM 3 , and NUM 4  corresponding to the respective weather conditions are set such that the relationship NUM 1 &lt;NUM 2 &lt;NUM 3 &lt;NUM 4  is satisfied. Specifically, it is sufficient that the numerical values corresponding to “sunny”, “cloudy”, “rain”, and “snow” are respectively set to “10”, “20”, “30”, and “40.” 
     The hidden layer L 20  includes N layers (where N is a positive integer) that each include a predetermined number M[j] of nodes x[j, i] (where 1≤i≤M[j], and M[j] is a positive integer). Specifically, the hidden layer L 20  has a structure in which various node rows are connected to each other. Here, an output y[j, i] of each nodes x[j, i] is expressed by the relational expression of Equation (1) below: 
       Equation (1) 
         y [ j,k ]= f (Σ i=1   M[j−1]   W [ j− 1, i,k ]× y [ j− 1, i ])  (1)
 
     Here, W[j, i, k] represents the weighting coefficient, and f(*) represents the activation function. The weighting coefficient W[j, i, k] corresponds to the first neural network coefficient that determines the structure of the neural network described above. A non-linear function such as a sigmoid function, a ramp function, a step function, a softmax function, or the like is used as the activation function. For example, when the activation function is a sigmoid function, the activation function is expressed by the relational expression of Equation (2) below: 
     
       
         
           
             Equation 
             ⁢ 
                 
             2 
           
         
       
       
         
           
             
               
                 
                   
                     y 
                     ⁢ 
                     o 
                   
                   = 
                   
                     
                       f 
                       ⁡ 
                       ( 
                       
                         y 
                         ⁢ 
                         i 
                       
                       ) 
                     
                     = 
                     
                       1 
                       
                         1 
                         + 
                         
                           e 
                           
                             - 
                             yi 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   2 
                   ) 
                 
               
             
           
         
       
     
     Here, yi represents an argument, and yo represents an output value. When the activation function is a ramp function, the activation function is expressed by the relational expression of Equation (3) below: 
       Equation 3 
         yo=f ( yi )=max(0, yi )  (3)
 
     Here, yi represents an argument, and yo represents an output value. 
     The information input to the nodes of the hidden layer L 20  is the sum of products obtained by multiplying the output of each node of the previous layer by the weighting coefficient. The output of the activation function in which the sum is the argument is transmitted to the next layer. The output layer L 30  outputs the output y[j, i] from the ultimate layer of the hidden layer L 20  without modification. 
     The coefficient setter  213  sets the weighting coefficient described above. The neural network calculator  214  uses the neural network, in which the weighting coefficient is set by the coefficient setter  213 , to calculate the future device setting parameters of the air conditioners  4 ,  52  and the water heater  51  from the weather prediction information and the environment parameter indicating the environment at present included in the environment history information. Here, the environment parameter expressing the environment at present is a parameter expressing the indoor temperature acquired from the air conditioners  4 ,  52  or the temperature of the hot water acquired from the water heater  51 . In some cases, due to the measuring frequencies of the measuring devices  461  of the air conditioners  4 ,  52  and the measuring device  561  of the water heater  51  and an acquisition frequency of the environment parameter of the history information acquirer  211 , the environment parameter expressing the current environment is a parameter expressing an environment a few seconds to a few minutes before the present time. Here, the neural network calculator  214  uses the neural network described above to calculate, from the environment parameter such as the indoor temperature at present, the hot water temperature at present, or the like expressed by the environment history information included in the history information, the numerical value indicating the date and time at present, and the information obtained by quantifying the future weather condition expressed by the weather prediction information, the device setting parameter of the air conditioners  4 ,  52  for each time frame of a single day. 
     The coefficient determiner  215  determines the weighting coefficient of the neural network on the basis of the operation history information and the environment history information included in the history information, and the weather record information. Firstly, the coefficient determiner  215  acquires the information expressing the initial coefficient from the neural network storage  233 , and sets the acquired initial coefficient as the weighting coefficient of the neural network. Next, the coefficient determiner  215  acquires the device setting parameter that the neural network calculator  214  calculates using the neural network on the basis of the past environment parameter expressed by the environment history information, the date and time expressed by the date and time information, and information obtained by quantifying the past weather condition expressed by the weather record information. Next, the coefficient determiner  215  acquires the past device setting parameter expressed by the operation history information stored in the history information storage  231 , and calculates an error from the device setting parameter calculated using the neural network. Then, the coefficient determiner  215  determines, on the basis of the calculated error, the weighting coefficient of the neural network by the backpropagation method. Here, the coefficient determiner  215  uses an autoencoder, for example, to determine the weighting coefficient. 
     The coefficient determiner  215  uses dropout information when determining the weighting coefficient of the neural network. The dropout information is defined for each node of the hidden layer L 20  described above, and is information expressing whether the node is deactivated, that is, whether the output of the node x[j, i] is set to “0” when the coefficient determiner  215  determines the weighting coefficient of the neural network. Each node is activated with a predetermined probability P, and is deactivated with a probability (1-P). The probability P is set for every node, and takes a value that is in a range of greater than 0 and less than or equal to 1. When the probability P is set to “1”, the corresponding node is always activated. For example, when rY is a variable that takes “1” with the probability P and takes “0” with the probability (1-P), the output y[j, i] of each node x[j, i] is expressed by the relational expressions of Equations (4) and (5) below: 
       Equation 4 
         y [ j,k ]= f (Σ i=1   M[j−1]   W [ j− 1, i,k ]× y [ j− 1, i ])× rY   (4)
 
       Equation 5 
         rY ˜Bernoulli( P )  (5)
 
     Here, Bernoulli(*) represents a function that takes “1” with probability according to the Bernoulli distribution. 
     The schedule generator  216  generates, on the basis of the device setting parameter calculated by the neural network calculator  214 , schedule information expressing the future operation schedule of each of the air conditioners  4 ,  52  and the water heater  51 . The schedule sender  217  sends the generated schedule information to the air conditioners  4 ,  52  and the water heater  51 . 
     Next, the operations of the control system according to the present embodiment are described while referencing  FIGS. 13 and 14 . Firstly, when a history information generation period arrives, the air conditioners  4 ,  52  and the water heater  51  generate the history information and the history attribute information using the operation history information, the environment history information, the date and time information, and the user information stored in the history information storages  434 ,  534  (step S 1 ). 
     The history information includes protocol information, history information identification information that identifies the generated history information, the operation history information, and the environment history information. The protocol information includes a variety of information related to a communication protocol used when sending the history information to the cloud server  2 . As illustrated in  FIG. 14 , for example, the history attribute information includes the protocol information and a variety of attribute information. Examples of the attribute information include history attribute information identification information that identifies the generated history attribute information, device identification information that identifies the air conditioner  4 ,  52  or the water heater  51 , the user identification information described above, format information, parameter acquisition condition information, device setting type information, environment type information, linked device identification information, linking target information, and operation device identification information. In one example, the history information identification information includes at least one of identification information imparted to the attribute information, identification information imparted to the history information, and identification information of the air conditioner  4 ,  52  or the water heater  51 . The format information includes information expressing a data format or a file format and information expressing a compression format of each of the attribute information and the history information. In one example, the format information includes information expressing that the file format of the attribute information is JSON schema and information expressing that the file format of the history information is JSON. Additionally, the format information includes information expressing a number of history information files, and a file size of each of the history information files. For example, a configuration is possible in which, when flag information included in the format information is “0”, the number of history information files is expressed, when “1”, the file size of the first history information is expressed, when “2” the file size of the second history information is expressed, when “N”, the file size of the Nth history information is expressed, and when “N+1”, the compression format of the history information is expressed. 
     The parameter acquisition condition information includes information expressing acquisition conditions of a variety of parameters such as a period in which the operation history information or the environment history information is acquired, and a time interval for acquiring the device setting parameter and the environment parameter. Additionally, the parameter acquisition condition information may include information expressing a presence/absence of change history of the acquisition conditions of the variety of parameters and, when the acquisition conditions of the variety of parameter have been changed, a change date and time. Here, for example, a configuration is possible in which, when flag information included in the parameter acquisition condition information is “0”, an acquisition date and time of the parameter is expressed, when “1”, an acquisition start date and time of the parameter is expressed, when “2”, an acquisition end date and time of the parameter is expressed, and when “3”, an acquisition interval of the parameter is expressed. The device setting type information is information that supplements the content of the operation history information, and includes information expressing the type of the device setting parameter such as ON/OFF, the setting temperature, the setting air flow, a setting air direction, and the like of each of the air conditioners  4 ,  52  and the water heater  51 . Here, for example, a configuration is possible in which, when flag information included in the device setting type information is “0”, the ON/OFF of each of the air conditioners  4 ,  52  and the water heater  51  is expressed, when “1”, the setting temperature is expressed, when “2”, the setting air flow is expressed, and when “3”, the setting air direction is expressed. The operation device identification information includes information expressing whether the operation device  6 ,  71 ,  72  that set the device setting parameter is a remote controller, a TV, or a mobile terminal such as a smartphone in the house H, or a remote control terminal connected via the cloud server  2 . For example, a configuration is possible in which the operation device identification information is set to “0” when the operation device is the remote controller, to “1” when the mobile terminal, and to “2” when the remote control terminal. 
     The environment type information is information that supplements the content of the environment history information, and includes information expressing the type of the environment parameter such as a room temperature, an air temperature outside the house H, whether a person is detected in the house H, the temperature of the surface of the person residing in the house H, a detection state by a smell sensor, a CO2 concentration, a concentration of microparticles (for example, PM 2.5) in the air, and the like. Here, for example, a configuration is possible in which, when flag information included in the environment type information is “0”, the room temperature is expressed, when “1”, humidity is expressed, when “2”, the outside air temperature is expressed, and when “3”, whether a person is detected is expressed. Additionally, the environment type information includes the weather information. In one example, the linked device identification information includes identification information of a device that operates linked with the air conditioner  4 ,  52  or the water heater  51 . In one example, the linking target information includes identification information of an operation state of a device that is to be linked with the air conditioner  4 ,  52  or the water heater  51 . In one example, the linked device identification information includes identification information of a ventilation fan that is linked with the water heater  51 . In such a case, in one example, the linking target information includes information expressing that an operation of the ventilation fan linked with the water heater  51  is an ON/OFF operation. 
     Returning to  FIG. 13 , thereafter, the generated history information is sent from the air conditioners  4 ,  52  and the water heater  51  to the cloud server  2  (step S 2 ). When the cloud server  2  receives the history information, the cloud server  2  stores, in the history information storage  231 , the operation history information, the environment history information, the date and time information, and the user information included in the history information. Next, weather information request information requesting, to the weather server  3 , sending of the weather information including the weather prediction information and the weather record information is sent from the cloud server  2  to the weather server  3  (step S 3 ). Meanwhile, when the weather server  3  receives the weather information request information, the weather server  3  identifies the weather prediction information and the weather record information of the region in which the house H exists, and generates weather information including the weather prediction information and the weather record information that are identified (step S 4 ). Next, the generated weather information is sent from the weather server  3  to the cloud server  2  (step S 5 ). Meanwhile, when the cloud server  2  receives the weather information, the cloud server  2  stores, in the weather information storage  232 , the weather record information and the weather prediction information included in the received weather information. Then, the cloud server  2  determines the weighting coefficient of the neural network on the basis of the operation history information, the environment history information, the date and time information, the user information, and the weather record information that are acquired (step S 6 ). 
     Next, it is assumed that the air conditioner  4 ,  52  or the water heater  51  receives a switching operation performed by the user for switching to the automatic mode (step S 7 ). In this case, the operation mode is set to the automatic mode by the air conditioner  4 ,  52  or the water heater  51  storing, in the operation mode storage  433 ,  533 , operation mode information expressing that the operation mode is the automatic mode (step S 8 ). Next, when the air conditioner  4 ,  52  or the water heater  51  determines that a predetermined update period of the operation schedule of the air conditioner  4 ,  52  or the water heater  51  has arrived, schedule request information requesting, to the cloud server  2 , sending of the schedule information is sent from the air conditioner  4 ,  52  or the water heater  51  to the cloud server  2  (step S 9 ). Meanwhile, when the cloud server  2  receives the schedule request information, the cloud server  2  uses the neural network described above to calculate, from the environment parameter at present, the numerical value indicating the date and time, and the information obtained by quantifying the weather condition, a device setting parameter indicating the setting temperature and the air flow level of the air conditioners  4 ,  52 , the setting temperature and the like of the water heater  51 , in each time frame of a single day. Then, the cloud server  2  uses the calculated device setting parameter to generate the schedule information (step S 10 ). Next, the generated schedule information is sent from the cloud server  2  to the air conditioner  4 ,  52  or the water heater  51  (step S 11 ). Meanwhile, when the air conditioner  4 ,  52  or the water heater  51  receives the schedule information, the air conditioner  4 ,  52  or the water heater  51  stores the received schedule information in the schedule storage  435 ,  535 . Thereafter, it is assumed that the air conditioner  4 ,  52  or the water heater  51  references the schedule information stored in the schedule storage  435 ,  535  to determine that the update period of the device setting information has arrived. In such a case, the air conditioner  4 ,  52  or the water heater  51  updates, on the basis of the schedule information, the device setting information stored in the device setting storage  431 ,  531  (step S 12 ). Thereafter, the processing of the aforementioned step S 12  is repeatedly executed every time the update period of the device setting information arrives. 
     Next, device control processing executed by the air conditioner  4 ,  52  according to the present embodiment is described while referencing  FIG. 15 . In one example, this device control processing starts when the power to the air conditioner  4 ,  52  is turned ON. Note that device control processing that is the same as the device control processing described in the following is executed for the water heater  51  as well. In the following, device control processing for the air conditioner  4 ,  52  is described. 
     Firstly, the history information generator  416  determines whether the history information generation period for generating history information to be sent to the cloud server  2  has arrived (step S 101 ). When the history information generator  416  determines that the history information generation period has not arrived (step S 101 ; No), the processing of hereinafter described step S 105  is executed with no modification. 
     However, it is assumed that the history information generator  416  determines that the history information generation period has arrived (step S 101 ; Yes). In this case, the history information generator  416  acquires the operation history information and the environment history information from the history information storage  434  (step S 102 ). Next, the history information generator  416  uses the operation history information and the environment history information that are acquired, the date and time information, and the user information stored in the user information storage  432  to generate history information that includes these pieces of information (step S 103 ). Next, the history information sender  417  sends the generated history information to the cloud server  2  (step S 104 ). 
     Thereafter, the operation receiver  413  determines whether a change operation of the operation mode of the air conditioner  4  is received (step S 105 ). Specifically, the operation receiver  413  determines whether operation information related to a change of the operation mode of the air conditioner  4  is received. When the operation receiver  413  determines that the change operation of the operation mode of the air conditioner  4  is not received (step S 105 ; No), the processing of hereinafter described step S 108  is executed without modification. However, when the operation receiver  413  determines that operation information related to a change of the operation mode of the air conditioner  4  is received (step S 105 ; Yes), the operation mode setter  420  updates the operation mode information stored in the operation mode storage  433  (step S 106 ). Next, the schedule acquirer  418 ,  518  references the operation mode information stored in the operation mode storage  433  to determine whether the operation mode of the air conditioner  4 ,  52  or the water heater  51  is the automatic mode (step S 107 ). When the schedule acquirer  418  determines that the operation mode of the air conditioner  4 ,  52  or the water heater  51  is the manual mode (step S 107 ; No), the processing of step S 101  is executed again. However, when the schedule acquirer  418  determines that the operation mode of the air conditioner  4 ,  52  or the water heater  51  is the automatic mode (step S 107 ; Yes), the schedule acquirer  418  determines whether a schedule update period has arrived (step S 108 ). When the schedule acquirer  418  determines that the schedule update period has not arrived (step S 108 ; No), the processing of hereinafter described step S 112  is executed without modification. However, it is assumed that the schedule acquirer  418  determines that the schedule update period has arrived (step S 108 ; Yes). In this case, the schedule acquirer  418  sends the schedule request information described above to the cloud server  2  (step S 109 ) to acquire the schedule information from the cloud server  2  (step S 110 ). The schedule acquirer  418  stores the acquired schedule information in the schedule storage  435 . Then, a device setting information generator  116  references the schedule information stored in the schedule storage  435  to determine whether an update period of the device setting information of the air conditioner  4 ,  52  or the water heater  51  has arrived (step S 111 ). When the device setting information generator  116  determines that the update period of the device setting information of the air conditioner  4 ,  52  or the water heater  51  has not arrived (step S 111 ; No), the processing of step S 101  is executed again. However, when the device setting information generator  116  determines that the update period of the device setting information of the air conditioner  4 ,  52  or the water heater  51  has arrived (step S 111 ; Yes), the device setting information generator  116  updates the device setting information on the basis of the schedule information stored in the schedule storage  435  (step S 113 ). Then, the processing of step S 101  is executed again. 
     Next, schedule generation processing executed by the cloud server  2  according to the present embodiment is described while referencing  FIGS. 16 to 18 . In one example, this schedule generation processing starts when the power to the cloud server  2  is turned ON. 
     Firstly, as illustrated in  FIG. 16 , the history information acquirer  211  determines whether the history information is acquired from the air conditioner  4 ,  52  or the water heater  51  (step S 201 ). When the history information acquirer  211  determines that the history information is not acquired (step S 201 ; No), the processing of hereinafter described step S 206  is executed without modification. However, when the history information acquirer  211  determines that the history information is acquired (step S 201 ; Yes), the history information acquirer  211  stores the acquired history information in the history information storage  231  (step S 202 ). Next, the weather information acquirer  212  sends weather information request information requesting, to the weather server  3 , sending of the weather information (step S 203 ) to acquire the weather information from the weather server  3  (step S 204 ). Here, the weather information acquirer  212  stores, in the weather information storage  232 , the weather prediction information and the weather record information included in the acquired weather information. Next, coefficient determination processing for determining, on the basis of the operation history information and the environment history information included in the history information and the weather record information, the coefficient of the neural network described above is executed (step S 205 ). 
     Here, details of the coefficient determination processing are described in detail while referencing  FIG. 17 . Firstly, the neural network calculator  214  acquires the operation history information, the environment history information, and the date and time information from the history information storage  231 , and acquires the weather record information from the weather information storage  232  (step S 301 ). The operation history information, the environment history information, and the date and time information correspond to teacher information for training the neural network. Next, the coefficient setter  213  acquires, from the neural network storage  233 , information expressing an initial weighting coefficient that is an initial value of the weighting coefficient, and sets the weighting coefficient of the neural network to the initial weighting coefficient (step S 302 ). Next, the neural network calculator  214  uses the neural network in which the initial weighting coefficient is set to calculate, from the environment parameter included in the acquired environment history information, the date and time expressed by the date and time information, and the information obtained by quantifying the weather condition expressed by the weather record information, the device setting parameter at each of a plurality of time frames on a predetermined day (step S 303 ). Then, for each of the plurality of time frames, the coefficient determiner  215  calculates the error between the calculated device setting parameter and the device setting parameter included in the operation history information (step S 304 ). Next, the coefficient determiner  215  determines, on the basis of the calculated error, each weighting coefficient by the backpropagation method (step S 305 ). Then, the coefficient determiner  215  stores the determined weighting coefficients in the neural network storage  233  (step S 306 ). 
     Returning to  FIG. 16 , next, the schedule generator  216  determines whether the schedule request information is acquired from the air conditioner  4 ,  52  or the water heater  51  (step S 206 ). When the schedule generator  216  determines that the schedule request information is not acquired (step S 206 ; No), the processing of step S 201  is executed again. However, when the schedule generator  216  determines that the schedule request information is acquired (step S 206 ; Yes), device setting calculation processing is executed (step S 207 ). 
     Here, details of the device setting calculation processing are described in detail while referencing  FIG. 18 . Firstly, the neural network calculator  214  acquires, from the history information storage  231 , the environment parameter at present and the date and time respectively included in the environment history information and the date and time information, and acquires the weather prediction information from the weather information storage  232  (step S 401 ). Next, the coefficient setter  213  acquires, from the neural network storage  233 , the weighting coefficient determined in the coefficient determination processing, and sets the weighting coefficient of the neural network to the acquired weighting coefficient (step S 402 ). Then, the neural network calculator  214  uses the neural network in which the weighting coefficient is set to calculate the future device setting parameter from the environment parameter at present, the date and time expressed by the date and time information, and the information obtained by quantifying the weather condition expressed by the weather prediction information that are acquired (step S 403 ). 
     Returning to  FIG. 16 , thereafter, the schedule generator  216  uses the calculated device setting parameter to generate the schedule information (step S 208 ). Here, the schedule generator  216  stores the generated schedule information in the schedule storage  234 . Next, the schedule sender  217  sends the schedule information stored in the schedule storage  234  to the air conditioner  4 ,  52  or the water heater  51  (step S 209 ). Then, the processing of step S 201  is executed again. 
     As described above, with the control system according to the present embodiment, in the cloud server  2 , the neural network calculator  214  uses the neural network, for which the weighting coefficient is determined by the coefficient determiner  215 , to calculate the future device setting parameters of the air conditioners  4 ,  52  and the water heater  51  from the weather prediction information and the environment parameter at present included in the environment history information. Additionally, the schedule generator  216  generates, on the basis of the device setting parameters calculated by the neural network calculator  214 , the future operation schedules of the air conditioners  4 ,  52  and the water heater  51 . Meanwhile, the device setting updater  419 ,  519  of the air conditioner  4 ,  52  or the water heater  51  updates the device setting information stored in the device setting storage  431 ,  531  in accordance with the operation schedule expressed by the schedule information, and the device controller  414 ,  514  controls the air conditioner  4 ,  52  or the water heater  51  on the basis of the device setting parameter expressed by the device setting information stored in the device setting storage  431 ,  531 . As a result, the air conditioner  4 ,  52  or the water heater  51  can be controlled as a result of the air conditioner  4 ,  52  or the water heater  51  merely sending the history information to the cloud server  2  and acquiring the schedule information from the cloud server  2  every period corresponding to the operation schedule expressed by the schedule information. Therefore, the frequency at which the history information and the schedule information are exchanged between the air conditioner  4 ,  52  or the water heater  51  and the cloud server  2  is reduced, which leads to the benefit of a reduction of the effects, on the operations of the air conditioner  4 ,  52  or the water heater  51 , of the communication traffic on the external network NT 1 . 
     With the control system according to the present embodiment, the air conditioner  4 ,  52  or the water heater  51  sends the history information related to the air conditioner  4 ,  52  or the water heater  51  to the cloud server  2  as teacher information, and the cloud server  2  generates the schedule information on the basis of the device setting parameter calculated by the neural network calculator  214 . As a result, the air conditioner  4  can be operated on an operation schedule suited to the physical features or lifestyle of the user, without being provided with a neuro engine. 
     Furthermore, with the control system according to the present embodiment, the air conditioner  4 ,  52  or the water heater  51  acquires, from the air conditioner  4 ,  52  or the water heater  51 , the user information and sends the acquired user information to the cloud server  2 . As a result, the cloud server  2  determines the weighting coefficient of the neural network in consideration of the content of the user information and, as such, when, for example, the user of the air conditioner  4 ,  52  or the water heater  51  changes (for example, when the user changes from a father, a mother, a son, or a daughter to a grandmother), an environment suited to that user can be provided. 
     Embodiment 2 
     With a control system according to the present embodiment, a server uses a second neural network to calculate a preference feature amount from operation history information expressing a history of a device setting parameter of a device, environment history information of a location at which the device is installed, and weather record information expressing a past weather condition. Here, the second neural network has a predetermined number of nodes and a predetermined number of layers and is for calculating a preference feature amount indicating a feature amount of a preference of a user. Additionally, the preference feature amount is information obtained by quantifying the feature amount of the preference of the user of the device. The server includes a history information acquirer that acquires history information including operation history information expressing a history of the device setting parameter of the device, environment history information expressing a history of an environment in which the device operates, and user information expressing the user of the device; and a weather information acquirer that acquires, from a weather server, weather information including weather record information expressing a past weather condition. Additionally, the server includes a coefficient determiner that determines a weighting coefficient of the second neural network on the basis of the weather record information and the history information, and a neural network calculator that uses the second neural network, for which the weighting coefficient is determined by the coefficient determiner, to calculate the preference feature amount from the history information and the weather record information. Moreover, the device includes a schedule storage that associates a plurality of types of schedule information expressing an operation schedule of the device with the preference feature amount information, and stores the associated information, a schedule identifier that identifies schedule information corresponding to the preference feature amount calculated by the neural network calculator, and a device controller that controls the device in accordance with the operation schedule expressed by the schedule information identified by the schedule identifier. 
     As with the control system described using  FIG. 1  in Embodiment 1, the control system according to the present embodiment includes an air conditioner and a water heater installed in a house H, and a cloud server illustrated in  FIG. 19  that is capable of communicating with the air conditioner and the water heater via an external network NT 1 . Note that, in the present embodiment, constituents that are the same as in Embodiment 1 are denoted with the same reference numerals used in Embodiment 1. Additionally, only the air conditioner is described in the present embodiment. The water heater executes the same processing as the air conditioner. Additionally, it is assumed that an internal network NT 2  is laid and a router and a data line terminal device that are connected to the internal network NT 2  are installed in the house H. 
     As described using  FIG. 2 , an air conditioner  15004  according to the present embodiment can identify the user by using an image captured by an imaging device  481 . With the control system according to the present embodiment, in the air conditioner  15004 , user feature amount information expressing a physical feature of the user is generated from the image obtained by using the imaging device  481  to image the user. Then, the generated user feature amount information is sent from the air conditioner  15004  to a cloud server  15002 . Meanwhile, the cloud server  15002  determines the weighting coefficient of the neural network on the basis of the operation history information, the environment history information, and the preference feature amount stored in a teacher information storage  15235 . Here, the cloud server  15002  uses the neural network to determine which category of a plurality of types of physical features to classify the user into. Examples of the plurality of types of physical features include “sensitive to heat (a person that, as an individual physical feature, is relatively sensitive to heat)” and “sensitive to cold (a person that, as an individual physical feature, is relatively sensitive to cold).” Then, the cloud server  15002  sends the preference feature amount information corresponding to the determined category to the air conditioner  15004 . As a result, the air conditioner  15004  operates in accordance with the operation schedule expressed by the schedule information corresponding to the category of “sensitive to heat”, for example. In the present embodiment, a configuration is possible in which the air conditioner  15004  is not provided with a function for performing the calculations of the neural network. 
     The hardware configuration of the cloud server  15002  according to the present embodiment is the same as the hardware configuration of the cloud server  2  described using  FIG. 10  in Embodiment 1. With the cloud server  15002 , the CPU  201  reads out the program stored in the auxiliary storage  203  to the main storage  202  and executes the program to function as a history information acquirer  211 , a weather information acquirer  212 , a coefficient setter  15213 , a neural network calculator  214 , a coefficient determiner  15215 , a preference feature amount information generator  15217 , and a preference feature amount sender  15218 , as illustrated in  FIG. 19 . Additionally, as illustrated in  FIG. 19 , the auxiliary storage  203  illustrated in  FIG. 10  includes a history information storage  231  that stores the history information and history attribute information acquired from the air conditioner  15004 , a weather information storage  232  that stores the weather record information acquired from the weather server  3 , a neural network storage  15233 , and the teacher information storage  15235 . Note that, in  FIG. 19 , the constituents that are the same as in Embodiment 1 are denoted with the same reference numerals as used in  FIG. 11 . The teacher information storage  15235  stores teacher information that is used by the coefficient determiner  15215  to determine the neural network coefficient. The teacher information is information obtained by combining environment information expressing a history of an indoor environment parameter of the house H, operation history information expressing a history of a setting parameter of the air conditioner  15004  installed in the house H, and the preference feature amount indicating the feature amount of the preference of the user of the air conditioner  15004 . Here, the preference feature amount is obtained by categorizing the feature of the preference of the user when using the air conditioner  15004 . Regarding the preference feature amount, as illustrated in  FIG. 20 , in the case of, for example, tendencies of starting cooling operation even when the indoor temperature is about 26° C. or lower, lowering a cooling setting temperature with high frequency, cooling at high power regardless of the indoor temperature, and, after the indoor temperature has decreased due to the cooling operation, not reducing the operation level even after a certain amount of time elapses, it is predictable that the user is sensitive to heat, and the preference feature amount for the combination of the environment history information and the operation history information expressed by these tendencies is characterized as “sensitive to heat”, expressed by “10.” Additionally, in the case of, for example, tendencies of starting heating operation even when the indoor temperature is about 18° C. or higher, raising a heating setting temperature with high frequency, heating at high power regardless of the indoor temperature, and, after the indoor temperature has risen due to the heating operation, not reducing the operation level even after a certain amount of time elapses, it is predictable that the user is sensitive to cold, and the preference feature amount for the combination of the environment history information and the operation history information expressed by these tendencies is characterized as “sensitive to cold”, expressed by “20.” 
     The teacher information stored in the teacher information storage  15235  may be automatically created by a program executed by the cloud server  15002  or another information processing device (not illustrated in the drawings) other than the cloud server  15002 . Alternatively, an administrator that administrates the cloud server  15002  may create the teacher information by artificially defining the preference feature from the environment history information and the operation history information that is collected from the air conditioner  15004  at any time. Additionally, when estimating the preference feature amount, the weather record information may be used in addition to the operation history information and the environment history information. A configuration is possible in which, on a hot or very hot summer day, for example, when the cooling operation is started or the cooling setting temperature is lowered regardless of the indoor environment, the user of the air conditioner  15004  is estimated to be “sensitive to heat”, and the preference feature amount for the combination of the environment history information and the operation history information corresponding to these operations is characterized as “sensitive to heat”, expressed by “10.” 
     The neural network storage  15233  stores information expressing a hereinafter discussed structure of the neural network, and the weighting coefficient of the neural network. The information expressing the structure of the neural network includes information expressing a shape of an activation function at each node, number of layers information, information about the number of nodes in each layer, and the like. Additionally, the neural network storage  233  stores information expressing an initial coefficient that is an initial value of the weighting coefficient used when determining the weighting coefficient of the neural network from the operation history information, the environment history information, and the weather record information of the air conditioners  4 ,  52  and the water heater  51  described above. 
     The neural network calculator  214  uses a neural network having a predetermined number of nodes and a predetermined number of layers to calculate the preference feature amount indicating the feature of the preference of the user from the operation history information, the environment history information, and the weather record information. Here, the neural network is the second neural network for calculating the preference feature amount indicating the feature of the preference of the user. 
     The coefficient setter  15213  sets the weighting coefficient of the neural network. Then, the neural network calculator  214  uses the neural network, in which the weighting coefficient is set by the coefficient setter  15213 , to calculate the preference feature amount indicating the feature of the preference of the user of the air conditioners  4 ,  52  and the water heater  51  from the weather record information, the operation history information, and the environment history information. The neural network calculator  214  uses the neural network to calculate the preference feature amount from the operation history information, the environment history information, and information obtained by quantifying a past weather condition expressed by the weather record information. 
     The coefficient determiner  15215  determines the weighting coefficient of the neural network on the basis of the preference feature amount information, the operation history information, the environment history information, and the weather record information. Firstly, the coefficient determiner  15215  acquires, from the neural network storage  15233 , the information expressing the initial coefficient, and sets the acquired initial coefficient as the weighting coefficient of the neural network. Next, the coefficient determiner  15215  acquires the preference feature amount that the neural network calculator  214  uses the neural network to calculate on the basis of the operation history information, the environment history information, and the information obtained by quantifying the past weather condition expressed by the weather record information that are stored in the teacher information storage  15235 . Then, the coefficient determiner  15215  acquires, from the teacher information storage  15235 , the preference feature amount information corresponding to the combination of the operation history information and the environment history information, and calculates an error with the preference feature amount calculated using the neural network. Then, the coefficient determiner  15215  determines, on the basis of the calculated error, the weighting coefficient of the neural network by the backpropagation method. 
     When the preference feature amount information generator  15217  receives preference feature amount request information from the air conditioner  15004 , the preference feature amount information generator  15217  causes the neural network calculator  214  to calculate the preference feature amount. Then, the preference feature amount information generator  15217  generates preference feature amount information expressing the calculated preference feature amount. The preference feature amount sender  15218  sends the generated preference feature amount information to the air conditioner  15004  that is the sender of the preference feature amount request information. 
     The CPU  401  of the air conditioner  15004  according to the present embodiment reads out the program stored in the auxiliary storage  403  to the main storage  402  and executes the program to function as an environment information acquirer  411 , an image acquirer  412 , an operation receiver  413 , a device controller  414 , a time keeper  415 , a history information generator  416 , a history information sender  417 , a preference feature amount acquirer  15418 , a device setting updater  419 , an operation mode setter  420 , a user identifier  421 , and a schedule identifier  15425 , as illustrated in  FIG. 21 . Additionally, as illustrated in  FIG. 20 , the auxiliary storage  403  includes a device setting storage  431 , a user information storage  432 , an operation mode storage  433 , a history information storage  434 , and a schedule storage  15435 . 
     As illustrated in  FIG. 22 , for example, the schedule storage  15435  associates a plurality of types of schedule information with the preference feature amount information expressing the preference feature amount, and stores the associated information. Here, the feature of the preference of the user is categorized on the basis of, for example, the physical feature of the user, and the preference feature amount is information obtained by quantifying each preference. For example, a configuration is possible in which, for the preference feature amount information, “10” is assigned to “sensitive to heat”, “20” is assigned to “sensitive to cold”, “30” is assigned to “sensitive to heat at first, but immediately lowers settings when the room cools”, “40” is assigned to “sensitive to heat only immediately after returning home”, “90” is assigned to “sensitive to heat only during time frame after finishing bathing”, “100” is assigned to “sensitive to heat during mealtimes”, “110” is assigned to “uses air conditioner rarely”, and “120” is assigned to “only uses on extremely hot days.” 
     The preference feature amount acquirer  15418  acquires the preference feature amount information from the cloud server  15002 , and notifies the schedule identifier  15425  of the acquired preference feature amount information. The schedule identifier  15425  identifies, from the plurality of types of schedule information stored in the schedule storage  15435 , the schedule information corresponding to the preference feature amount calculated by the neural network calculator  214  and acquired by the preference feature amount acquirer  15418 . Then, the device setting updater  419  updates, on the basis of the schedule information identified by the schedule identifier  15425 , the device setting information stored in the device setting storage  431 . 
     Next, the operations of the control system according to the present embodiment are described while referencing  FIGS. 23 and 24 . Note that, in  FIG. 23 , the processes that are the same as in Embodiment 1 are denoted with the same reference numerals as used in  FIG. 13 . Firstly, the cloud server  15002  determines the weighting coefficient of the neural network on the basis of the operation history information, the environment history information, and the preference feature amount information that are acquired from the teacher information storage  15235  (step S 15001 ). Next, it is assumed that the air conditioner  4 ,  52  or the water heater  51  receives a switching operation performed by the user for switching to the automatic mode (step S 15002 ), and the operation mode is set to the automatic mode (step S 15003 ). 
     Thereafter, when a history information generation period arrives, the air conditioner  15004  generates the history information and the history attribute information using the operation history information, the environment history information, the date and time information, and the user information stored in the history information storages  434 ,  534  (step S 15004 ). In one example, the history attribute information has a structure such as that illustrated in  FIG. 24 . Next, the generated history information and history attribute information are sent from the air conditioner  15004  to the cloud server  2  (step S 15005 ). 
     Next, when the air conditioner  15004  determines that a predetermined update period of the operation schedule of the air conditioner  15004  has arrived, schedule request information requesting, to the cloud server  15002 , sending of the schedule information is sent from the air conditioner  15004  to the cloud server  15002  (step S 15006 ). Meanwhile, when the cloud server  15002  receives the schedule request information, weather record request information requesting, to the weather server  3 , sending of the weather record information is sent from the cloud server  15002  to the weather server  3  (step S 15007 ). Meanwhile, when the weather server  3  receives the weather record request information, the weather server  3  generates weather record information of the region in which the house H, in which the air conditioner  4 ,  52  or the water heater  51  is installed, exists (step S 15008 ). Then, the generated weather record information is sent from the weather server  3  to the cloud server  15002  (step S 15009 ). Next, the cloud server  15002  uses the neural network to calculate the preference feature amount of the user from the operation history information, the environment history information, and the weather record information (step S 15010 ). Then, the cloud server  15002  generates preference feature amount information expressing the calculated preference feature amount (step S 15011 ). Next, the generated preference feature amount information is sent from the cloud server  15002  to the air conditioner  15004  (step S 15012 ). Meanwhile, when the air conditioner  15004  receives the preference feature amount information, the air conditioner  15004  identifies the schedule information corresponding to the received preference feature amount from among the plurality of types of schedule information stored in the schedule storage  15435  (step S 15013 ). Then, the air conditioner  15004  updates, on the basis of the schedule information, the device setting information stored in the device setting storage  431  (step S 12 ). Thereafter, the processing of the aforementioned step S 12  is repeatedly executed every time the update period of the device setting information arrives. 
     Next, preference feature amount information generation processing executed by the cloud server  15002  according to the present embodiment is described while referencing  FIGS. 25 to 27 . In one example, this preference feature amount information generation processing starts when the power to the cloud server  15002  is turned ON. 
     Firstly, as illustrated in  FIG. 25 , coefficient determination processing is executed for determining the coefficient of the neural network on the basis of the operation history information, the environment history information, the weather record information, and the preference feature amount information that are acquired from the teacher information storage  15235  (step S 15201 ). 
     Here, details of the coefficient determination processing are described in detail while referencing  FIG. 26 . Firstly, the neural network calculator  214  acquires the operation history information, the environment history information, and the date and time information from the teacher information storage  15235 , and acquires the weather record information from the weather information storage  232  (step S 15301 ). Next, the coefficient setter  15213  acquires, from the neural network storage  15233 , information expressing an initial weighting coefficient that is an initial value of the weighting coefficient, and sets the weighting coefficient of the neural network to the initial weighting coefficient (step S 15302 ). Next, the neural network calculator  214  uses the neural network for which the initial weighting coefficient is set to calculate the preference feature amount from the environment parameter included in the environment history information, the date and time expressed by the date and time information, and the information obtained by quantifying the weather condition expressed by the weather record information that are acquired (step S 15303 ). Then, the coefficient determiner  15215  acquires, from the history information storage  231 , the preference feature amount information included in the history attribute information, and calculates an error between the calculated preference feature amount and the preference feature amount expressed by the acquired preference feature amount information (step S 15304 ). Next, the coefficient determiner  15215  determines, on the basis of the calculated error, the weighting coefficient of the neural network by the backpropagation method (step S 15305 ). Then, the coefficient determiner  15215  stores the determined weighting coefficient in the neural network storage  15233  (step S 15306 ). 
     Returning to  FIG. 25 , the history information acquirer  211  determines whether the history information is acquired from the air conditioner  15004  (step S 15202 ). When the history information acquirer  211  determines that the history information is not acquired (step S 15202 ; No), the processing of hereinafter described step S 15204  is executed without modification. However, when the history information acquirer  211  determines that the history information is acquired (step S 15202 ; Yes), the history information acquirer  211  stores the acquired history information in the history information storage  231  (step S 15203 ). Next, the preference feature amount information generator  15217  determines whether the preference feature amount request information is acquired from the air conditioner  15004  (step S 15204 ). When the preference feature amount information generator  15217  determines that the preference feature amount request information is not acquired (step S 15204 ; No), the processing of step S 15201  is executed again. However, when the preference feature amount information generator  15217  determines that the preference feature amount request information is acquired (step S 15204 ; Yes), preference feature amount calculation processing is executed (step S 15205 ). 
     Here, details of the preference feature amount calculation processing are described in detail while referencing  FIG. 27 . Firstly, the neural network calculator  214  acquires the environment history information and the operation history information from the history information storage  231  (step S 15401 ). Next, the weather information acquirer  212  sends the weather record request information requesting, to the weather server  3 , sending of the weather record information (step S 15402 ) to acquire the weather record information from the weather server  3  (step S 15403 ). Here, the weather information acquirer  212  stores the acquired weather record information in the weather information storage  232 . Then, the coefficient setter  15213  acquires, from the neural network storage  15233 , the weighting coefficient determined in the coefficient determination processing, and sets the weighting coefficient of the neural network to the acquired weighting coefficient (step S 15404 ). Thereafter, the neural network calculator  214  uses the neural network in which the weighting coefficient is set to calculate, from the acquired environment history information and operation history information, and the information obtained by quantifying the weather condition expressed by the weather record information, the preference feature amount that is the feature amount of the preference of the user (step S 15405 ). 
     Returning to  FIG. 25 , next, the preference feature amount information generator  15217  generates preference feature amount information expressing the preference feature amount calculated by the neural network calculator  214  (step S 15206 ). Next, the preference feature amount sender  15218  sends the generated preference feature amount information to the air conditioner  15004  (step S 15207 ). Then, the processing of step S 15201  is executed again. 
     As described above, with the control system according to the present embodiment, in the cloud server  2 , the neural network calculator  214  uses the neural network, for which the weighting coefficient is determined by the coefficient determiner  215 , to calculate the preference feature amount that is the feature amount of the preference of the user from the weather record information, the environment history information, and the operation history information. Meanwhile, the schedule identifier  15425  of the air conditioner  15004  identifies, from the plurality of types of schedule information stored in the schedule storage  15435 , the schedule information corresponding to the preference feature amount calculated by the cloud server  15002 ; the device setting updater  419  updates the device setting information stored in the device setting storage  431  in accordance with the operation schedule expressed by the schedule information identified by the schedule identifier  15425 ; and the device controller  414  controls the air conditioner  15004  on the basis of the device setting parameter expressed by the device setting information stored in the device setting storage  431 . As a result, the air conditioner  15004  can be controlled as a result of the air conditioner  15004  merely sending the history information to the cloud server  2  and acquiring the preference feature amount information from the cloud server  2  every period corresponding to the operation schedule expressed by the schedule information. Therefore, it is sufficient that only the preference feature amount information is sent from the cloud server  15002  to the air conditioner  15004 , which leads to the benefit of a reduction of the effects, on the operations of the air conditioner  15004 , of the communication traffic on the external network NT 1 . 
     Note that, in the present embodiment, an example is described in which the schedule information is sent from the cloud server  15002  to the air conditioner  15004 ,  52 , but the present embodiment is not limited thereto, and a configuration is possible in which the preference feature amount information expressing the preference feature amount is sent to the air conditioner  15004 ,  52 . For example, a configuration is possible in which, when the air conditioner  15004 ,  52  includes the measuring device  461 , the air conditioner  15004 ,  52  is controlled using the preference feature amount information and the environment history information obtained by the measuring device  461 . In such a case, since the amount of information of the preference feature amount information is less than that of the schedule information, the communication traffic can be reduced a corresponding amount. 
     Embodiment 3 
     With a control system according to the present embodiment, a device uses a neural network to calculate a future device setting parameter of the device from an environment parameter of a location at which the device is installed and a future weather condition expressed by weather prediction information. Here, the neural network has a predetermined number of nodes and a predetermined number of layers, and is for calculating the future device setting parameter of the device. A server includes a history information acquirer that acquires, from the device, history information including operation history information expressing a history of the device setting parameter, environment history information expressing a history of an environment in which the device operates, and user information expressing a user of the device; and a weather information acquirer that acquires, from a weather server, weather information including weather record information expressing a past weather condition. Additionally, the server includes a coefficient determiner that determines a weighting coefficient of the neural network on the basis of the history information and the weather record information that are acquired. The device includes a neural network calculator that uses the neural network for which neural network coefficient is determined to calculate the future device setting parameter of the device from the weather prediction information and an environment parameter, included in the environment history information, indicating an environment at present. 
     As with the control system described using  FIG. 1  in Embodiment 1, the control system according to the present embodiment includes an air conditioner and a water heater installed in a house H, and a cloud server that is capable of communicating with the air conditioner and the water heater via an external network NT 1 . Note that, in the present embodiment, constituents that are the same as in Embodiment 1 are denoted with the same reference numerals used in Embodiment 1. Additionally, only the air conditioner is described in the present embodiment. The water heater executes the same processing as the air conditioner. Additionally, it is assumed that an internal network NT 2  is laid and a router and a data line terminal device that are connected to the internal network NT 2  are installed in the house H. 
     As illustrated in  FIG. 28 , an air conditioner  2004  according to the present embodiment includes a controller  2400 , a measuring device  461 , and an imaging device  481 . Additionally, the air conditioner  2004  includes a compressor (not illustrated in the drawings) and a blowing fan (not illustrated in the drawings) that operate on the basis of command signals input from the controller  2400 . The controller  2400  includes a CPU  401 , a main storage  402 , an auxiliary storage  403 , a communication interface  405 , a measuring device interface  406 , a wireless module  407 , an imaging interface  408 , a neuro engine  404 , and a bus  409  that connects these components to each other. Note that, in  FIG. 28 , the constituents that are the same as in Embodiment 1 are denoted with the same reference numerals as used in  FIG. 2 . The neuro engine  404  is hardware dedicated to calculation processing using a neural network that has a predetermined number of nodes and a predetermined number of layers. The neuro engine  404  has the same functions as the neural network calculator  214  described in Embodiment 1. As illustrated in  FIG. 29 , the neuro engine  404  includes a processor  441 , a work memory  442 , a calculation accelerator  443 , an input/output register  444 , and a download buffer  445 . Here, hereinafter described coefficient attribute information and coefficient information are acquired from a cloud server  2002 . Note that, in one example, the coefficient attribute information has a JSON schema file format, and the coefficient information has a JSON file format. Here, the coefficient attribute information is temporarily stored in the download buffer  445 , and then is stored in the work memory  442  used by the processor  441 . The processor  441  reads out coefficient attribute information DAZ 2  of the work memory  442  and, on the basis of information expressing the number of layers and the number of nodes of the neural network and information expressing the structure of the neural network included in the coefficient attribute information DAZ 2 , secures memory regions needed to store weighting coefficient information DAC 2 , node calculation value information DAN 21 , and input/output node value information DAN 22 . Then, the processor  441  associates the weighting coefficient and the nodes of the neural network in each of the memory regions. 
     Additionally, the processor  441  stores the weighting coefficient information DAC 2  in the corresponding portion of the work memory  442 . The processor  441  stores input value information to the neural network, which is inputted from the input/output register  444 , in the memory region for storing the input/output node value information DAN 22  and, then, sequentially reads out the weighting coefficient information DAC 2 . The processor  441  sets, in the calculation program, activation function information included in the coefficient attribute information DAZ 2  stored in the work memory  442  and, then, executes sequential calculations for each layer and each node of the neural network. Then, when the calculations for each layer and each node of the neural network are complete, the processor  441  stores the resulting output value information in the memory region storing the input/output node value information DAN 22  and, thereafter, transfers the output value information from the memory region storing the input/output node value information DAN 22  to an output portion of the input/output register  444 . Note that, in the calculation processing executed by the processor  441 , the work memory  442  must have large capacity and, also, there are frequent transfers of numerical information between the processor  441  and the work memory  442 . Accordingly, a certain amount of time is needed to carry out the neural network calculations using the processor  441 . As such, in some cases, a graphical processing unit (GPU) capable of high-speed calculation is used as the processor  441  to shorten the calculation time of the neural network. 
     A calculation accelerator  443  is a dedicated accelerator configured from hardware, and is specialized in processing specific to the calculations of a neural network that executes the enormous number of simple calculations required for every node of the neural network. The calculation accelerator  443  includes a plurality of node unit calculators  443   a . The various node unit calculators  443   a  are provided for every node (for example node X 1 , Y 1 ) of the neural network. Each node unit calculator  443   a  includes a local register  443   b , a product sum calculator  443   c , and a conversion table section  443   d . The number of the node unit calculators  443   a  that is provided is the same as the number of the nodes of the neural network. Additionally, considering that the number of registers differs depending on the scale of the neural network, the local register  443   b  corresponding to the conversion table section  443   d  and the product sum calculator  443   c  has a structure capable of selecting the needed number of local registers. Moreover, the calculation accelerator  443  selects the required number of local registers  443   b  on the basis of the information expressing the number of layers and the number of nodes of the neural network included in the coefficient attribute information acquired from the cloud server  2002 . 
     After the calculation accelerator  443  selects the needed number of local registers  443   b , the weighting coefficient information is stored in each local register  443   b  and the calculations of each node of the neural network are executed. Additionally, the conversion table section  443   d  is for carrying out the calculations of the activation function described above, and the content of the conversion table section  443   d  is set on the basis of information expressing the shape of the activation function included in the coefficient attribute information. Moreover, as described later, the coefficient attribute information includes structure information expressing the structure of the neural network. The node unit calculators  443  a reference information related to the structure of the neural network included in the coefficient attribute information to determine the position of the local register  443   b  in which the weighting coefficient of the neural network is stored and a connection relationship between the node unit calculators  443   a , and acquire the coefficient information. Due to being provided with such a hardware configuration, the calculation accelerator  443  can carry out calculations individually for every node of the neural network, or can carry out calculations at once for a plurality of nodes. This calculation accelerator  443  is capable of processing at higher speeds than when performing calculations using the work memory  442  and the processor  441 . Additionally, the calculation accelerator  443  reads out results of the calculations obtained using the neural network from the local register  443   b  of the node unit calculator  443   a  corresponding to an output node, and outputs the results to the output portion of the input/output register  444 . 
     Note that the calculation accelerator  443  cannot change the circuit scale of the hardware, regardless of the scale of the calculations. As such, the neuro engine  404  according to the present embodiment has a configuration that combines the calculation accelerator  443 , the processor  441 , and the work memory  442 . 
     Returning to  FIG. 28 , the CPU  401  reads out the program stored in the auxiliary storage  403  to the main storage  402  and executes the program to function as an environment information acquirer  411 , an image acquirer  412 , an operation receiver  413 , a device controller  414 , a time keeper  415 , a history information generator  416 , a history information sender  417 , a device setting updater  2419 , an operation mode setter  420 , a user identifier  421 , a weather information acquirer  2422 , a coefficient acquirer  2423 , and a coefficient setter  2424 , as illustrated in  FIG. 30 . Additionally, as illustrated in  FIG. 30 , the auxiliary storage  403  illustrated in  FIG. 28  includes a device setting storage  431 , a user information storage  432 , an operation mode storage  433 , a history information storage  434 , a neural network storage  2436 , and a weather information storage  2437 . The neural network storage  2436  stores neural network structure information expressing the structure of the neural network and weighting coefficient information expressing the weighting coefficient of the neural network that the neuro engine  404  uses. The structure information of the neural network includes information expressing a shape of an activation function at each node, number of layers information, information about the number of nodes in each layer, and the like. The weather information storage  2437  stores the weather prediction information acquired from the cloud server  2002 . 
     The weather information acquirer  2422  is a second weather information acquirer that acquires, from the weather server  3 , weather information including weather prediction information expressing a future weather condition. Here, the weather information acquirer  2422  acquires the weather information from the weather server  3  by sending weather information request information requesting, to the weather server  3 , sending of the weather information. The coefficient acquirer  2423  acquires, from the cloud server  2002 , the coefficient information including the information expressing the weighting coefficient of the neural network realized in the neuro engine  404 . Here, the coefficient acquirer  2423  acquires the coefficient information from the cloud server  2002  by sending coefficient request information requesting, to the cloud server  2002 , sending of the coefficient information. Additionally, the coefficient acquirer  2423  executes information expansion processing on the coefficient information and the coefficient attribute information that are acquired from the cloud server  2002  and that have been subjected to reversible information compression processing. Then, the coefficient acquirer  2423  stores the weighting coefficient information included in the coefficient information in the neural network storage  2436 . 
     The coefficient setter  2424  sets the weighting coefficient of the neural network. The neuro engine  404  uses the neural network, in which the weighting coefficient is set by the coefficient setter  2424 , to calculate the future device setting parameter of the air conditioner  2004  from the weather prediction information and the environment parameter indicating the environment at present included in the environment history information. Here, the environment parameter expressing the environment at present is a parameter expressing the indoor temperature acquired from the air conditioners  2004 ,  52  or the temperature of the hot water acquired from the water heater  51 . In some cases, due to the measuring frequencies of the measuring device  461  of the air conditioners  2004 ,  52  and the measuring device  561  of the water heater  51  and an acquisition frequency of the environment parameter of the history information acquirer  211 , the environment parameter expressing the current environment is a parameter expressing an environment a few seconds to a few minutes before the present time. Additionally, the neuro engine  404  uses the neural network to calculate the device setting parameter from the environment parameter such as the indoor temperature at present, the hot water temperature at present, or the like expressed by the environment history information included in the history information, the numerical value indicating the date and time at present, and the information obtained by quantifying the future weather condition expressed by the weather prediction information. 
     The device setting updater  2419  references the operation mode information stored in the operation mode storage  433  and, when the operation mode is set to the automatic mode, uses the device setting information calculated by the neuro engine  404  to update the device setting information stored in the device setting storage  431 . Here, the period in which the device setting updater  2419  updates the device setting information can be set to a time that arrives at predetermined regular time intervals. For example, the period can be set to a time that arrives at time intervals of five minutes. 
     The hardware configuration of the cloud server  2002  is the same as the hardware configuration of the cloud server  2  of Embodiment 1 illustrated in  FIG. 10 . With the cloud server  2002 , the CPU  401  reads out the program stored in the auxiliary storage  403  to the main storage  402  and executes the program to function as a history information acquirer  211 , a weather information acquirer  212 , a coefficient setter  213 , a neural network calculator  214 , a coefficient determiner  215 , a coefficient information generator  2218 , and a coefficient sender  2219 , as illustrated in  FIG. 31 . Note that, in  FIG. 31 , the constituents that are the same as in Embodiment 1 are denoted with the same reference numerals as used in  FIG. 10 . Additionally, as illustrated in  FIG. 31 , the auxiliary storage  203  illustrated in  FIG. 10  includes a history information storage  231 , a weather information storage  232  that stores the weather record information acquired from the weather server  3 , and a neural network storage  233 . 
     The weather information acquirer  212  is a first weather information acquirer that acquires, from the weather server  3 , the weather record information expressing the past weather condition. Here, the weather information acquirer  212  acquires the weather record information from the weather server  3  by sending weather record request information requesting, to the weather server  3 , sending of the weather record information. As in Embodiment 1, the coefficient determiner  215  determines the weighting coefficient of the neural network on the basis of the history information and the weather record information. The coefficient information generator  2218  generates coefficient information that includes information expressing the weighting coefficient determined by the coefficient determiner  215 . In one example, the coefficient information generator  2218  generates coefficient attribute information for which the file format is JSON schema, and generates coefficient information for which the file format is JSON. The coefficient sender  2219  sends the coefficient information generated by the coefficient information generator  2218  to the air conditioner  2004 . Here, the coefficient sender  2219  performs reversible information compression processing on the coefficient information and the coefficient attribute information and then sends the processed information. As a result, the amount of information sent from the cloud server  2002  to the air conditioner  2004  can be reduced. 
     Next, the operations of the control system according to the present embodiment are described while referencing  FIGS. 32 and 33 . Firstly, when a history information generation period arrives, the air conditioner  2004  generates the history information using the operation history information, the environment history information, the date and time information, and the user information stored in the history information storage  434  (step S 21 ). The structure of the history information is the same as the structure of the history information described using  FIG. 12  in Embodiment 1. Then, the generated history information is sent from the air conditioner  2004  to the cloud server  2002  (step S 22 ). When the cloud server  2002  receives the history information, the cloud server  2002  stores, in the history information storage  231 , the operation history information, the environment history information, the date and time information, and the user information included in the history information. 
     Next, weather record request information requesting, to the weather server  3 , sending of the weather record information is sent from the cloud server  2002  to the weather server  3  (step S 23 ). Meanwhile, when the weather server  3  receives the weather record request information, the weather server  3  generates weather record information of the region in which the house H exists (step S 24 ). Next, the generated weather record information is sent from the weather server  3  to the cloud server  2002  (step S 25 ). Meanwhile, when the cloud server  2002  receives the weather record information, the cloud server  2002  stores the received weather record information in the weather information storage  232 . Then, the cloud server  2002  determines the weighting coefficient of the neural network on the basis of the operation history information, the environment history information, the date and time information, the user information, and the weather record information that are acquired (step S 26 ). The cloud server  2002  stores information expressing the determined weighting coefficient in the neural network storage  233 . 
     Next, it is assumed that the air conditioner  2004  receives a switching operation performed by the user for switching to the automatic mode (step S 27 ). In this case, the operation mode is set to the automatic mode by the air conditioner  2004  storing, in the operation mode storage  433 , operation mode information expressing that the operation mode is the automatic mode (step S 28 ). Next, when the air conditioner  2004  determines that a predetermined update period of the weighting coefficient of the neural network realized by the neuro engine  2104  has arrived, coefficient request information requesting, to the cloud server  2002 , sending of the coefficient information is sent from the air conditioner  2004  to the cloud server  2002  (step S 29 ). Meanwhile, when the cloud server  2002  receives the coefficient request information, the cloud server  2002  generates coefficient information including the information expressing the weighting coefficient stored in the neural network storage  233  (step S 30 ). 
     The coefficient information includes protocol information, coefficient information identification information that identifies the generated coefficient information, and the weighting coefficient information. The protocol information includes a variety of information related to a communication protocol used when sending the coefficient information to the air conditioner  2004 . As illustrated in  FIG. 33 , for example, the coefficient attribute information includes the protocol information and a variety of attribute information. Examples of the attribute information include coefficient attribute information identification information that identifies the generated coefficient attribute information, device identification information that identifies the air conditioner  4 ,  52  or the water heater  51  that is the target for which the device setting parameter is to be calculated using the neural network, the user identification information described above, format information, neural network structure information, calculation information, training method information, training period information, coefficient update period information, realized function information, and device use environment information. In one example, the coefficient information identification information includes at least one of identification information imparted to the attribute information, identification information imparted to the weighting coefficient information, and identification information of the air conditioner  4 ,  52  or the water heater  51 . The format information includes information expressing a data format or a file format and information expressing a compression format of each of the attribute information and the weighting coefficient information. In one example, the format information includes information expressing that the file format of the attribute information is JSON schema and information expressing that the file format of the weighting coefficient information is JSON. The neural network structure information includes information expressing the number of layers and the number of nodes of each layer of the neural network, information expressing the degree of the matrix used in the calculations using the neural network, and information expressing the shape of the activation function at each node of the neural network. Additionally, the neural network structure information includes normalization processing or drop-out information of the calculations using the neural network, and information about the node connected to an input side and the node connected to an output side of each node of the neural network. Here, the “drop-out information” is information expressing whether any of the nodes of the neural network are deactivated when determining the weighting coefficient of the neural network. The calculation information includes information expressing processing methods for when performing calculations using the neural network, such as multi-thread processing, pipeline processing, and the like. The training method information includes information expressing a training method such as backpropagation using an autoencoder. The training period information includes information expressing a present or past period at which the operation history information, the environment history information, and the weather record information, which are used when determining the coefficient of the neural network, are acquired. The coefficient update period information includes information expressing a period in which the weighting coefficient of the neural network is updated. The realized function information is information expressing a function of the air conditioner  4 ,  52  or the water heater  51  that is to be controlled by the device setting parameter calculated using the neural network. Additionally, the realized function information includes information expressing operation content performed on the operation device  6 ,  71 ,  72  when the user manually changes the device setting parameter calculated using the neural network. The device use environment information includes information expressing the arrangement of each of the air conditioners  4 ,  52  and the water heater  51  in the house H, and information expressing the composition of the household residing in the house H. 
     Returning to  FIG. 32 , next, the generated coefficient information is sent from the cloud server  2002  to the air conditioner  2004  (step S 31 ). Meanwhile, when the air conditioner  2004  receives the coefficient information, the air conditioner  2004  stores the received coefficient information in the neural network storage  2436 . Then, the air conditioner  2004  acquires the weighting coefficient stored in the neural network storage  2436 , and sets the acquired weighting coefficient in the neuro engine  404 . Thereafter, it is assumed that the air conditioner  2004  determines that the update period of the device setting information has arrived. In this case, weather information request information requesting, to the weather server  3 , sending of the weather information including the weather prediction information and the weather record information is sent from the air conditioner  2004  to the weather server  3  (step S 32 ). Meanwhile, when the weather server  3  receives the weather information request information, the weather server  3  identifies the weather prediction information of the region in which the house H exists, and generates weather information including the identified weather prediction information (step S 33 ). Next, the generated weather information is sent from the weather server  3  to the air conditioner  2004  (step S 34 ). 
     Next, the air conditioner  2004  uses the neural network in which the weighting coefficient is set to calculate the future device setting parameter of the air conditioner  2004  from the future weather condition expressed by the weather prediction information and the environment parameter indicating the environment at present included in the environment history information (step S 35 ). Then, the air conditioner  2004  uses the calculated device setting parameter to update the device setting information stored in the device setting storage  431  (step S 36 ). Thereafter, the series of processing from step S 32  to step S 36  is repeatedly executed every time the update period of the device setting information arrives. 
     Next, device control processing executed by the air conditioner  2004  according to the present embodiment is described while referencing  FIG. 34 . In one example, this device control processing starts when the power to the air conditioner  2004  is turned ON. 
     Firstly, the series of processing from step S 2101  to step S 2106  is executed. Here, the series of processing from step S 2101  to step S 2106  is the same as the series of processing from step S 101  to step S 106  described using  FIG. 15  in Embodiment 1. Next, the coefficient acquirer  2423  references the operation mode information stored in the operation mode storage  433  to determine whether the operation mode of the air conditioner  2004  is the automatic mode (step S 2107 ). When the coefficient acquirer  2423  determines that the operation mode of the air conditioner  2004  is the manual mode (step S 2107 ; No), the processing of step S 2101  is executed again. However, when the coefficient acquirer  2423  determines that the operation mode of the air conditioner  2004  is the automatic mode (step S 2107 ; Yes), the coefficient acquirer  2423  determines whether a coefficient update period of the neural network has arrived (step S 2108 ). When the coefficient acquirer  2423  determines that the coefficient update period has not arrived (step S 2108 ; No), the processing of hereinafter described step S 2111  is executed without modification. However, it is assumed that the coefficient acquirer  2423  determines that the coefficient update period has arrived (step S 2108 ; Yes). In this case, the coefficient acquirer  2423  sends the coefficient request information to the cloud server  2002  (step S 2109 ) to acquire the coefficient information from the cloud server  2002  (step S 2110 ). The coefficient acquirer  2423  stores the acquired coefficient information in the neural network storage  2436 . 
     Next, the device setting updater  2419  determines whether a predetermined update period of the device setting information of the air conditioner  2004  has arrived (step S 2111 ). When the device setting updater  2419  determines that the update period of the device setting information of the air conditioner  2004  has not arrived (step S 2111 ; No), the processing of step S 2101  is executed again. However, it is assumed that the device setting updater  2419  determines that a update period of the device setting information of the air conditioner  2004  has arrived (step S 2111 ; Yes). In this case, the weather information acquirer  2422  sends, to the weather server  3 , the weather information request information (step S 2112 ) to acquire the weather information from the weather server  3  (step S 2113 ). Here, the weather information acquirer  2422  stores, in the weather information storage  2437 , the weather prediction information included in the acquired weather information. 
     Thereafter, the neuro engine  404  uses the neural network, in which the weighting coefficient is set by the coefficient setter  2424 , to calculate, on the basis of the weather prediction information and the environment parameter at present included in the environment history information, the device setting parameter of the air conditioner  2004  (step S 2114 ). Next, the device setting updater  2419  uses the calculated device setting parameter to update the device setting information stored in the device setting storage  431  (step S 2115 ). Then, the processing of step S 2101  is executed again. 
     Next, coefficient information generation processing executed by the cloud server  2002  according to the present embodiment is described while referencing  FIG. 35 . In one example, this coefficient information generation processing starts when the power to the cloud server  2002  is turned ON. 
     Firstly, the processing of steps S 2201  and S 2202  is executed. The content of the processing of steps S 2201  and S 2202  is the same as that of the processing of steps S 201  and S 202  described using  FIG. 16  in Embodiment 1. Next, a weather record acquirer  2212  sends weather record request information requesting, to the weather server  3 , sending of the weather record information (step S 2203 ) to acquire the weather record information from the weather server  3  (step S 2204 ). Here, the weather record acquirer  2212  stores the acquired weather record information in the weather information storage  232 . Next, coefficient determination processing is executed for determining the coefficient of the neural network described above on the basis of the operation history information and the environment history information included in the history information, and the weather record information (step S 2205 ). The content of the coefficient determination processing is the same as that of the coefficient determination processing described using  FIG. 17  in Embodiment 1. However, in step S 303  of  FIG. 17 , the neural network calculator  214  uses the neural network in which the initial weighting coefficient is set to calculate, from the environment parameter included in the acquired environment history information and the information obtained by quantifying the weather condition expressed by the weather record information, the device setting parameter for every date and time expressed by the date and time information. Then, in step S 304 , the coefficient determiner  215  calculates, for every date and time expressed by the date and time information, the error between the calculated device setting parameter and the device setting parameter included in the operation history information. 
     Then, the coefficient information generator  2218  determines whether the coefficient request information is acquired from the air conditioner  2004  (step S 2206 ). When the coefficient information generator  2218  determines that the coefficient request information is not acquired (step S 2206 ; No), the processing of step S 2201  is executed again. Meanwhile, when the coefficient information generator  2218  determines that the coefficient request information is acquired (step S 2206 ; Yes), the coefficient information generator  2218  generates coefficient information including the weighting coefficient information stored in the neural network storage  233  (step S 2207 ). Thereafter, the coefficient sender  2219  sends the generated coefficient information to the air conditioner  2004  (step S 2208 ). Then, the processing of step S 2201  is executed again. 
     As described above, with the control system according to the present embodiment, in the cloud server  2002 , the coefficient determiner  215  determines the weighting coefficient of the neural network and sends the coefficient information that includes the information expressing the determined weighting coefficient to the air conditioner  2004 . Additionally, in the air conditioner  2004 , the neuro engine  404  uses the neural network, in which the weighting coefficient expressed by the coefficient information received from the cloud server  2002  is set, to calculate the future device setting parameter of the air conditioner  2004  from the weather prediction information and the environment parameter at present included in the environment history information. Moreover, the device controller  414  controls the air conditioner  2004  on the basis of the device setting parameter calculated by the neuro engine  404 . As a result, the air conditioner  2004  can be controlled as a result of the air conditioner  2004  merely sending the history information to the cloud server  2002  and acquiring the coefficient information from the cloud server  2002  every time the coefficient information update period arrives, and acquiring the weather information from the cloud server  2002  every time the device setting information update period arrives. Therefore, the frequency at which the history information, the coefficient information, and the weather information are exchanged between the air conditioner  2004  and the cloud server  2002  is reduced, which leads to the benefit of a reduction of the effects, on the operations of the air conditioner  2004 , of the communication traffic on the external network NT 1 . Additionally, when the neural network needs to be re-trained, the air conditioner  2004  can re-send the history information to the cloud server  2002  and acquire information expressing a weighting coefficient of the revised neural network. 
     Note that the amount of information related to a neural network is much greater than the amount of information of a typical so-called IoT home appliance. For example, the amount of communication in a home appliance can be reduced by installing the neural network itself in that home appliance, However, in such a case, although measurement information of a sensor of the home appliance or operation information of the home appliance can be processed in real time, the content that can be processed and/or the training functions that can be realized in the home appliance are limited due to the measuring resources of the CPU and/or the memory of the home appliance. In particular, due to the capacity of the memory of home appliances, it is difficult to retain the huge amount of information related to the neural network, such as the past history information of the home appliance. Additionally, it is difficult to manage all home appliances, from multi-function high-spec home appliances that have sufficient CPU resources and/or memory to single-function low-cost home appliances that are only provided with a comparatively low-performance CPU, on the same platform. As such, as a control system that includes a cloud server and devices, there is a need for the realization of a control system that, even when each device is provided with different CPU resources, can uniformly use the training function of the neural network for each device and that is less likely to be affected by communication traffic. Additionally, in a control system using these neural networks, a case is anticipated in which a neural network trained for a single user is used across home appliances of different manufacturers or different models, and/or platforms of different manufacturers. Moreover, in order to use this neural network that is trained for a single user across home appliances of different manufacturers or different models, and/or platforms of different manufacturers, there is also a need to unify the information related to the neural networks in a standard data format. 
     To answer these needs, with the control system according to the present embodiment, as described above, the coefficient information and the coefficient attribute information have predetermined structures. As a result, a benefit is realized in that it is easier to use the coefficient information and the coefficient attribute information across the platforms of different manufacturers. 
     According to the present embodiment, the air conditioner  2004  sends the history information related to the air conditioner  2004  to the cloud server  2002 , and the cloud server  2002  determines the weighting coefficient of the neural network on the basis of the received history information. As a result, even though the air conditioner  2004  does not include a coefficient determiner, the air conditioner  2004  can acquire, from the cloud server  2002 , the weighting coefficient of the neural network that is determined on the basis of the history information related to the air conditioner  2004 . Accordingly, when, for example, implementing a new air conditioner  2004  due to a malfunction or the end of life of an existing air conditioner  2004 , the weighting coefficient of the neural network determined on the basis of the history information related to the air conditioner  2004  used to-date can be inherited and applied. Accordingly, the operation tendencies when automatically operating the air conditioner  2004  are maintained and, as such, the environment in which the air conditioner  2004  is installed is maintained, which is a benefit. 
     Furthermore, as described above, the coefficient attribute information according to the present embodiment includes the coefficient information identification information, the device identification information, the user identification information, the format information, the neural network structure information, the calculation information, the training method information, the training period information, the coefficient update period information, the realized function information, and the device use environment information. As such, the coefficient information is easier to distribute to the market, for example, or to apply to air conditioners, water heaters, and the like of different manufacturers, which is a benefit. 
     Embodiment 4 
     With a control system according to the present embodiment, a device includes a schedule storage that associates a plurality of types of schedule information expressing an operation schedule of the device with preference feature amount information that is information obtained by quantifying a preference of a user of the device, and stores the associated information. The device uses a second neural network to identify the schedule information expressing the operation schedule of the device from environment history information of a location at which the device is installed, and weather record information expressing a past weather condition. Here, the second neural network has a predetermined number of nodes and a predetermined number of layers and is for calculating a preference feature amount indicating a feature of the preference of the user. A server includes a history information acquirer that acquires, from the device, history information including operation history information expressing a history of the device setting parameter, environment history information expressing a history of an environment in which the device operates, and user information expressing the user of the device; and a weather information acquirer that acquires, from a weather server, weather information including the weather record information expressing the past weather condition, and weather prediction information expressing a future weather condition. Additionally, the server includes a coefficient determiner that determines a weighting coefficient of the second neural network on the basis of the history information and the weather record information that are acquired. The device includes a neural network calculator that uses the second neural network for which the weighting coefficient is determined to calculate the feature amount of the preference of the user from the operation history information, the environment history information, and the weather record information. 
     As with the control system described using  FIG. 1  in Embodiment 1, the control system according to the present embodiment includes an air conditioner and a water heater installed in a house H, and a cloud server that is capable of communicating with the air conditioner and the water heater via an external network NT 1 . Note that, in the present embodiment, constituents that are the same as in Embodiment 1 are denoted with the same reference numerals used in Embodiment 1. Additionally, only the air conditioner is described in the present embodiment. The water heater executes the same processing as the air conditioner. Additionally, it is assumed that an internal network NT 2  is laid and a router and a data line terminal device that are connected to the internal network NT 2  are installed in the house H. 
     The hardware configuration of an air conditioner  16004  according to the present embodiment is the same as the hardware configuration of the air conditioner  2004  described using  FIG. 28  in Embodiment 3. As illustrated in  FIG. 36 , the air conditioner  16004  includes a controller  16400 , a measuring device  461 , and an imaging device  481 . Note that, in  FIG. 36 , the constituents that are the same as in Embodiment 3 are denoted with the same reference numerals as used in  FIG. 30 . 
     As illustrated in  FIG. 36 , in the controller  16400 , the CPU reads out a program stored in an auxiliary storage to a main storage and executes the program to function as an environment information acquirer  411 , an image acquirer  412 , an operation receiver  413 , a device controller  414 , a time keeper  415 , a history information generator  416 , a history information sender  417 , a device setting updater  2419 , an operation mode setter  420 , a user identifier  421 , a weather information acquirer  2422 , a coefficient acquirer  16423 , and a coefficient setter  16424 . Additionally, the auxiliary storage includes a device setting storage  431 , a user information storage  432 , an operation mode storage  433 , a history information storage  434 , a neural network storage  16436 , a weather information storage  2437 , and a schedule storage  16435 . Note that the CPU, the main storage, and the auxiliary storage are the same as the CPU  401 , the main storage  402 , and the auxiliary storage  403  illustrated in  FIG. 28 . The neural network storage  16436  stores the second neural network that is for calculating the preference feature amount that is the feature amount of the preference of the user of the air conditioner  16004 . The neural network storage  16436  stores neural network structure information expressing the structure of the neural network and weighting coefficient information expressing the weighting coefficient of the neural network that the neuro engine  404  uses. As described using  FIG. 22  in Embodiment 2, the schedule storage  16435  associates a plurality of types of schedule information with the preference feature amount, and stores the associated information. 
     The coefficient acquirer  16423  acquires, from the cloud server  16002  via the external network NT 1 , coefficient information including information expressing the weighting coefficient of the neural network realized in the neuro engine  404 . Here, the coefficient acquirer  16423  acquires the coefficient information from the cloud server  16002  by sending coefficient request information requesting, to the cloud server  16002 , sending of the coefficient information. The coefficient setter  16424  sets the weighting coefficient of the neural network. Then, the neuro engine  404  uses the neural network, in which the weighting coefficient is set by the coefficient setter  16424 , to calculate the preference feature amount from the weather prediction information, the operation history information, and the environment history information. Here, the neuro engine  404  uses the neural network to calculate the preference feature amount from the operation history information and the environment history information included in the history information, and information obtained by quantifying a future weather condition expressed by the weather prediction information. 
     The schedule identifier  16425  identifies, from the plurality of types of schedule information stored in the schedule storage  16435 , the schedule information corresponding to the preference feature amount that is calculated by the neuro engine  404 . The device setting updater  16419  references the operation mode information stored in the operation mode storage  433  and, when the operation mode is set to the automatic mode, updates device setting information stored in the device setting storage  431  on the basis of the schedule information identified by the schedule identifier  16425 . 
     The hardware configuration of the cloud server  16002  is the same as the hardware configuration of the cloud server  2  of Embodiment 1 illustrated in  FIG. 10 . With the cloud server  16002 , the CPU  201  illustrated in  FIG. 10  reads out a program stored in the auxiliary storage  203  to the main storage  202  and executes the program to function as a coefficient setter  15213 , a neural network calculator  214 , a coefficient determiner  16215 , and a coefficient sender  16219 , as illustrated in  FIG. 37 . Note that, in  FIG. 37 , the constituents that are the same as in Embodiment 1 are denoted with the same reference numerals as used in  FIG. 10 . Additionally, as illustrated in  FIG. 37 , the auxiliary storage  203  illustrated in  FIG. 10  includes a neural network storage  16233 , a schedule storage  16234 , and a teacher information storage  15235 . As with the schedule storage  16435  described above, the schedule storage  16234  associates a plurality of types of schedule information with the preference feature amount, and stores the associated information. As in Embodiment 2, the teacher information storage  15235  stores teacher information that is used by the coefficient determiner  16215  to determine the neural network coefficient. 
     The coefficient determiner  16215  determines the weighting coefficient of the neural network on the basis of the history information and the weather record information. The coefficient information generator  16218  generates coefficient information that includes information expressing the weighting coefficient determined by the coefficient determiner  16215 . The coefficient sender  16219  sends the coefficient information generated by the coefficient information generator  16218  to the air conditioner  16004 . Here, the coefficient sender  16219  performs reversible information compression processing on the coefficient information and then distributes the processed information. As a result, the amount of information sent from the cloud server  16002  to the air conditioner  16004  can be reduced. 
     Next, the operations of the control system according to the present embodiment are described while referencing  FIG. 38 . Note that, in  FIG. 38 , the processes that are the same as in Embodiment 3 are denoted with the same reference numerals as used in  FIG. 32 . Firstly, the cloud server  16002  determines the weighting coefficient of the neural network on the basis of the operation history information, the environment history information, and the preference feature amount information that are acquired from the teacher information storage  15235  (step S 16021 ). 
     Next, when the air conditioner  16004  determines that a predetermined update period of the weighting coefficient of the neural network realized by the neuro engine  404  has arrived, coefficient request information requesting, to the cloud server  16002 , sending of the coefficient information is sent from the air conditioner  16004  to the cloud server  2  (step S 16022 ). Meanwhile, when the cloud server  16002  receives the coefficient request information, the cloud server  16002  generates coefficient information including information expressing the weighting coefficient stored in the neural network storage  16233 , and coefficient attribute information (step S 16023 ). The respective structures of the coefficient information and the coefficient attribute information are the same as the structures described in Embodiment 3. 
     Next, the coefficient information and the coefficient attribute information that are generated are sent from the cloud server  16002  to the air conditioner  16004  (step S 16024 ). Meanwhile, when the air conditioner  16004  receives the coefficient information and the coefficient attribute information, the air conditioner  16004  stores the received coefficient information and coefficient attribute information in the neural network storage  16436 . Then, the air conditioner  16004  acquires the weighting coefficient information stored in the neural network storage  16436 , and sets the weighting coefficient expressed by the acquired weighting coefficient information in the neuro engine  404 . 
     Thereafter, it is assumed that the air conditioner  16004  receives a switching operation performed by the user for switching to the automatic mode (step S 16025 ). In this case, the air conditioner  16004  sets the operation mode to the automatic mode (step S 16026 ). Next, it is assumed that the air conditioner  16004  determines that an update period of the schedule information has arrived. In this case, weather record request information requesting, to the weather server  3 , sending of weather record information is sent from the air conditioner  16004  to the weather server  3  (step S 16027 ). Meanwhile, when the weather server  3  receives the weather record request information, the weather server  3  generates weather record information of the region in which the house H exists (step S 16028 ). Next, the generated weather information is sent from the weather server  3  to the air conditioner  16004  (step S 16029 ). 
     Then, the air conditioner  16004  uses the neural network in which the weighting coefficient is set to calculate the preference feature amount from the future weather condition expressed by the weather prediction information, the operation history information, and the environment history information. Moreover, the air conditioner  16004  identifies, from the plurality of types of schedule information stored in the schedule storage  16435 , the schedule information corresponding to the calculated preference feature amount (step S 16030 ). Then, the air conditioner  16004  updates, on the basis of the identified schedule information, the device setting information stored in the device setting storage  431  (step S 16031 ). Thereafter, the processing of the aforementioned step S 16031  is repeatedly executed every time the update period of the device setting information arrives. 
     Next, device control processing executed by the air conditioner  16004  according to the present embodiment is described while referencing  FIG. 39 . In one example, this device control processing starts when the power to the air conditioner  16004  is turned ON. 
     Firstly, the coefficient acquirer  16423  determines whether a coefficient update period of the neural network has arrived (step S 16001 ). When the coefficient acquirer  16423  determines that the coefficient update period has not arrived (step S 16001 ; No), the processing of hereinafter described step S 16004  is executed without modification. However, it is assumed that the coefficient acquirer  16423  determines that the coefficient update period has arrived (step S 16001 ; Yes). In this case, the coefficient acquirer  16423  sends the coefficient request information to the cloud server  16002  (step S 16002 ) to acquire the coefficient information and the coefficient attribute information from the cloud server  16002  (step S 16003 ). The coefficient acquirer  2423  stores the coefficient information and the coefficient attribute information that are acquired in the neural network storage  16436 . 
     Next the processing of steps S 16004  and S 16005  is executed. Here, the processing of steps S 16004  and S 16005  is the same as that of the processing of steps S 105  and S 106  described using  FIG. 15  in Embodiment 1. Then, the schedule identifier  16425  references the operation mode information stored in the operation mode storage  433  to determine whether the operation mode of the air conditioner  16004  is the automatic mode (step S 16006 ). When the schedule identifier  16425  determines that the operation mode of the air conditioner  16004  is the manual mode (step S 16006 ; No), the processing of step S 16001  is executed again. 
     However, it is assumed that the device setting updater  16425  determines that the operation mode of the air conditioner  16004  is the automatic mode (step S 16006 ; Yes). In this case, the schedule identifier  16425  determines whether a predetermined update period of the operation schedule of the air conditioner  16004  has arrived (step S 16007 ). When the schedule identifier  16425  determines that the update period of the operation schedule of the air conditioner  16004  has not arrived (step S 16007 ; No), the processing of hereinafter described step S 16011  is executed. However, it is assumed that the schedule identifier  16425  determines that the update period of the operation schedule of the air conditioner  16004  has arrived (step S 16007 ; Yes). In this case, the weather information acquirer  2422  sends the weather record request information to the weather server  3  (step S 16008 ) to acquire the weather record information from the weather server  3  (step S 16009 ). Here, the weather information acquirer  2422  stores the acquired weather record information in the weather information storage  2437 . 
     Thereafter, the neuro engine  404  calculates, on the basis of the operation history information, the environment history information, and the weather record information, the preference feature amount of the air conditioner  16004  using the neural network in which the weighting coefficient is set by the coefficient setter  16424 . Moreover, the schedule identifier  16425  identifies the schedule information corresponding to the calculated preference feature amount (step S 16010 ). Next, the device setting updater  16419  determines whether a predetermined update period of the device setting information of the air conditioner  16004  has arrived (step S 16011 ). When the device setting updater  16419  determines that the update period of the device setting information has not arrived (step S 16011 ; No), the processing of step S 16101  is executed again. Meanwhile, when the device setting updater  16419  determines that the update period of the device setting information has arrived (step S 16011 ; Yes), the device setting updater  16419  updates, on the basis of the schedule information identified by the schedule identifier  16425 , the device setting information stored in the device setting storage  431  (step S 16012 ). Then, the processing of step S 16101  is executed again. 
     Next, coefficient information generation processing executed by the cloud server  16002  according to the present embodiment is described while referencing  FIG. 40 . After the power to the cloud server  16002  is turned ON, this coefficient information generation processing may, for example, be executed every time the operation history information, the environment history information, the weather record information, and the preference feature amount information stored in the teacher information storage  15235  are updated. 
     Firstly, coefficient determination processing is executed for determining the coefficient of the neural network on the basis of the operation history information, the environment history information, the weather record information, and the preference feature amount information that are acquired from the teacher information storage  15235  (step S 16201 ). The content of the coefficient determination processing is the same as that of the coefficient determination processing described using  FIG. 26  in Embodiment 2. 
     Then, the coefficient information generator  16218  determines whether the coefficient request information is acquired from the air conditioner  16004  (step S 16202 ). When the coefficient information generator  16218  determines that the coefficient request information is not acquired (step S 16202 ; No), the processing of step S 16202  is executed again. Meanwhile, when the coefficient information generator  16218  determines that the coefficient request information is acquired (step S 16202 ; Yes), the coefficient information generator  16218  generates coefficient information including the weighting coefficient information stored in the neural network storage  16233 , and coefficient attribute information (step S 16203 ). Next, the coefficient sender  16219  sends the coefficient information and the coefficient attribute information that are generated to the air conditioner  16004  (step S 16204 ). Then, the processing of step S 16202  is executed again. 
     As described above, with the control system according to the present embodiment, in the cloud server  16002 , the coefficient determiner  16215  determines the weighting coefficient of the neural network and sends the coefficient information that includes the information expressing the determined weighting coefficient to the air conditioner  16004 . In the air conditioner  16004 , the neuro engine  404  uses the neural network, in which the weighting coefficient expressed by the coefficient information received from the cloud server  16002  is set, to calculate the preference feature amount that is the feature amount of the preference of the user of the air conditioner  16004 , from the operation history information, the environment history information, and the weather record information. Then, the schedule identifier  16425  identifies the schedule information corresponding to the preference feature amount calculated by the neuro engine  404 . Moreover, the device controller  414  controls the air conditioner  16004  in accordance with the operation schedule expressed by the schedule information. As a result, the air conditioner  16004  can be controlled as a result of the air conditioner  16004  merely sending the history information to the cloud server  16002  and acquiring the coefficient information from the cloud server  16002  every time the schedule update period arrives, and acquiring the weather information from the cloud server  16002  every time the coefficient information update period arrives. Therefore, the frequency at which the history information and the coefficient information are exchanged between the air conditioner  16004  and the cloud server  16002  is reduced, which leads to the benefit of a reduction of the effects, on the operations of the air conditioner  16004 , of the communication traffic on the external network NT 1 . 
     Embodiment 5 
     With a control system according to the present embodiment, a device determines a weighting coefficient of a neural network, and uses the neural network for which the weighting coefficient is determined to calculate a future device setting parameter of the device. Here, the neural network has a predetermined number of nodes and a predetermined number of layers, and is for calculating the future device setting parameter of the device. A server determines an initial coefficient that is a weighting coefficient initially set in the neural network used by the device. The server includes an initial coefficient determiner that determines the initial coefficient of the weighting coefficient of the neural network, and a coefficient sender that sends, to the device, coefficient information including initial coefficient information expressing the initial coefficient. Additionally, the device includes a coefficient acquirer that acquires the coefficient information; a history information acquirer that acquires operation history information and environment history information of the device; a weather information acquirer that acquires weather information including weather record information expressing a past weather condition and weather prediction information expressing a future weather condition; a coefficient determiner that determines the weather record information of the neural network on the basis of the initial coefficient information, the operation history information, the environment history information, and the weather record information; a neural network calculator that uses the neural network to calculate the future device setting parameter of the device from the future weather condition expressed by the weather prediction information and an environment parameter, included in the environment history information, indicating an environment at present; and a device controller that controls the device on the basis of the calculated device setting parameter. 
     As with the control system described using  FIG. 1  in Embodiment 1, the control system according to the present embodiment includes an air conditioner and a water heater installed in a house H, and a cloud server that is capable of communicating with the air conditioner and the water heater via an external network NT 1 . Note that, in the present embodiment, constituents that are the same as in Embodiments 1 and 3 are denoted with the same reference numerals used in Embodiments 1 and 3. Additionally, it is assumed that an internal network NT 2  is laid and a router and a data line terminal device that are connected to the internal network NT 2  are installed in the house H. Furthermore, a customer server  3003  that manages customers that purchase the air conditioner, for example, is connected to the external network NT 1 . 
     The customer server  3003  includes a storage (not illustrated in the drawings) in which history information and device identification information that identifies the air conditioner are associated and stored. Here, the history information includes a history of device setting information of the air conditioner purchased by a customer and a history of environment information expressing an environment parameter including temperature information. Every time the customer server  3003  periodically receives the history information from the air conditioner purchased by the customer, the customer server  3003  associates the received history information with the device identification information, and stores the associated information in the storage, Additionally, when the customer server  3003  receives history request information from a cloud server  3002 , the customer server  3003  identifies, from the history information stored in the storage, the operation history information and the environment history information that correspond to the history request information. In one example, the customer server  3003  identifies another house in which an air conditioner of the same model as the air conditioner  3004  is installed, and generates the history information including the operation history information and the environment history information of the air conditioner installed in the identified house. Note that a configuration is possible in which, for example, the operation history information and the environment history information included in the history information express a history of an average of the device setting parameter and a history of an average of the environment parameter of a plurality of households in which air conditioners, of the same model as the air conditioner  3004  installed in the house H, are installed. 
     The hardware configuration of the air conditioner  3004  according to the present embodiment is the same as the hardware configuration of the air conditioner  2004  illustrated in  FIG. 28  of Embodiment 3. The controller  3400  includes a CPU (not illustrated), a main storage (not illustrated), an auxiliary storage (not illustrated), a communication interface (not illustrated), a measuring device interface (not illustrated), a wireless module (not illustrated), an imaging interface (not illustrated), a neuro engine  404 , and a bus (not illustrated) that connects these components to each other. In the controller  3400 , the CPU reads out a program stored in the auxiliary storage to the main storage and executes the program to function as an environment information acquirer  411 , an image acquirer  412 , an operation receiver  413 , a device controller  414 , a time keeper  415 , a history information generator  416 , a history information sender  417 , a device setting updater  2419 , an operation mode setter  420 , a user identifier  421 , a weather information acquirer  2422 , a coefficient acquirer  2423 , a coefficient determiner  3425 , and a coefficient setter  3424 , as illustrated in  FIG. 41 . Note that, in  FIG. 41 , the constituents that are the same as in Embodiments 1 and 3 are denoted with the same reference numerals as used in  FIGS. 3 and 30 . Additionally, the auxiliary storage includes a device setting storage  431 , a user information storage  432 , an operation mode storage  433 , a history information storage  434 , a neural network storage  2436 , and a weather information storage  2437 . Note that the CPU, the main storage, and the auxiliary storage are the same as the CPU  401 , the main storage  402 , and the auxiliary storage  403  illustrated in  FIG. 28 . The coefficient acquirer  2423  acquires, from the cloud server  3002  via the external network NT 1 , coefficient information including initial weighting coefficient information expressing an initial weighting coefficient of the neural network set initially in the neuro engine  404 . Here, the coefficient acquirer  2423  acquires the coefficient information including the initial weighting coefficient information from the cloud server  3002  by sending coefficient request information requesting, to the cloud server  3002 , sending of the coefficient information. 
     The coefficient determiner  3425  determines the weighting coefficient of the neural network on the basis of the operation history information, the environment history information, and the weather record information. Firstly, the coefficient determiner  3425  acquires the initial weighting coefficient information from the neural network storage  2436 . Then, the coefficient setter  3424  sets, in the neuro engine  404 , the weighting coefficient expressed by the initial weighting coefficient information acquired by the coefficient determiner  3425 . Next, the coefficient determiner  3425  acquires the device setting parameter calculated by the neuro engine  404  on the basis of a past environment parameter expressed by the environment history information, a date and time expressed by date and time information, and information obtained by quantifying the past weather condition expressed by the weather record information. Next, the coefficient determiner  3425  acquires a past device setting parameter expressed by the operation history information stored in the history information storage  434 , and calculates an error from the device setting parameter calculated by the neuro engine  404 . Then, the coefficient determiner  3425  determines, on the basis of the calculated error, the weighting coefficient of the neural network by the backpropagation method. 
     The coefficient setter  3424  sets the weighting coefficient determined by the coefficient determiner  3425  as the weighting coefficient of the neural network. The neuro engine  404  uses the neural network to calculate the future device setting parameter of the air conditioner  3004  from the weather prediction information and the environment parameter indicating the environment at present included in the environment history information. 
     The hardware configuration of the cloud server  3002  is the same as the hardware configuration of the cloud server  2  of Embodiment 1 illustrated in  FIG. 10 . The CPU  201  illustrated in  FIG. 10  reads out a program stored in the auxiliary storage  203  to the main storage  202  and executes the program to function as a history information acquirer  3211 , a weather record acquirer  3212 , a coefficient setter  213 , a neural network calculator  214 , a coefficient determiner  215 , a coefficient information generator  3218 , and a coefficient sender  3219 , as illustrated in  FIG. 42 . Note that, in  FIG. 42 , the constituents that are the same as in Embodiment 3 are denoted with the same reference numerals as used in  FIG. 31 . Additionally, as illustrated in  FIG. 42 , the auxiliary storage  203  illustrated in  FIG. 10  includes a history information storage  231 , a weather information storage  232 , and an initial coefficient storage  3233 . The initial coefficient storage  3233  stores information expressing the initial coefficient of the neural network determined on the basis of the weather record information and the history information including the operation history information and the environment history information of the air conditioner of the other house in which the air conditioner, of the same model as the air conditioner  3004  installed in the house H, is installed. 
     The history information acquirer  3211  acquires the history information including the operation history information and the environment history information of the air conditioner of the other house in which the air conditioner, of the same model as the air conditioner  3004  installed in the house H, is installed. In one example, the history information acquirer  3211  acquires the history information, via the external network NT 1 , from the customer server  3003  that manages customers that purchase the air conditioner. The weather record acquirer  3212  acquires the weather record information, expressing the past weather condition of the region in which the house of the household exists, corresponding to the history information. Here, the weather record acquirer  3212  acquires the weather record information from the weather server  3  via the external network NT 1 . As in Embodiment 1, the coefficient determiner  215  determines the weighting coefficient of the neural network on the basis of the history information and the weather record information. The coefficient information generator  3218  generates coefficient information that includes information expressing the weighting coefficient determined by the coefficient determiner  215  and information expressing that the weighting coefficient is the initial coefficient. The coefficient sender  3219  sends the coefficient information generated by the coefficient information generator  3218  to the air conditioner  3004  via the external network NT 1 . 
     Next, the operations of the control system according to the present embodiment are described while referencing  FIGS. 43 and 44 . Firstly, as illustrated in  FIG. 43 , history request information requesting, to the customer server  3003 , sending of the history information is sent from the cloud server  3002  to the customer server  3003  (step S 51 ). Here, the history information includes the operation history information and the environment history information of the air conditioner of the other house in which the air conditioner, of the same model as the air conditioner  3004 , is installed. Meanwhile, when the customer server  3003  receives the history request information, the customer server  3003  identifies the other house in which the air conditioner, of the same model as the air conditioner  3004 , is installed, and generates history information including the operation history information and the environment history information of the air conditioner installed in the identified house, and history attribute information (step S 52 ). Next, the history information and the history attribute information that are generated are sent from the customer server  3003  to the cloud server  3002  (step S 53 ). 
     Next, a weather record information request requesting, to the weather server  3 , sending of the weather record information is sent from the cloud server  3002  to the weather server  3  (step S 54 ). Meanwhile, when the weather server  3  receives the weather record request information, the weather server  3  generates weather record information of the region in which the house H exists (step S 55 ). Here, the weather record information is weather record information, expressing the past weather condition of the region in which the house of the household exists, that corresponds to the history information. Then, the generated weather record information is sent from the weather server  3  to the cloud server  3002  (step S 56 ). Meanwhile, when the cloud server  3002  receives the weather record information, the cloud server  2  stores the received weather record information in the weather information storage  232 . Then, the cloud server  3002  determines, as the initial coefficient, the weighting coefficient of the neural network on the basis of the operation history information, the environment history information, and the weather record information that are acquired (step S 57 ). The cloud server  3002  stores the initial weighting coefficient information expressing the determined initial weighting coefficient in the initial coefficient storage  3233 . 
     Next, it is assumed that a new air conditioner  3004  is installed in the house H and is started up. At this time, coefficient request information requesting, to the weather server  3002 , sending of the initial coefficient is sent from the air conditioner  3004  to the weather server  3002  (step S 58 ). Meanwhile, when the cloud server  3002  receives the coefficient request information, the cloud server  3002  generates coefficient information including the initial weighting coefficient information stored in the initial coefficient storage  3233 , and coefficient attribute information (step S 59 ). The structures of the coefficient information and the coefficient attribute information are the same as the structures of the coefficient information and the coefficient attribute information described using  FIG. 33  in Embodiment 3. Next, the coefficient information and the coefficient attribute information that are generated are sent from the cloud server  3002  to the air conditioner  3004  (step S 60 ). Meanwhile, when the air conditioner  3004  receives the coefficient information and the coefficient attribute information, the air conditioner  3004  stores the received coefficient information and coefficient attribute information in the neural network storage  2436 . 
     Thereafter, it is assumed that the air conditioner  3004  determines that a predetermined update period of the weighting coefficient of the neural network has arrived. In this case, weather record request information requesting, to the weather server  3 , sending of the weather record information is sent from the air conditioner  3004  to the weather server  3  (step S 61 ) and, meanwhile, when the weather server  3  receives the weather record request information, the weather server  3  generates the weather record information for the region in which the house H exists (step S 62 ). Next, the generated weather record information is sent from the weather server  3  to the air conditioner  3004  (step S 63 ). Meanwhile, when the air conditioner  3004  receives the weather record information, the air conditioner  3004  stores the received weather record information in the weather information storage  2437 . Then, the air conditioner  3004  determines the weighting coefficient of the neural network on the basis of the operation history information, the environment history information, the date and time information, the user information, and the weather record information that are acquired (step S 64 ). The air conditioner  3004  stores weighting coefficient information expressing the determined weighting coefficient in the neural network storage  2436 . Thereafter, the series of processing from step S 61  to step S 64  is repeatedly executed every time the update period of the weighting coefficient of the neural network arrives. 
     Next, as illustrated in  FIG. 44 , it is assumed that the air conditioner  3004  receives a switching operation performed by the user for switching to the automatic mode (step S 65 ). In this case, the operation mode is set to the automatic mode by the air conditioner  3004  storing, in the operation mode storage  433 , operation mode information expressing that the operation mode is the automatic mode (step S 66 ). 
     Then, it is assumed that the air conditioner  3004  determines that the update period of device setting information of the air conditioner  3004  has arrived. In this case, weather information request information requesting, to the weather server  3 , sending of the weather information including the weather prediction information and the weather record information is sent from the air conditioner  3004  to the weather server  3  (step S 67 ). Meanwhile, when the weather server  3  receives the weather information request information, the weather server  3  identifies the weather prediction information and the weather record information of the region in which the house H exists, and generates the weather information including the weather prediction information and the weather record information that are identified (step S 68 ). Next, the generated weather information is sent from the weather server  3  to the air conditioner  3004  (step S 69 ). 
     Next, the air conditioner  3004  uses the neural network, in which the weighting coefficient is set to calculate the future device setting parameter of the air conditioner  3004  from the weather prediction information and the environment parameter indicating the environment at present included in the environment history information (step S 70 ). Then, the air conditioner  3004  uses the calculated device setting parameter to update the device setting information stored in the device setting storage  431  (step S 71 ). Thereafter, the series of processing from step S 67  to step S 71  is repeatedly executed every time the update period of the device setting information arrives. 
     Next, device control processing executed by the air conditioner  3004  according to the present embodiment is described while referencing  FIG. 45 . In one example, this device control processing starts when the power to the air conditioner  3004  is turned ON. 
     Firstly, the coefficient acquirer  2423  sends coefficient request information to the cloud server  3002  (step S 3101 ) to acquire, from the cloud server  3002 , the coefficient information including the initial weighting coefficient information of the neural network and the coefficient attribute information (step S 3102 ). The coefficient acquirer  2423  stores the initial weighting coefficient information included in the coefficient information and the coefficient attribute information that are acquired in the neural network storage  2436 . 
     Next, the coefficient determiner  3425  determines whether a coefficient update period of the neural network has arrived (step S 3103 ). When the coefficient determiner  3425  determines that the coefficient update period has not arrived (step S 3103 ; No), the processing of hereinafter described step S 3110  is executed without modification. However, it is assumed that the coefficient determiner  3425  determines that the coefficient update period has arrived (step S 3103 ; Yes). In this case, the weather information acquirer  2422  sends the weather record request information to the weather server  3  (step S 3104 ) to acquire the weather record information from the weather server  3  (step S 3105 ). The weather information acquirer  2422  stores the acquired weather record information in the weather information storage  2437 . Thereafter, coefficient determination processing is executed (step S 3106 ). The content of this coefficient determination processing is the same as that of the coefficient determination processing described using  FIG. 17  in Embodiment 1. 
     Next the processing of steps S 3107  and S 3108  is executed. The content of the processing of steps S 3107  and S 3108  is the same as that of the processing of steps S 105  and S 106  described using  FIG. 15  in Embodiment 1. Then, the device setting updater  2419  references the operation mode information stored in the operation mode storage  433  to determine whether the operation mode of the air conditioner  3004  is the automatic mode (step S 3109 ). When the device setting updater  2419  determines that the operation mode of the air conditioner  3004  is the manual mode (step S 3109 ; No), the processing of step S 3103  is executed again. However, when the device setting updater  2419  determines that the operation mode of the air conditioner  3004  is the automatic mode (step S 3109 ; Yes), the device setting updater  2419  determines whether a predetermined update period of the device setting information of the air conditioner  3004  has arrived (step S 3110 ). When the device setting updater  2419  determines that the update period of the device setting information of the air conditioner  3004  has not arrived (step S 3110 ; No), the processing of step S 3103  is executed again. However, it is assumed that the device setting updater  2419  determines that the update period of the device setting information of the air conditioner  3004  has arrived (step S 3110 ; Yes). In this case, the series of processing from step S 3111  to step S 3114  is executed. Here, the content of the series of processing from step S 3111  to step S 3114  is the same as the series of processing from step S 2112  to step S 2115  described using  FIG. 34  in Embodiment 3. Then, the processing of step S 3103  is executed again. 
     Next, coefficient information generation processing executed by the cloud server  3002  according to the present embodiment is described while referencing  FIG. 46 . In one example, this coefficient information generation processing starts when the power to the cloud server  3002  is turned ON. 
     Firstly, the history information acquirer  3211  sends, to the customer server  3003 , history request information requesting, to the customer server  3003 , sending of the history information including the operation history information and the environment history information of an air conditioner of the same model as the air conditioner  3004  installed in the house H (step S 3201 ) to acquire the history information and the history attribute information from the customer server  3003  (step S 3202 ). Next, the weather record acquirer  2212  sends weather record request information requesting sending, to the weather server  3 , of the weather record information (step S 3203 ) to acquire the weather record information from the weather server  3  (step S 3204 ). Next, coefficient determination processing for determining, on the basis of the operation history information and the environment history information included in the history information and the weather record information, the coefficient of the neural network described above is executed (step S 3205 ). The content of the coefficient determination processing is the same as that of the coefficient determination processing described using  FIG. 17  in Embodiment 1. The initial weighting coefficient information expressing the initial weighting coefficient calculated by this coefficient determination processing is stored in the initial coefficient storage  3233 . 
     Thereafter, the coefficient information generator  3218  determines whether the coefficient request information is acquired from the air conditioner  3004  (step S 3206 ). When the coefficient information generator  3218  determines that the coefficient request information is not acquired (step S 3206 ; No), the processing of step S 3201  is executed again. Meanwhile, when the coefficient information generator  3218  determines that the coefficient request information is acquired (step S 3206 ; Yes), the coefficient information generator  3218  generates the coefficient information including the initial weighting coefficient information stored in the initial coefficient storage  3233 , and the coefficient attribute information (step S 3207 ). Thereafter, the coefficient sender  3219  sends the coefficient information and the coefficient attribute information that are generated to the air conditioner  3004  (step S 3208 ). Then, the processing of step S 3201  is executed again. 
     As described above, with the control system according to the present embodiment, in the cloud server  3002 , the coefficient determiner  215  determines the initial coefficient of the neural network and sends, to the air conditioner  3004 , the coefficient information that includes the information expressing the determined initial coefficient. Additionally, in the air conditioner  3004 , the coefficient setter  2121  sets the weighting coefficient of the neural network to the initial coefficient only one time after startup of the air conditioner  3004 . Then, in the air conditioner  3004 , the coefficient determiner  3122  updates the weighting coefficient of the neural network. Moreover, the neuro engine  2104  uses the neural network, for which the weighting coefficient is updated by the coefficient determiner  3122 , to calculate the future device setting parameter of the air conditioner  3004  from the weather prediction information and the environment parameter at present included in the environment history information. Then, the device setting updater  2419  updates the device setting information stored in the device setting storage  431  using the device setting information generated on the basis of the device setting parameter calculated by the neuro engine  2104 . Thus, the device controller  414  of the air conditioner  3004  controls the air conditioner  3004  using the device setting parameter calculated by the neuro engine  2104 . As a result, the device controller  414  can control the air conditioner  3004  by merely acquiring the weather information from the cloud server  2002 . Therefore, the amount of information exchanged between the air conditioner  3004  and the cloud server  3002  is reduced, which leads to the benefit of a reduction of the effects, on the operations of the air conditioner  3004 , of the communication traffic on the external network NT 1 . 
     Embodiment 6 
     With a control system according to the present embodiment, a device determines a weighting coefficient of a neural network, and uses a second neural network for which the weighting coefficient is determined to calculate a preference feature amount that indicates a feature amount of a preference of a user of the device. Here, the neural network has a predetermined number of nodes and a predetermined number of layers, and is for calculating the preference feature amount of the device. The server manages teacher information that is used when determining the weighting coefficient of the second neural network in the device. The server includes a teacher information identifier that identifies the teacher information to be used when determining the weighting coefficient of the second neural network, and a teacher information sender that sends the teacher information to the device. Additionally, the device includes a teacher information acquirer that acquires the teacher information; a history information acquirer that acquires operation history information and environment history information of the device; a weather information acquirer that acquires weather information including weather record information expressing a past weather condition and weather prediction information expressing a future weather condition; a coefficient determiner that determines the weighting coefficient of the second neural network on the basis of the teacher information; a neural network calculator that uses the second neural network to calculate the preference feature amount from the operation history information, the environment history information, and the weather record information; and a schedule identifier that identifies schedule information corresponding to the calculated preference feature amount. 
     As with the control system described using  FIG. 1  in Embodiment 1, the control system according to the present embodiment includes an air conditioner and a water heater installed in a house H, and a cloud server that is capable of communicating with the air conditioner and the water heater via an external network NT 1 . Note that, in the present embodiment, constituents that are the same as in Embodiments 4 and 5 are denoted with the same reference numerals used in Embodiments 4 and 5. Additionally, it is assumed that an internal network NT 2  is laid and a router and a data line terminal device that are connected to the internal network NT 2  are installed in the house H. 
     The hardware configuration of the air conditioner  17004  according to the present embodiment is the same as the hardware configuration of the air conditioner  2004  illustrated in  FIG. 28  of Embodiment 2. In a device controller  17400 , the CPU reads out a program stored in an auxiliary storage to a main storage and executes the program to function as an environment information acquirer  411 , an image acquirer  412 , an operation receiver  413 , a device controller  414 , a time keeper  415 , a history information generator  416 , a history information sender  417 , a device setting updater  2419 , an operation mode setter  420 , a user identifier  421 , a weather information acquirer  2422 , a teacher information acquirer  17423 , a coefficient setter  17424 , a coefficient determiner  17425 , and a schedule identifier  16425 , as illustrated in  FIG. 47 . Note that, in  FIG. 47 , the constituents that are the same as in Embodiments 4 and 5 are denoted with the same reference numerals as used in  FIGS. 36 and 41 . Additionally, the auxiliary storage includes a device setting storage  431 , a user information storage  432 , an operation mode storage  433 , a history information storage  434 , a neural network storage  17436 , a weather information storage  2437 , and a schedule storage  16435 . Note that the CPU, the main storage, and the auxiliary storage are the same as the CPU  401 , the main storage  402 , and the auxiliary storage  403  illustrated in  FIG. 28 . As described above, the schedule storage  16435  associates a plurality of types of schedule information with the preference feature amount, and stores the associated information. Additionally, the neural network storage  17436  stores the weighting coefficient of the neural network together with the teacher information that is acquired from a cloud server  17002  and is used by the coefficient determiner  17425  to determine the neural network coefficient. 
     The teacher information acquirer  17423  acquires the teacher information from the cloud server  17002 . Here, the teacher information acquirer  17423  acquires the teacher information from the cloud server  17002  by sending teacher information request information requesting, to the cloud server  17002 , sending of the teacher information. Additionally, the teacher information acquirer  17423  stores the acquired teacher information in the neural network storage  17436 . 
     The coefficient determiner  17425  determines the weighting coefficient of the neural network on the basis of the teacher information. Firstly, the coefficient determiner  17425  sets a predetermined initial weighting coefficient in the neuro engine  404 . Next, the coefficient determiner  17425  acquires the preference feature amount that the neuro engine  404  calculates on the basis of the operation history information, the environment history information, and the weather record information included in the teacher information stored in the neural network storage  17436 . Then, the coefficient determiner  17425  calculates an error between the preference feature amount included in the teacher information stored in the neural network storage  17436  and the preference feature amount calculated by the neuro engine  404 . Then, the coefficient determiner  17425  determines, on the basis of the calculated error, the weighting coefficient of the neural network by the backpropagation method. 
     The coefficient setter  17424  sets the weighting coefficient determined by the coefficient determiner  17425  as the weighting coefficient of the neural network. Then, the neuro engine  404  uses the neural network in which the weighting coefficient is set to calculate the preference feature amount from the operation history information, the environment history information, and the weather record information. 
     The hardware configuration of the cloud server  17002  is the same as the hardware configuration of the cloud server  2  of Embodiment 1 illustrated in  FIG. 10 . With the cloud server  17002 , the CPU reads out a program stored in the auxiliary storage to the main storage and executes the program to function as a teacher information identifier  17218  and a teacher information sender  17219 , as illustrated in  FIG. 48 . Additionally, the auxiliary storage includes a teacher information storage  15235 . Note that the CPU, the main storage, and the auxiliary storage are the same as the CPU  201 , the main storage  202 , and the auxiliary storage  203  illustrated in  FIG. 10 . As in Embodiment 2, the teacher information storage  15235  stores teacher information that is used by the coefficient determiner  16213  to determine the neural network coefficient. When the teacher information identifier  17218  acquires, from the air conditioner  17004 , the teacher information request information requesting sending of the teacher information, the teacher information identifier  17218  identifies, from among the plurality of types of teacher information stored in the teacher information storage  15235 , the teacher information corresponding to the teacher information request information. The teacher information sender  17219  sends the identified teacher information to the air conditioner  17004  that is the sender of the teacher information request information. 
     Next, the operations of the control system according to the present embodiment are described while referencing  FIG. 49 . Firstly, it is assumed that a new air conditioner  17004  is installed in the house H and is started up. At this time, teacher information request information requesting, to the cloud server  17002 , sending of the teacher information is sent from the air conditioner  17004  to the cloud server  17002  (step S 17051 ). When the cloud server  17002  receives the teacher information request information, the cloud server  17002  identifies, from among the plurality of types of teacher information stored in the teacher information storage  15235 , the teacher information corresponding to the air conditioner  17004  (step S 17052 ). Then, the identified teacher information is sent from the cloud server  17002  to the air conditioner  17004  (step S 17053 ). Meanwhile, when the air conditioner  17004  receives the teacher information, the air conditioner  17004  stores the received teacher information in the neural network storage  17436 . Next, the air conditioner  17004  determines the weighting coefficient of the neural network on the basis of the teacher information stored in the neural network storage  17436  (step S 17054 ). 
     Next, it is assumed that the air conditioner  17004  receives a switching operation performed by the user for switching to the automatic mode (step S 17055 ). In this case, the operation mode is set to the automatic mode by the air conditioner  17004  storing, in the operation mode storage  433 , operation mode information expressing that the operation mode is the automatic mode (step S 17056 ). 
     Then, it is assumed that the air conditioner  17004  determines that an update period of the schedule information has arrived. In this case, weather record request information requesting, to the weather server  3 , sending of the weather record information is sent from the air conditioner  17004  to the weather server  3  (step S 17057 ). Meanwhile, when the weather server  3  receives the weather record request information, the weather server  3  generates the weather record information of the region in which the house H exists (step S 17058 ). Next, the generated weather information is sent from the weather server  3  to the air conditioner  17004  (step S 17059 ). 
     Thereafter, the air conditioner  17004  uses the neural network in which the weighting coefficient is set to calculate the preference feature amount from the operation history information, the environment history information, and the weather record information. Moreover, the air conditioner  16004  identifies, from the plurality of types of schedule information stored in the schedule storage  16435 , the schedule information corresponding to the calculated preference feature amount (step S 17060 ). Then, the air conditioner  17004  updates, on the basis of the identified schedule information, the device setting information stored in the device setting storage  431  (step S 17061 ). Thereafter, the processing of the aforementioned step S 17061  is repeatedly executed every time an update period of the device setting information arrives. 
     Next, device control processing executed by the air conditioner  17004  according to the present embodiment is described while referencing  FIG. 50 . In one example, this device control processing starts when the power to the air conditioner  17004  is turned ON. Firstly, the teacher information acquirer  17423  sends the teacher information request information to the cloud server  17002  (step S 17101 ) to acquire the teacher information from the cloud server  17002  (step S 17102 ). The teacher information acquirer  17423  stores the acquired teacher information in the neural network storage  17436 . 
     Next, coefficient determination processing for determining the weighting coefficient of the neural network on the basis of the teacher information is executed (step S 17103 ). The content of this coefficient determination processing is the same as that of the coefficient determination processing described using  FIG. 26  in Embodiment 2. Then, the processing of steps S 17104  and S 17105  is executed. Here, the processing of steps S 17104  and S 17105  is the same as that of the processing of steps S 105  and S 106  described using  FIG. 15  in Embodiment 1. Then, the device setting updater  16419  references the operation mode information stored in the operation mode storage  433  to determine whether the operation mode of the air conditioner  17004  is the automatic mode (step S 17106 ). When the device setting updater  16419  determines that the operation mode of the air conditioner  17004  is the manual mode (step S 17106 ; No), the processing of step S 17104  is executed again. However, it is assumed that the device setting updater  16419  determines that the operation mode of the air conditioner  17004  is the automatic mode (step S 17106 ; Yes). In this case, the schedule identifier  16425  determines whether a predetermined schedule update period of the air conditioner  17004  has arrived (step S 17107 ). When the schedule identifier  16425  determines that the schedule update period of the air conditioner  17004  has not arrived (step S 17105 ; No), the processing of hereinafter described step S 17111  is executed. 
     However, it is assumed that the schedule identifier  16425  determines that the schedule update period of the air conditioner  17004  has arrived (step S 17107 ; Yes). In this case, the weather information acquirer  2422  sends, to the weather server  3 , the weather record request information (step S 17108 ) to acquire the weather record information (step S 17109 ). Next, the neuro engine  404  uses the neural network to calculate the preference feature amount from the operation history information, the environment history information, and the weather prediction information. Moreover, the schedule identifier  16425  identifies, from the plurality of types of schedule information stored in the schedule storage  16435 , the schedule information corresponding to the calculated preference feature amount (step S 17110 ). 
     Thereafter, the device setting updater  16419  determines whether an update period of the device setting information of the air conditioner  17004  has arrived (step S 17111 ). When the device setting updater  16419  determines that the update period of the device setting information of the air conditioner  17004  has not arrived (step S 17111 ; No), the processing of step S 17104  is executed again. However, it is assumed that the device setting updater  16419  determines that the update period of the device setting information of the air conditioner  17004  has arrived (step S 17111 ; Yes). In such a case, the device setting updater  16419  updates, on the basis of the schedule information identified by the schedule identifier  16426 , the device setting information stored in the device setting storage  431  (step S 17112 ). Then, the processing of step S 17104  is executed again. 
     Next, teacher information sending processing executed by the cloud server  17002  according to the present embodiment is described while referencing  FIG. 51 . In one example, this teacher information sending processing starts when the power to the cloud server  17002  is turned ON. Firstly, the teacher information identifier  17218  determines whether the teacher information request information requesting sending of the teacher information is acquired from the air conditioner  17004  (step S 17201 ). When the teacher information identifier  17218  determines that the teacher information request information is not acquired (step S 17201 ; No), the processing of step S 17201  is executed again. Meanwhile, when the teacher information identifier  17218  determines that the teacher information request information is acquired (step S 17201 ; Yes), the teacher information identifier  17218  identifies, from among the plurality of types of teacher information stored in the teacher information storage  15235 , the teacher information corresponding to the teacher information request information (step S 17202 ). Next, the teacher information sender  17219  sends the identified teacher information to the air conditioner  17004  that is the sender of the teacher information request information (step S 17203 ). Then, the processing of step S 17201  is executed again. 
     As described above, with the control system according to the present embodiment, in the cloud server  17002 , the coefficient determiner  16215  determines the initial coefficient of the neural network and sends, to the air conditioner  17004 , the coefficient information that includes the determined initial weighting coefficient information. Additionally, in the air conditioner  17004 , the coefficient setter  17424  sets the weighting coefficient of the neural network to the initial weighting of coefficient expressed in the initial weighting coefficient information only one time after startup of the air conditioner  17004 . Then, in the air conditioner  17004 , the coefficient determiner  17425  updates the weighting coefficient of the neural network. Then, the neuro engine  404  uses the neural network, for which the weighting coefficient is updated by the coefficient determiner  17425 , to calculate the preference feature amount from the weather prediction information, the operation history information, and the environment history information. Moreover, the schedule identifier  16425  identifies, from the plurality of types of schedule information stored in the schedule storage  16435 , the schedule information corresponding to the calculated preference feature amount. Additionally, the device setting updater  16419  updates, on the basis of the schedule information identified by the schedule identifier  16425 , the device setting information stored in the device setting storage  431 . Thus, the device controller  17400  of the air conditioner  17004  controls the air conditioner  17004  in accordance with the schedule corresponding to the preference feature amount calculated by the neuro engine  404 . As a result, the device controller  414  can control the air conditioner  17004  by merely acquiring the weather information from the cloud server  17002  every time a coefficient information update period arrives. Therefore, the amount of information exchanged between the air conditioner  17004  and the cloud server  17002  is reduced, which leads to the benefit of a reduction of the effects, on the operations of the air conditioner  17004 , of the communication traffic on the external network NT 1 . 
     Embodiment 7 
     With a control system according to the present embodiment, a device determines a weighting coefficient of a neural network, and uses the neural network for which the weighting coefficient is determined to calculate a preference feature amount that is a feature amount of a preference of a user. Here, the neural network has a predetermined number of nodes and a predetermined number of layers, and is for calculating the preference feature amount that is a feature amount of a preference of a user. Additionally, the device includes a neural network calculator that uses the neural network to calculate the preference feature amount from weather prediction information, operation history information, and environment history information, a schedule identifier that identifies schedule information corresponding to the calculated preference feature amount, and a preference feature amount sender that sends the calculated preference feature amount to another device. 
     As illustrated in  FIG. 52 , the control system according to the present embodiment includes an air conditioner  4004 , a cloud server  3002  capable of communicating with the air conditioner  4004  via an external network NT 1 , and an air conditioner  4052  capable of communicating with the air conditioner  4004  via an internal network NT 2 . Note that, in  FIG. 52 , the constituents that are the same as in Embodiment 1 are denoted with the same reference numerals as used in  FIG. 1 . A weather server  3  and the customer server  3003  described in Embodiment 3 are connected to the external network NT 1 . Operation devices  4006 ,  4072  for operating the air conditioners  4004 ,  4052  are installed in the house H. Additionally, as in Embodiment 1, a router  82  and a data line terminal device  81  are installed in the house H. 
     The hardware configuration of the air conditioner  4004  according to the present embodiment is the same as the hardware configuration of the air conditioner  2004  according to Embodiment 3, and includes a controller  4400 . In the controller  4400 , as illustrated in FIG.  53 , for example, the CPU reads out a program stored in an auxiliary storage to a main storage and executes the program to function as an environment information acquirer  411 , an image acquirer  412 , an operation receiver  413 , a device controller  414 , a time keeper  415 , a history information generator  416 , a history information sender  417 , a device setting updater  2419 , an operation mode setter  420 , a user identifier  421 , a weather information acquirer  2422 , a coefficient acquirer  2423 , a coefficient setter  3424 , a coefficient determiner  3425 , a schedule identifier  16425 , and a preference feature amount sender  4427 . Note that, in  FIG. 53 , the constituents that are the same as in Embodiment 6 are denoted with the same reference numerals as used in  FIG. 47 . Additionally, the auxiliary storage includes a device setting storage  431 , a user information storage  432 , an operation mode storage  433 , a history information storage  434 , a neural network storage  2436 , a weather information storage  2437 , and a schedule storage  16435 . Note that the CPU, the main storage, and the auxiliary storage are the same as the CPU  401 , the main storage  402 , and the auxiliary storage  403  illustrated in  FIG. 28 . 
     The schedule storage  16435  associates a plurality of types of schedule information expressing an operation schedule of the air conditioner  4004  with the preference feature amount, and stores the associated information. The schedule identifier  16425  identifies, from the plurality of types of schedule information stored in the schedule storage  16435 , the schedule information the basis of the preference feature amount of the user calculated by the neuro engine  404  from the weather record information, the operation history information, and the environment history information. The preference feature amount sender  4427  sends, to the air conditioner  4052 , preference feature amount information expressing the preference feature amount calculated by the neuro engine  404 . 
     As with the air conditioner  4  described in Embodiment 1, the air conditioner  4052  does not include a neuro engine. As illustrated in  FIG. 54 , the air conditioner  4052  includes a controller  4520  and an imaging device  481 . Additionally, the air conditioner  4052  includes a compressor (not illustrated in the drawings) and a blowing fan (not illustrated in the drawings) that operate on the basis of command signals input from the controller  4520 . The controller  4520  includes a CPU  401 , a main storage  402 , an auxiliary storage  403 , a communication interface  405 , a wireless module  407 , an imaging interface  408 , and a bus  409  that connects these components to each other. Note that, in  FIG. 54 , the constituents that are the same as in Embodiment 1 are denoted with the same reference numerals as used in  FIG. 2 . The CPU  401  reads out the program stored in the auxiliary storage  403  to the main storage  402  and executes the program to function as an image acquirer  412 , an operation receiver  413 , a device controller  414 , a time keeper  415 , a preference feature amount acquirer  4418 , a device setting updater  419 , an operation mode setter  420 , and a user identifier  421 , as illustrated in  FIG. 55 . Additionally, the auxiliary storage  403  includes a device setting storage  431 , a user information storage  432 , an operation mode storage  433 , a history information storage  434 , and a schedule storage  435 . The preference feature amount acquirer  4418  acquires the preference feature amount information from the air conditioner  4004 , and notifies the schedule identifier  4425  of the acquired preference feature amount information. The schedule identifier  4425  identifies, from among the plurality of types of schedule information stored in the schedule storage  435 , the schedule information corresponding to the notified preference feature amount. Then, the device setting updater  4419  updates, on the basis of the schedule information identified by the schedule identifier  4425 , the device setting information stored in the device setting storage  431 . 
     Next, the operations of the control system according to the present embodiment are described while referencing  FIG. 56 . Note that, in  FIG. 56 , the processes that are the same as in Embodiment 6 are denoted with the same reference numerals as used in  FIG. 49 . When the air conditioner  4004  determines that a schedule update period has arrived, the series of processing from step S 17057  to S 17060  of  FIG. 56  is executed and, as a result, the air conditioner  4004  acquires the weather record information. Next, the air conditioner  4004  uses the neural network in which the weighting coefficient is set to calculate the preference feature amount from the operation history information, the environment history information, and the weather record information. Moreover, the air conditioner  4004  identifies, from the plurality of types of schedule information stored in the schedule storage  16435 , the schedule information corresponding to the calculated preference feature amount (step S 17060 ). Then, the preference feature amount information expressing the preference feature amount identified by the air conditioner  4004  is sent from the air conditioner  4004  to the air conditioner  4052  (step S 81 ). Meanwhile, when the air conditioner  4052  receives the preference feature amount information, the air conditioner  4052  identifies the schedule information corresponding to the receives preference feature amount information (step S 82 ). Then, when an update period of the device setting information arrives, the air conditioner  4004  uses the identified schedule information to update the device setting information stored in the device setting storage  431  (step S 17061 ). Additionally, the air conditioner  4052  also uses the identified schedule information to update the device setting information stored in the device setting storage  431  (step S 83 ). Thereafter, the processing of step S 17061  and the processing of step S 83  are repeatedly executed every time the update period of the device setting information arrives. 
     Next, device control processing executed by the air conditioner  4004  according to the present embodiment is described while referencing  FIG. 57 . Note that, in  FIG. 57 , the processes that are the same as in Embodiment 6 are denoted with the same reference numerals as used in  FIG. 50 . 
     Firstly, the series of processing from step S 3101  to step S 3112  is executed. Next, the schedule identifier  16425  determines whether the predetermined schedule update period of the air conditioner  4004  has arrived (step S 17105 ). When the schedule identifier  16425  determines that the schedule update period of the air conditioner  17004  has not arrived (step S 17105 ; No), the processing of hereinafter described step S 17109  is executed. However, it is assumed that the schedule identifier  16425  determines that the schedule update period of the air conditioner  17004  has arrived (step S 17105 ; Yes). In this case, the processing of step S 17106  and step S 17107  are executed and, then, the neuro engine  404  uses the neural network to calculate the preference feature amount from the operation history information, the environment history information, and the weather prediction information. Moreover, the schedule identifier  16425  identifies, from the plurality of types of schedule information stored in the schedule storage  16435 , the schedule information corresponding to the calculated preference feature amount (step S 17008 ). Then, the preference feature amount sender  4427  sends a preference feature information amount expressing the preference feature amount calculated by the neuro engine  404  to the air conditioner  4052  (step S 4101 ). Then, the processing of step S 17109  and subsequent processing is executed. 
     As described above, with the control system according to the present embodiment, in the air conditioner  4004 , the neuro engine  404  uses the neural network to calculate the preference feature amount from the operation history information, the environment history information, and the weather prediction information. Moreover, moreover, the schedule identifier  16425  identifies, from the plurality of types of schedule information stored in the schedule storage  16435 , the schedule information corresponding to the calculated preference feature amount, and the preference feature amount sender  4427  sends the preference feature amount calculated by the neuro engine  404  to the air conditioner  4052 . As a result, even though the air conditioner  4052  does not include a neuro engine, the air conditioner can be controlled in accordance with an operation schedule expressed by the schedule information corresponding to the preference feature amount identified in the air conditioner  4004 . Accordingly, the schedule information identified in the air conditioner  4004  that includes the neuro engine  404  can be shared with the air conditioner  4052  that does not include a neuro engine. Therefore, by linking with the air conditioner  4052  that does not include a neuro engine, it is possible to maintain the entire house H, in which the air conditioners  4004 ,  4052  are installed, at an environment that is comfortable to the user. 
     Embodiment 8 
     A control system according to the present embodiment includes a plurality of devices having functions for determining a weighting coefficient of a neural network, and using the neural network for which the weighting coefficient is determined to calculate a future device setting parameter of the device. Here, the neural network has a predetermined number of nodes and a predetermined number of layers, and is for calculating the future device setting parameter. 
     As illustrated in  FIG. 58 , the control system according to the present embodiment includes air conditioners  5041 ,  5042 ,  5043 , and a cloud server  5002 . Note that, in  FIG. 58 , the constituents that are the same as in Embodiment 1 are denoted with the same reference numerals as used in  FIG. 1 . 
     The hardware configuration of the air conditioners  5041 ,  5042 ,  5043  is the same as the hardware configuration of the air conditioner  2004  according to Embodiment 3. The CPU reads out a program stored in an auxiliary storage to a main storage and executes the program to function as an environment information acquirer  411 , an image acquirer  412 , an operation receiver  413 , a device controller  414 , a time keeper  415 , a history information generator  416 , a history information sender  417 , a device setting updater  2419 , an operation mode setter  420 , a user identifier  421 , a weather information acquirer  2422 , a coefficient acquirer  2423 , a coefficient setter  3424 , a coefficient determiner  3425 , a coefficient information generator  5428 , and a coefficient sender  5429 , as illustrated in  FIG. 59 . Note that, in  FIG. 59 , the constituents that are the same as in Embodiment 5 are denoted with the same reference numerals as used in  FIG. 41 . Additionally, the auxiliary storage includes a device setting storage  431 , a user information storage  432 , an operation mode storage  433 , a history information storage  434 , a neural network storage  2436 , and a weather information storage  2437 . Note that the CPU, the main storage, and the auxiliary storage are the same as the CPU  401 , the main storage  402 , and the auxiliary storage  403  illustrated in  FIG. 28 . 
     The coefficient acquirer  2423  is a second coefficient acquirer that acquires coefficient information and coefficient attribute information from the cloud server  5002 . The coefficient information generator  5428  generates coefficient information including weighting coefficient information stored in the neural network storage  2436 , and coefficient attribute information. The coefficient sender  5429  sends the coefficient information and the coefficient attribute information generated by the coefficient information generator  5428  to the cloud server  5002 . When the operation receiver  413  receives an operation for setting the operation mode of the air conditioners  5041 ,  5042 ,  5043 , the operation mode setter  5423  stores operation mode information expressing the operation mode corresponding to the received operation content in the operation mode storage  433 . 
     The hardware configuration of the cloud server  5002  is the same as the hardware configuration of the cloud server  2  described in Embodiment 1. The CPU reads out the program stored in the auxiliary storage to the main storage and executes the program to function as a history information acquirer  3211 , a weather record acquirer  2212 , a coefficient setter  213 , a neural network calculator  214 , a coefficient determiner  215 , a coefficient information generator  5218 , a coefficient sender  5219 , and a coefficient acquirer  5220 , as illustrated in  FIG. 60 . Note that, in  FIG. 60 , the constituents that are the same as in Embodiment 5 are denoted with the same reference numerals as used in  FIG. 42 . Additionally, the auxiliary storage includes a history information storage  231 , a weather information storage  232 , and a neural network storage  5233 . Note that the CPU, the main storage, and the auxiliary storage are the same as the CPU  201 , the main storage  202 , and the auxiliary storage  203  illustrated in  FIG. 10 . The neural network storage  5233  stores initial weighting coefficient information expressing an initial weighting coefficient of the neural network determined on the basis of the operation history information, the environment history information, and the weather record information of an air conditioner of another house in which an air conditioner, of the same the model as the air conditioners  5041 ,  5042 ,  5043  installed in the house H, is installed. Additionally, the neural network storage  5233  associates the weighting coefficient information included in the coefficient information acquired from the air conditioners  5041 ,  5042 ,  5043  with device identification information of the air conditioners  5041 ,  5042 ,  5043  that are the senders of the coefficient information, and stores the associated information. 
     The coefficient information generator  5218  generates coefficient information that includes the weighting coefficient information expressing the weighting coefficient determined by the coefficient determiner  215 . Additionally, the coefficient information generator  5218  generates coefficient information including the weighting coefficient information stored in the neural network storage  5233 , and coefficient attribute information. The coefficient sender  5219  sends the coefficient information and the coefficient attribute information generated by the coefficient information generator  3218  to the air conditioners  5041 ,  5042 ,  5043 . The coefficient acquirer  5220  is a first coefficient acquirer that, when the coefficient information and the coefficient attribute information sent from the air conditioners  5041 ,  5042 ,  5043  are acquired, associates the weighting coefficient information included in the acquired coefficient information with the device identification information of the air conditioners  5041 ,  5042 ,  5043 , and stores the associated information in the neural network storage  5233 . 
     Next, the operations of the control system according to the present embodiment are described while referencing  FIGS. 61 and 62 . Note that, in  FIGS. 61 and 62 , the processes that are the same as in Embodiment 5 are denoted with the same reference numerals as used in  FIGS. 43 and 44 . As illustrated in  FIG. 61 , firstly, the series of processing from step S 51  to step S 57  is executed and, as a result, the initial weighting coefficient of the neural network is determined. Here, the cloud server  5002  stores the initial weighting coefficient information expressing the determined initial weighting coefficient in the neural network storage  5233 . Next, it is assumed that a new air conditioner  5041  ( 5042 ,  5043 ) is installed in the house H and is started up. At this time, coefficient request information requesting, to the cloud server  5002 , sending of the coefficient information including the initial weighting coefficient information is sent from the air conditioner  5041  ( 5042 ,  5043 ) to the cloud server  5002  (step S 58 ). Meanwhile, when the cloud server  5002  receives the coefficient request information, the cloud server  5002  generates coefficient information including the initial weighting coefficient information stored in the neural network storage  5233 , and coefficient attribute information (step S 59 ). Next, the coefficient information and the coefficient attribute information that are generated are sent from the cloud server  5002  to the air conditioner  5041  ( 5042 ,  5043 ) (step S 60 ). Then, it is assumed that the air conditioner  5041  ( 5042 ,  5043 ) determines that a predetermined coefficient update period for updating the weighting coefficient of the neural network has arrived. In this case, the series of processing from step S 61  to S 65  is executed and, as a result, the air conditioner  5041  ( 5042 ,  5043 ) acquires the weather record information. Then, the air conditioner  5041  ( 5042 ,  5043 ) determines the weighting coefficient of the neural network on the basis of the operation history information, the environment history information, the date and time information, and the user information stored in the history information storage  434 , and the weather record information stored in the weather information storage  2437  (step S 66 ). Thereafter, the series of processing from step S 61  to step S 66  is repeatedly executed every time the update period of the weighting coefficient of the neural network arrives. 
     Next, it is assumed that the air conditioner  5041  ( 5042 ,  5043 ) determines that the update period of the device setting information stored in the device setting storage  431  has arrived. In this case, the series of processing from step S 67  to S 69  is executed and, as a result, the air conditioner  5041  ( 5042 ,  5043 ) acquires the weather record information. Next, as illustrated in  FIG. 62 , the air conditioner  5041  ( 5042 ,  5043 ) uses the neural network to calculate the future device setting parameter of the air conditioner  5041  ( 5042 ,  5043 ) from the weather prediction information and the environment parameter indicating the environment at present (step S 70 ). Then, the air conditioner  5041  ( 5042 ,  5043 ) uses the calculated device setting parameter to update the device setting information stored in the device setting storage  431  (step S 71 ). Thereafter, the series of processing from step S 67  to step S 71  is repeatedly executed every time the update period of the device setting information arrives. 
     Thereafter, it is assumed that the air conditioner  5041  ( 5042 ,  5043 ) receives an operation for uploading, to the cloud server  3002 , the coefficient information including the weighting coefficient information stored in the neural network storage  2436  (step S 1009 ). In this case, the air conditioner  5041  ( 5042 ,  5043 ) uses the weighting coefficient information stored in the neural network storage  2436  to generate the coefficient information and to generate the coefficient attribute information (step S 1010 ). Then, the coefficient information and the coefficient attribute information that are generated are sent from the air conditioner  5041  ( 5042 ,  5043 ) to the cloud server  5002  (step S 1011 ). Meanwhile, when the cloud server  5002  receives the coefficient information and the coefficient attribute information, the cloud server  5002  associates the received coefficient information and coefficient attribute information with the device identification information identifying the air conditioner  5041  ( 5042 ,  5043 ), and stores the associated information in the neural network storage  2436  (step S 1012 ). 
     Additionally, it is assumed that the air conditioner  5041  ( 5042 ,  5043 ) receives an operation for downloading the coefficient information from the cloud server  5002  (step S 1013 ). In this case, coefficient request information requesting, to the cloud server  5002 , sending of the coefficient information is sent from the air conditioner  5041  ( 5042 ,  5043 ) to the cloud server  5002  (step S 1014 ). This coefficient request information includes the device identification information of the air conditioner  5041  ( 5042 ,  5043 ). Meanwhile, when the cloud server  5002  receives the coefficient request information, the cloud server  5002  identifies the coefficient information associated with the device identification information included in the received coefficient request information (step S 1015 ). Next, the identified coefficient information and the coefficient attribute information corresponding thereto are sent from the cloud server  5002  to the air conditioner  5041  ( 5042 ,  5043 ) (step S 1016 ). Meanwhile, when the air conditioner  5041  ( 5042 ,  5043 ) receives the coefficient information and the coefficient attribute information, the air conditioner  5041  ( 5042 ,  5043 ) stores the weighting coefficient information included in the received coefficient information in the neural network storage  2436  (step S 1017 ). 
     Next, device control processing executed by the air conditioner  5041  ( 5042 ,  5043 ) according to the present embodiment is described while referencing  FIGS. 63 and 64 . Note that, in  FIGS. 63 and 64 , the processes that are the same as in Embodiment 5 are denoted with the same reference numerals as used in  FIG. 44 . Firstly, the coefficient acquirer  2423  sends coefficient request information to the cloud server  5002  (step S 3101 ) to acquire, from the cloud server  5002 , coefficient information including information expressing an initial coefficient of the neural network (step S 3102 ). The coefficient acquirer  2423  stores the acquired information expressing the initial coefficient in the neural network storage  2436 . 
     Next, the coefficient determiner  3425  determines whether a coefficient update period of the neural network has arrived (step S 3103 ). When the coefficient determiner  3425  determines that the coefficient update period has not arrived (step S 3103 ; No), the processing of hereinafter described step S 3107  is executed without modification. However, it is assumed that the coefficient determiner  3425  determines that the coefficient update period has arrived (step S 3103 ; Yes). In this case the processing of steps S 3014  and S 3015  is executed. Thereafter, coefficient determination processing is executed (step S 3106 ). The content of this coefficient determination processing is the same as that of the coefficient determination processing described using  FIG. 17  in Embodiment 1. Next the processing of steps S 3107  and S 3108  is executed. The content of the processing of steps S 3107  and S 3108  is the same as that of the processing of steps S 105  and S 106  described using  FIG. 15  in Embodiment 1. 
     Then, the device setting updater  2419  determines whether the operation mode of the air conditioner  5041  ( 5042 ,  5043 ) is the automatic mode (step S 3109 ). When the device setting updater  2419  determines that the operation mode of the air conditioner  5041  ( 5042 ,  5043 ) is the manual mode (step S 3109 ; No), the processing of hereinafter described step S 3115  is executed. However, when the device setting updater  2419  determines that the operation mode of the air conditioner  5041  ( 5042 ,  5043 ) is the automatic mode (step S 3109 ; Yes), the device setting updater  2419  determines whether a predetermined update period of the device setting information of the air conditioner  5041  ( 5042 ,  5043 ) has arrived (step S 3110 ). When the device setting updater  2419  determines that the device setting information update period has not arrived (step S 3110 ; No), the processing of hereinafter described step S 5115  is executed. However, it is assumed that the device setting updater  2419  determines that the update period of the device setting information has arrived (step SS 3110 ; Yes). In this case, the series of processing from step S 3111  to step S 3114  is executed. Here, the content of the series of processing from step S 3111  to step S 3114  is the same as the series of processing from step S 3111  to step S 3114  described using  FIG. 44  in Embodiment 5. Then, as illustrated in  FIG. 64 , the operation receiver  413  determines whether an upload operation for uploading the coefficient information to the cloud server  5002  is received (step S 5115 ). When the operation receiver  413  determines that the upload operation is not received (step S 5115 ; No), the processing of hereinafter described step S 5118  is executed. Meanwhile, when the operation receiver  413  determines that the upload operation is received (step S 5115 ; Yes), the coefficient information generator  5428  generates coefficient information including the weighting coefficient information stored in the neural network storage  5433 , and also generates coefficient attribute information corresponding to the coefficient information (step S 5116 ). Next, the coefficient sender  5429  sends the coefficient information and the coefficient attribute information that are generated to the cloud server  5002  (step S 5117 ). Then, the operation receiver  413  determines whether a download operation for downloading the coefficient information from the cloud server  5002  is received (step S 5118 ). When the operation receiver  413  determines that the download operation is not received (step S 5118 ; No), the processing of step S 5113  is executed again. Meanwhile, when the operation receiver  413  determines that the download operation is received (step S 5118 ; Yes), the coefficient acquirer  2423  sends coefficient request information to the cloud server  5002  (step S 5119 ) to acquire the coefficient information and the coefficient attribute information from the cloud server  5002  (step S 5120 ). The coefficient acquirer  2423  stores the weighting coefficient information included in the acquired coefficient information in the neural network storage  2436 . Then, the processing of step S 3103  is executed again. 
     Here, while referencing  FIG. 65 , a case is described in which the weighting coefficient of the neural network determined in the air conditioner  5041  ( 5042 ) is transmitted to the air conditioner  5043 . Note that, in  FIG. 65 , the processes that are the same as the processes described above using  FIGS. 61 and 62  are denoted with the same reference numerals as used in  FIGS. 61 and 62 . Firstly, it is assumed that the air conditioner  5041  ( 5042 ) determines that a predetermined update period of the weighting coefficient of the neural network has arrived. In this case, the series of processing from step S 61  to step S 66  is executed and, as a result, the weighting coefficient of the neural network of the air conditioner  5041  ( 5042 ) is determined. Thereafter, it is assumed that the air conditioner  5041  ( 5042 ) receives an operation for uploading the coefficient information to the cloud server  5002  (step S 1047 ). In this case, the air conditioner  5041  ( 5042 ) uses the information expressing the weighting coefficient information stored in the neural network storage  2436  to generate coefficient information and coefficient attribute information corresponding thereto (step S 1048 ). Then, the coefficient information and the coefficient attribute information that are generated are sent from the air conditioner  5041  ( 5042 ) to the cloud server  5002  (step S 1049 ). Meanwhile, when the cloud server  5002  receives the coefficient information and the coefficient attribute information, the cloud server  5002  associates the received coefficient information and coefficient attribute information with the device identification information identifying the air conditioner  5041  ( 5042 ), and stores the associated information in the neural network storage  5233  (step S 1050 ). 
     Then, it is assumed that the air conditioner  5043  is newly installed in the house H, for example and, thereafter, receives an operation for downloading the coefficient information from the cloud server  5002  (step S 1051 ). In this case, coefficient request information is sent from the air conditioner  5043  to the cloud server  5002  (step S 1052 ). In one example, this coefficient request information includes the device identification information of the air conditioner  5041  ( 5042 ). Meanwhile, when the cloud server  5002  receives the coefficient request information, the cloud server  5002  identifies the coefficient information associated with the device identification information included in the received coefficient request information (step S 1053 ). Next, the identified coefficient information and the coefficient attribute information corresponding thereto are sent from the cloud server  5002  to the air conditioner  5043  (step S 1054 ). Meanwhile, when the air conditioner  5043  receives the coefficient information and the coefficient attribute information, the air conditioner  5043  stores the weighting coefficient information included in the received coefficient information in the neural network storage  2436  of the air conditioner  5043  (step S 1054 ). Thus, it is possible to set the weighting coefficient set in the neural network used by the air conditioner  5041  ( 5042 ) in the neural network used by the air conditioner  5043 . 
     In the device setting processing described using  FIGS. 63 and 64 , the air conditioner  5041  ( 5042 ,  5043 ) may upload the history information of the cloud server  5002 , download the history information from the cloud server  5002 , and the like. In this case, it is sufficient that the air conditioner  5041  ( 5042 ,  5043 ) includes a history information generator that generates history information including the operation history information and the environment history information stored in the history information storage  434 , and history attribute information corresponding to the history information, a history information sender that sends the history information and the history attribute information, and a device side history information acquirer that is a second history information acquirer that acquires the history information and the history attribute information of another air conditioner from the cloud server  5002 . Moreover, when the history information acquirer of the cloud server  5002  acquires the history information and the history attribute information sent from the air conditioners  5041 ,  5042 ,  5043 , it is sufficient that the cloud server  5002  functions as a cloud side history information acquirer that is a first history information acquirer that associates the history information and the history attribute information that are acquired with the device identification information of the air conditioners  5041 ,  5042 ,  5043 , and stores the associated information in the history information storage  231 . 
     In the air conditioner  5041  ( 5042 ,  5043 ), after step S 3114  described using  FIG. 63 , as illustrated in  FIG. 66 , the operation receiver  413  determines whether an upload operation for uploading the history information to the cloud server  5002  is received (step S 5121 ). When the operation receiver  413  determines that the upload operation is not received (step S 5121 ; No), the processing of hereinafter described step S 5124  is executed. Meanwhile, when the operation receiver  413  determines that the upload operation is received (step S 5121 ; Yes), the history information generator generates history information including the operation history information and the environment information stored in the history information storage  5434 , and also generates history attribute information corresponding to the history information (step S 5122 ). Next, the history information sender sends the history information and the history attribute information that are generated to the cloud server  5002  (step S 5123 ). At this time, the cloud side history information acquirer associates the history information and the history attribute information acquired from the air conditioner  5041  ( 5042 ,  5043 ) with the device identification information of the air conditioner  5041  ( 5042 ,  5043 ), and stores the associated information in the history information storage  231 . Then, the operation receiver  413  determines whether a download operation for downloading the history information from the cloud server  5002  is received (step S 5124 ). When the operation receiver  413  determines that the download operation is not received (step S 5124 ; No), the processing of step S 3113  is executed again. Meanwhile, when the operation receiver  413  determines that the download operation is received (step S 5124 ; Yes), the device side history information acquirer sends history request information to the cloud server  5002  (step S 5125 ) to acquire the history information and the history attribute information from the cloud server  5002  (step S 5126 ). The history information acquirer stores, in the history information storage  434 , the operation history information, the environment history information, and the user information included in the acquired history information. Then, the processing of step S 3103  is executed again. 
     Here, while referencing  FIG. 67 , a case is described in which the operation history information and the environment history information accumulated in the air conditioner  5041  ( 5042 ) are transmitted to the air conditioner  5043 . Note that, in  FIG. 67 , the processes that are the same as the processes described above using  FIGS. 61 and 62  are denoted with the same reference numerals as used in  FIGS. 61 and 62 . Firstly, when the air conditioner  5041  ( 5042 ) determines that the predetermined update period of the weighting coefficient of the neural network has arrived, the series of processing from step S 61  to step S 66  is executed and, as a result, the weighting coefficient of the neural network of the air conditioner  5041  ( 5042 ) is determined. Thereafter, it is assumed that the air conditioner  5041  ( 5042 ) receives an operation for uploading the history information to the cloud server  5002  (step S 1201 ). In this case, the air conditioner  5041  ( 5042 ) generates history information including the operation history information and the environment information stored in the history information storage  5434 , and also generates history attribute information corresponding to the history information (step S 1202 ). Then, the history information and the history attribute information that are generated are sent from the air conditioner  5041  ( 5042 ) to the cloud server  5002  (step S 1203 ). Meanwhile, when the cloud server  5002  receives the history information and the history attribute information, the cloud server  5002  associates the received history information and history attribute information with the device identification information identifying the air conditioner  5041  ( 5042 ), and stores the associated information in the history information storage  231 . 
     Then, it is assumed that the air conditioner  5043  is newly installed in the house H, for example and, thereafter, receives an operation for downloading the history information from the cloud server  5002  (step S 1205 ). In this case, history request information is sent from the air conditioner  5043  to the cloud server  5002  (step S 1206 ). In one example, this history request information includes the device identification information of the air conditioner  5041  ( 5042 ). Meanwhile, when the cloud server  5002  receives the history request information, the cloud server  5002  identifies the history information associated with the device identification information included in the received history request information (step S 1207 ). Next, the identified history information and the history attribute information corresponding thereto are sent from the cloud server  5002  to the air conditioner  5043  (step S 1208 ). Meanwhile, when the air conditioner  5043  receives the history information and the history attribute information, the air conditioner  5043  stores the operation history information and the environment information included in the received history information in the history information storage  434  of the air conditioner  5043  (step S 1209 ). Thus, it is possible to store the operation history information and the environment history information accumulated in the air conditioner  5041  ( 5042 ) in the history information storage  434  of the air conditioner  5043 . As a result, in the air conditioner  5043 , the coefficient determiner  5420  can determine the weighting coefficient of the neural network using the operation history information and the environment history information acquired from the air conditioner  5041  ( 5042 ). 
     As described above, with the control system according to the present embodiment, the weighting coefficient of the neural network of the air conditioner  5041 ,  5042 ,  5043  is downloaded from the cloud server  5002  and updated and, as a result, the control system according to the present embodiment can flexibly adapt to changes of the use method of the user of the air conditioners  5041 ,  5042 ,  5043 , particularly, changes in the installation environment of the air conditioners  5041 ,  5042 ,  5043  caused by moving of the user, changes in the family structure of the user, and the like. Additionally, when introducing a new air conditioner  5041 ,  5042 ,  5043 , information expressing the weighting coefficient of the neural network, which is uploaded in advance to the cloud server  5002 , is downloaded. As a result, the operation tendencies when in automatic operation of the air conditioners  5041 ,  5042 ,  5043  used to-date can be passed on to the new air conditioner  5041 ,  5042 ,  5043 . 
     Furthermore, with the control system according to the present embodiment, coefficient information corresponding to each user can be downloaded and used by uploading, in advance to the cloud server  5002 , weighting coefficient information of the neural network corresponding to a plurality of different users. As a result, even in cases in which the number of users increases substantially, the air conditioners  5041 ,  5042 ,  5043  can be caused to automatically operate in accordance with operation tendencies suited to each user. 
     Embodiments of the present disclosure are described above, but the present disclosure is not limited to the configurations described in the embodiments. For example, as illustrated in  FIG. 68 , a configuration is possible in which the control system includes a storage server  9008  that manages neural network related information (hereinafter referred to as “NN related information”) that includes history information, coefficient information, and the like related to the neural network used by the air conditioner  3004 . Note that, in  FIG. 68 , the air conditioner  3004  is the same as the air conditioner  3004  described in Embodiment 5. Additionally, in  FIG. 68 , the constituents that are the same as in Embodiment 1 are denoted with the same reference numerals as used in  FIG. 1 . In the present modified example, an air conditioner  9004  that has the same configuration as the air conditioner  3004  is installed in another house H 2  that differs from a house H 1 . The storage server  9008  is capable of communicating with a server  9002  via an external network NT 1 . 
     The hardware configuration of the cloud server  9002  is the same as the hardware configuration of the cloud server  2  of Embodiment 1 illustrated in  FIG. 10 . With the cloud server  9002 , the CPU reads out a program stored in an auxiliary storage to a main storage and executes the program to function as a history information acquirer  3211 , a weather record acquirer  3212 , a coefficient setter  213 , a neural network calculator  214 , a coefficient determiner  215 , a coefficient information generator  3218 , a coefficient sender  2219 , a neural network related information generator (hereinafter referred to as “NN related information generator”)  9218 , a neural network related information sender (hereinafter referred to as “NN related information sender”)  9219 , and a neural network related information acquirer (hereinafter referred to as “NN related information acquirer”)  9220 , as illustrated in  FIG. 69 . Note that, in  FIG. 69 , the constituents that are the same as in Embodiment 5 are denoted with the same reference numerals as used in  FIG. 42 . Additionally, the auxiliary storage includes a history information storage  231 , a weather information storage  232 , and an initial coefficient storage  3233 . Note that the CPU, the main storage, and the auxiliary storage are the same as the CPU  201 , the main storage  202 , and the auxiliary storage  203  illustrated in  FIG. 10 . 
     The NN related information generator  9218  acquires the history information from the air conditioner  3004  and generates, on the basis of user information included in the acquired history information, use situation information expressing a use situation of the air conditioner  3004  Moreover, the NN related information generator  9218  acquires operation history information and environment history information from the history information storage  231 , and generates the NN related information that includes the operation history information and the environment history information that are acquired, and the generated information expressing the use situation. The NN related information sender  9219  sends the generated NN related information to the storage server  9008 . The NN related information acquirer  9220  acquires the NN related information from the storage server  9008  by sending, to the storage server  9008 , NN related information request information requesting, to the storage server  9008 , sending of the NN related information. The NN related information request information includes the use situation information expressing the use situations of the air conditioner  9004  in the house H 1 . 
     The hardware configuration of the storage server  9008  is the same as the hardware configuration of the cloud server  2  of Embodiment 1 illustrated in  FIG. 10 . With the storage server  9008 , the CPU reads out a program stored in an auxiliary storage to a main storage and executes the program to function as an NN related information acquirer  9801 , a neural network related information identifier (hereinafter referred to as “NN related information identifier)  9802 , and an NN related information sender  9803 , as illustrated in  FIG. 70 . Additionally, the auxiliary storage includes an NN related information storage  931  that stores the NN related information acquired from the cloud server  9002 . Note that the CPU, the main storage, and the auxiliary storage are the same as the CPU  201 , the main storage  202 , and the auxiliary storage  203  illustrated in  FIG. 10 . In one example, as illustrated in  FIG. 71 , the NN related information storage  931  associates the use situation information, the coefficient information, the operation history information, the environment history information, and the like included in the NN related information with neural network identification information (hereinafter referred to as “NN identification information) that identifies the NN related information, and stores the associated information. 
     The NN related information acquirer  9801  acquires the NN related information sent from the cloud server  9002 , imparts identification information to the acquired NN related information, and stores the resulting NN related information in the NN related information storage  931 . When the NN related information identifier  9802  acquires the NN related information request information sent from the cloud server  9002 , the NN related information identifier  9802  extracts the use situation information from the acquired NN related information request information. Then, the NN related information identifier  9802  identifies, from among the NN related information stored in the NN related information storage  931 , NN related information for which the content of the use situation information thereof is similar to the content of the extracted use situation information. The NN related information sender  9803  sends the NN related information identified by the NN related information identifier  9802  to the cloud server  9002 . 
     Next, the operations of the control system according to the present modified example are described while referencing  FIG. 72 . Here, a case is described in which the air conditioner  9004  is newly installed in the house H 2 . Firstly, it is assumed that the cloud server  9002  determines that a predetermined NN related information generation period has arrived. In this case, coefficient history request information requesting, to the air conditioner  3004 , sending of coefficient information and history information is sent from the cloud server  9002  to the air conditioner  3004  (step S 1151 ). Meanwhile, when the air conditioner  3004  acquires the coefficient history request information, the air conditioner  3004  generates the coefficient information and the history information (step S 1152 ). Next, the coefficient information and the history information that are generated are sent from the air conditioner  3004  to the cloud server  9002  (step S 1153 ). Meanwhile, when the cloud server  9002  acquires the coefficient information and the history information, the cloud server  9002  generates, on the basis of the user information included in the acquired history information, use situation information expressing the use situation of the air conditioner  3004 . Additionally, the cloud server  9002  stores the operation history information and the environment history information included in the history information in the history information storage  231 . Moreover, the cloud server  9002  acquires the operation history information and the environment history information from the history information storage  231 , and generates the NN related information that includes the operation history information and the environment history information that are acquired, and the generated information expressing the use situation (step S 1154 ). Next, the generated NN related information is sent from the cloud server  9002  to the storage server  9008  (step S 1155 ). Meanwhile, when the storage server  9008  acquires the NN related information, the storage server  9008  imparts identification information to the acquired NN related information, and stores the resulting NN related information in the NN related information storage  931 . 
     Thereafter, the air conditioner  9004  is newly installed in the house H 2 , and coefficient request information requesting, to the cloud server  9002 , the initial coefficient of the neural network is sent from the air conditioner  9004  to the cloud server  9002  (step S 1157 ). Next, when the cloud server  9002  acquires the coefficient request information, the NN related information described above is sent from the cloud server  9002  to the storage server  9008  (step S 1158 ). Meanwhile, when the storage server  9008  acquires the NN related information request information, the storage server  9008  extracts the use situation information from the acquired NN related information request information. Then, the storage server  9008  identifies, from among the NN related information stored in the NN related information storage  931 , NN related information for which the content of the use situation information thereof is similar to the content of the extracted use situation information (step S 1159 ). 
     Next, the NN related information identified by the storage server  9008  is sent from the storage server  9008  to the cloud server  9002  (step S 1160 ). Meanwhile, when the cloud server  9002  acquires the NN related information, the cloud server  9002  extracts the coefficient information from the acquired NN related information (step S 1161 ). Thereafter, the extracted coefficient information is sent from the cloud server  9002  to the air conditioner  9004  (step S 1162 ). Thus, the air conditioner  9004  can acquire the information expressing the weighting coefficient that is stored in the neural network storage  2436  of the air conditioner  3004 , and store the acquired information expressing the weighting coefficient in the neural network storage of the air conditioner  9004 . 
     Additionally, as illustrated in  FIG. 73 , for example, a configuration is possible in which a terminal device  11009  is a device for displaying an image GA 2  on a display  11009   a . Here, the image GA 2  includes a photograph image GA 21  of inside the house in which the air conditioner  3004  is installed, and NN identification information ID  11001  imparted to the neural network used by the air conditioner  3004 . 
     In this case, as illustrated in  FIG. 74 , for example, firstly, the series of processing from step S 1152  to step S 1156  is executed and, as a result, the storage server  9008  stores, in the NN related information storage  931 , the NN related information corresponding to the neural network used by the air conditioner  3004 . Note that, in  FIG. 74 , the processes that are the same as the processes described using  FIG. 72  are denoted with the same reference numerals. Then, as illustrated in  FIG. 74 , for example, it is assumed that the terminal device  11009  displays the image GA 2  including the photograph image GA 21  and the NN identification information ID  11001  on the display  11009   a  (step S 1176 ). Here, it is assumed that the user of the terminal device  11009  performs, on the terminal device  11009 , a coefficient setting operation for setting a weighting coefficient, that is the same as the weighting coefficient set in the neural network used by the air conditioner  3004 , in the neural network used by the air conditioner  9004 . In this coefficient setting operation, the user inputs the NN identification information ID  11001  from a predetermined operation screen, for example. Upon such input, the terminal device  11009  receives the coefficient setting operation performed by the user (step S 1177 ). Next, coefficient request information including the NN identification information ID  11001  is sent from the terminal device  11009  to the cloud server  9002  (step S 1178 ). 
     Then, when the cloud server  9002  acquires the coefficient request information, NN related information request information including the NN identification information ID  11001  is sent from the cloud server  9002  to the storage server  9008  (step S 1179 ). Meanwhile, when the storage server  9008  acquires the NN related information request information, the storage server  9008  extracts the NN identification information ID  11001  from the acquired NN related information request information. Then, the storage server  9008  identifies, from among the NN related information stored in the NN related information storage  931 , the NN related information to which the NN identification information ID  11001  is imparted (step S 1180 ). 
     Thereafter, the NN related information identified by the storage server  9008  is sent from the storage server  9008  to the cloud server  9002  (step S 1181 ). Meanwhile, when the cloud server  9002  acquires the NN related information, the cloud server  9002  extracts the coefficient information from the acquired NN related information (step S 1182 ). Thereafter, the extracted coefficient information is sent from the cloud server  9002  to the air conditioner  9004  (step S 1183 ). 
     In Embodiment 3, an example is described in which the history information is directly sent from the air conditioner  2004  to the cloud server  2002 , and the coefficient information is directly sent from the cloud server  2002  to the air conditioner  2004 . However, the sending method of the history information and the coefficient information in Embodiment 2 is not limited thereto. For example, a configuration is possible in which the history information is sent from the air conditioner  2004  to the cloud server  2002 , relayed through a terminal device (not illustrated in the drawings) that has a so-called tethering function, and the coefficient information is sent from the cloud server  2002  to the air conditioner  2004 , relayed through the terminal device. In Embodiment 5, an example is described in which the coefficient information and the weather record information are directly sent from the cloud server  3002  to the air conditioner  3004 . However, the sending method of the coefficient information and the weather record information in Embodiment 3 is not limited thereto. For example, a configuration is possible in which the coefficient information and the weather record information are sent from the cloud server  3002  to the air conditioner  3004 , relayed through a terminal device. Here, a mobile terminal such as a smartphone or the like, for example, can be used as the terminal device. 
     According to the present configuration, even when the air conditioner  2004 ,  3004  is not directly connected to the network, it is possible to send the history information from the air conditioner  2004  to the cloud server  2002 , and to send the coefficient information and the weather record information from the cloud server  2002 ,  3002  to the air conditioner  2004 ,  3004 . 
     In Embodiment 2, a configuration is described in which the cloud server  15002  uses the weather information acquired from the weather server  3  to generate the schedule information. However, the present disclosure is not limited thereto, and a configuration is possible in which, for example, the cloud server  15002  calculates the device setting parameter without using the weather information to generate the schedule information. In such a case, for example, as illustrated in  FIG. 75 , it is sufficient that the cloud server  15002  has a configuration that does not include the weather information acquirer  212  and the weather information storage  232 . 
     In Embodiment 3, a configuration is described in which the air conditioner  2004  uses the weather information acquired from the weather server  3  to calculate the device setting parameter. However, the present disclosure is not limited thereto, and a configuration is possible in which, for example, the air conditioner  2004  calculates the device setting parameter without using the weather information. In such a case, for example, as illustrated in  FIG. 76 , it is sufficient that the air conditioner  2004  has a configuration that does not include the weather information acquirer  2422  and the weather information storage  2437 . Additionally, as illustrated in  FIG. 77 , a configuration is possible in which the cloud server  2002  does not include the weather information acquirer  212  and the weather information storage  232 . 
     In Embodiment 5, a configuration is described in which the air conditioner  3004  uses the weather information acquired from the weather server  3  to calculate the device setting parameter. However, the present disclosure is not limited thereto, and a configuration is possible in which, for example, the air conditioner  3004  calculates the device setting parameter without using the weather information. In such a case, for example, as illustrated in  FIG. 78 , it is sufficient that the air conditioner  3004  has a configuration that does not include the weather information acquirer  2422  and the weather information storage  2437 . Additionally, as illustrated in  FIG. 79 , a configuration is possible in which the cloud server  2002  does not include the weather record acquirer  3212  and the weather information storage  232 . 
     In the various embodiments, a configuration is possible in which the user identifier  421  identifies which category, of a predetermined plurality of body types, a body type of the user of the air conditioner  4  belongs to. 
     Any method may be used to provide the program to a computer. For example, the program may be uploaded to a bulletin board system (BBS) of a communication line, and distributed to the computer via the communication line. Then, the computer starts up the program and, under the control of the operating system (OS), executes the program in the same manner as other applications. As a result, the computer functions as the air conditioner  4 ,  2004 ,  3004 ,  4004 ,  5041 ,  5042 ,  5043 ,  9004 ,  15004 ,  16004 ,  17004 , and the cloud server  2 ,  2002 ,  3002 ,  4002 ,  5002 ,  9002 ,  15002 ,  16002 ,  17002  that execute the processings described above. 
     The foregoing describes some example embodiments for explanatory purposes. Although the foregoing discussion has presented specific embodiments, persons skilled in the art will recognize that changes may be made in form and detail without departing from the broader spirit and scope of the invention. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. This detailed description, therefore, is not to be taken in a limiting sense, and the scope of the invention is defined only by the included claims, along with the full range of equivalents to which such claims are entitled. 
     INDUSTRIAL APPLICABILITY 
     The present disclosure is suitable for automatically controlling the operations of a home appliance installed in a house.