Patent Description:
A demand control process has been known in the art to control power supplied to equipment (e.g., air-conditioner) based on target power in order to reduce the contracted power. Check technology has also been advanced to check the adequacy of target power that is selected when performing the demand control process.

Unfortunately, the conventional check technology is not so advanced as to derive proper target power upon finding that the target power is inadequate.

Document <CIT> discloses obtaining an average power consumption of electrical apparatus in a set time period, and obtaining a spare power of the average power consumption with respect to a target power in the set time period that is based on the set power, for the purpose of updating a demand value.

The claimed invention provides a demand control system according to claim <NUM>.

The present invention can provide a demand control system comprising a target power calculation apparatus for deriving a target power that is to be set when performing a demand control process.

The dependent claims relate to special embodiments.

In the following, embodiments will be described with reference to the accompanying drawings. In the specification and drawings, elements having substantially the same functions or configurations are referred to by the same numerals, and a duplicate description thereof will be omitted.

The system configuration of a demand control system will be described first. <FIG> is a first drawing illustrating an example of the system configuration of a demand control system. As illustrated in <FIG>, a demand control system <NUM>, which is installed in a building <NUM> such as an office building, includes a demand control object <NUM>, a demand control apparatus <NUM>, and a target power calculation apparatus <NUM>.

The demand control object <NUM> includes devices for which supplied power is controlled. The demand control apparatus <NUM> controls power supplied to each device such that the target power is not exceeded. The device <NUM>, device <NUM>,. , device n illustrated in <FIG> are air-conditioners installed in each room in the building <NUM>, for example.

The devices included in the demand control object <NUM> operates based on demand control data (e.g., suspend instruction for suspending the supply of power, resume instruction for resuming the supply of power, etc.) sent from the demand control apparatus <NUM>.

The amount of power consumed by each device included in the demand control object <NUM> is transmitted as demand pulses to the demand control apparatus <NUM>. Further, device information about each device of the demand control object <NUM> and various types of information such as environmental information, device information, and the like detected by each device of the demand control object <NUM> during the demand control process are transmitted to the demand control apparatus <NUM>.

The demand control apparatus <NUM> has a demand control program installed therein, which is executed to cause the demand control apparatus <NUM> to function as a demand controller <NUM>.

and the like (which will hereinafter be referred to as "history information") to the target power calculation apparatus <NUM>.

The demand controller <NUM> obtains a target power that is newly calculated by the target power calculation apparatus <NUM> responding to the history information transmitted to the target power calculation apparatus <NUM>, followed by setting the target power in the memory in the demand control apparatus <NUM>. The demand controller <NUM> performs a demand control process based on the target power setting.

The target power calculation apparatus <NUM> has a target power calculation program installed therein, which is executed to cause the target power calculation apparatus <NUM> to function as a learning unit <NUM>, an input unit <NUM>, and an inference unit <NUM>.

The learning unit <NUM>, which is an example of an obtainment unit, obtains the history information transmitted from the demand controller <NUM>. The learning unit <NUM> is also an example of a learning unit, and performs machine learning with respect to a model for calculating target power based on the obtained history information. With this arrangement, the learning unit <NUM> generates a trained model for calculating target power.

The input unit <NUM> is an example of an input unit. The input unit <NUM> receives information entered by the administrator of the demand control apparatus <NUM> when the learning unit <NUM> performs machine learning with respect to a model, for example. The administrator of the demand control apparatus <NUM> enters knowledge obtained by referring to the history information. The entering of information into the input unit <NUM> may be performed either manually or automatically. Automatic entering of information into the input unit <NUM> may include a method of entering information by generating data in which information is arrayed in a predetermined format, and then feeding the data to the input unit <NUM>.

The inference unit <NUM> inputs, into the trained model, information newly transmitted from the demand controller <NUM> to execute the trained model, thereby calculating an adequate target power. Further, the inference unit <NUM> transmits the calculated target power to the demand controller <NUM>.

In the following, specific examples of the demand control object <NUM> will be described. <FIG> is a drawing illustrating specific examples of demand control objects. <FIG> illustrates air-conditioners <NUM>, <NUM>, and <NUM> as examples of the demand control object <NUM>.

The air-conditioner <NUM> functions to remove contamination from the air and automatically adjust temperature and humidity inside a room <NUM> on a predetermined floor <NUM> of the building <NUM>. The air-conditioner <NUM> detects in-room information <NUM> (e.g., the number of occupants, room temperature, room humidity, and the like in the room <NUM>) while operating. The air-conditioner <NUM> also detects a length of operating time while operating.

Similarly, the air-conditioner <NUM> functions to remove contamination from the air and automatically adjust temperature and humidity inside a room <NUM> on the predetermined floor <NUM> of the building <NUM>. The air-conditioner <NUM> detects in-room information <NUM> (e.g., the number of occupants, room temperature, room humidity, and the like in the room <NUM>) while operating. The air-conditioner <NUM> also detects a length of operating time while operating.

The air-conditioners <NUM>, <NUM>, and <NUM> further detect open-air information <NUM> (e.g., weather, insolation, outdoor temperature, outdoor humidity, and the like outside the building <NUM>) while operating.

Although power lines for supplying power to the air-conditioners <NUM>, <NUM>, and <NUM> are not illustrated in <FIG>, these power lines are provided with respective power meters attached thereto. The power meters are configured to transmit demand pulses to the demand control apparatus <NUM>.

Control lines for transmitting demand control data to the air-conditioners <NUM>, <NUM>, and <NUM> are not illustrated in <FIG>. The air-conditioners <NUM>, <NUM>, and <NUM> are configured to receive demand control data transmitted from the demand control apparatus <NUM> through such control lines.

Communication lines for transmitting environmental information (in-room information and open-air information) detected by the air-conditioners <NUM>, <NUM>, and <NUM> to the demand control apparatus <NUM> are not illustrated in <FIG>. The air-conditioners <NUM>, <NUM>, and <NUM> are configured to transmit environmental information to the demand control apparatus <NUM> through such communication lines. The air-conditioners <NUM>, <NUM>, and <NUM> are further configured to transmit device information about the devices (inclusive of information about a length of operating time) to the demand control apparatus <NUM> through such control lines.

In the following, the hardware configurations of apparatuses constituting the demand control system <NUM> will be described. Since the demand control apparatus <NUM> and the target power calculation apparatus <NUM> have substantially the same hardware configuration, only the hardware configuration of the target power calculation apparatus <NUM> will be described.

<FIG> is a drawing illustrating an example of the hardware configuration of the target power calculation apparatus. As illustrated in <FIG>, the target power calculation apparatus <NUM> includes a CPU (central processing unit) <NUM>, a ROM (read only memory) <NUM>, and a RAM (random access memory) <NUM>. The CPU <NUM>, the ROM <NUM>, and the RAM <NUM> together constitute a computer. The target power calculation apparatus <NUM> further includes an auxiliary storage device <NUM>, a display device <NUM>, an operating device <NUM>, an I/F (interface) device <NUM>, and a drive device <NUM>. The individual hardware parts of the target power calculation apparatus <NUM> are connected to one another through a bus <NUM>.

The CPU <NUM> is an arithmetic device which executes various types of program (e.g., target power calculation programs) installed in the auxiliary storage device <NUM>. The ROM <NUM> is a nonvolatile memory. The ROM <NUM>, which functions as a main memory device, stores various types of program, data, and the like necessary for the CPU <NUM> to execute the various types of program installed in the auxiliary storage device <NUM>. Specifically, the ROM <NUM> stores boot programs and the like such as BIOS (basic input/output system) and EFI (extensible firmware interface).

The RAM <NUM> is a volatile memory such as a DRAM (dynamic random access memory) and an SRAM (static random access memory). The RAM <NUM>, which functions as a main memory device, provides a work area to which the various types of program installed in the auxiliary storage device <NUM> are loaded when executed by the CPU <NUM>.

The auxiliary storage device <NUM> stores various types of program, and stores information used when the various types of program are executed.

The display device <NUM> is a display apparatus that displays information (e.g., history information) obtained by the target power calculation apparatus <NUM>. The operating device <NUM> is an operating apparatus that is used by the administrator of the demand control apparatus <NUM> or the users of each room, for example, to perform various types of operation with respect to the target power calculation apparatus <NUM>. The I/F device <NUM> is a communication device for communicating with the demand control apparatus <NUM>.

The drive device <NUM> is a device to which a recording medium <NUM> is set. Here, the recording medium <NUM> includes a medium for optically, electrically, or magnetically recording information, such as a CD-ROM, a flexible disk, a magneto-optical disk, or the like. The recording medium <NUM> may also include a semiconductor memory or the like that electrically records information, such as a ROM, a flash memory, or the like.

The various types of programs to be installed in the auxiliary storage device <NUM> are installed by the drive device <NUM> reading the various types of programs recorded in the recording medium <NUM> upon the recording medium <NUM> being supplied and set in the drive device <NUM>, for example. Alternatively, the various types of program to be installed in the auxiliary storage device <NUM> may be installed upon being downloaded from a network (not shown).

In the following, the functional configuration of the demand controller <NUM> will be described. <FIG> is a drawing illustrating an example of the functional configuration of the demand controller. As illustrated in <FIG>, the demand controller <NUM> includes a pulse counter <NUM>, a remaining time computation unit <NUM>, a power prediction computation unit <NUM>, a control-need detection unit <NUM>, a load control unit <NUM>, and an analysis unit <NUM>.

The pulse counter <NUM> accumulates demand pulses to calculate track-record information about the power used by the demand control object <NUM>. The remaining time computation unit <NUM> subtracts a lapsed time from the demand period (e.g., <NUM> minutes) to calculate a remaining time length. The power prediction computation unit <NUM> calculates predicted power based on the track-record information about the power used and the remaining time length.

The control-need detection unit <NUM> checks whether control is needed, based on the target power retrieved from a target power storage <NUM> and set in the memory as well as the predicted power calculated by the power prediction computation unit <NUM>. The control-need detection unit <NUM> supplies a suspension level to the load control unit <NUM> upon detecting the need for control.

The suspension level is a value indicative of the extent to which the supply of power is suspended in response to a suspend instruction with respect to the devices included in the demand control object <NUM>. A high suspension level state refers to the state in which a suspend instruction is issued to a large number of devices, or the state in which the functions of these devices are restricted. A low suspension level state refers to the state in which a suspend instruction is issued to only a small number of devices, or the state in which the functions of the devices are not restricted.

The target power calculation apparatus <NUM> updates the target power stored in the target power storage <NUM>. The control-need detection unit <NUM> stores the retrieved target power, the supplied suspension level, the track-record information about the consumed power, and the like in a history information storage <NUM> such that they are associated with the various types of information transmitted from the demand control object <NUM>.

The load control unit <NUM> transmits demand control data to the demand control object <NUM>. The demand control data includes a suspend instruction or a resume instruction responsive to the suspension level supplied from the control-need detection unit <NUM>.

The analysis unit <NUM> analyzes the suspension level stored in the history information storage <NUM>, and stores the analysis results in the history information storage <NUM> such that the results are associated with the suspension level.

The target power calculation apparatus <NUM> retrieves the history information stored in the history information storage <NUM>.

In the following, a specific example of the demand control process by the demand controller <NUM> will be described. <FIG> is a drawing illustrating a specific example of the demand control process.

An example of the track-record information about power consumed during the demand period is illustrated in 5a of <FIG>. In 5a of <FIG>, the horizontal axis represents time, and the vertical axis represents power. "T" in the horizontal axis indicates the time until which the outputting of demand control data should be prohibited, and "<NUM> minutes" indicates the end of the demand period" "α" in the vertical axis indicates the target power.

A graph <NUM> illustrated in 5a of <FIG> represents the track-record information about power consumed from immediately after the start of the demand period to the present time. The power prediction computation unit <NUM> calculates: predetermined pulse weight x accumulated pulse value x (<NUM>/demand period [minutes]), thereby calculating the track record information about power consumed up to the present time. The power prediction computation unit <NUM> further calculates: (the track-record information about power consumed up to the present time) + (a change in power during the preceding Δt minutes) / (sampling time interval Δt) x (remaining time), thereby calculating predicted power.

The control-need detection unit <NUM> calculates: target power / demand period x lapsed time, thereby calculating a current target power at each time point.

An example of the occurrence of a suspension level during the demand period is illustrated in 5b of <FIG>. In 5b of <FIG>, the horizontal axis represents time, and the vertical axis represents the suspension level.

The control-need detection unit <NUM> increases the suspension level by an increment of one when:.

In such a case, the load control unit <NUM> outputs a suspend instruction as demand control data.

Further, the control-need detection unit <NUM> decreases the suspension level by a decrement of one when:.

In such a case, the load control unit <NUM> outputs a resume instruction as demand control data.

The control-need detection unit <NUM> maintains the suspension level as it is when neither of the above-noted conditions are satisfied. In such a case, the load control unit <NUM> does not output demand control data (i.e., suspend instruction or resume instruction).

A graph <NUM> illustrated in 5b of <FIG> represents the occurrences of suspension levels from immediately after the start of the demand period to the present time. In the example illustrated in the graph <NUM>, the following conditions:.

are satisfied immediately before the present time, so that the control-need detection unit <NUM> increases the suspension level by an increment of one.

In the following, specific examples of analytic results obtained by the analysis unit <NUM> will be described. Analytic results by the analysis unit <NUM> are obtained when the analysis unit <NUM> analyzes the occurrences of suspension levels stored in the history information storage <NUM>. <FIG> is a drawing illustrating specific examples of the analytic results.

Cumulative time lengths of respective suspension levels are illustrated in 6a-<NUM> of <FIG> of <FIG>, as calculated by the analysis unit <NUM> analyzing the status of occurrences of suspension levels that are stored in the history information storage <NUM> by the control-need detection unit <NUM>. It may be noted that 6a-<NUM> of <FIG> of <FIG> show cumulative time lengths on a suspension-level-specific basis in respective cases in which different target powers are set during the demand control process concerning the same demand control object <NUM>. As shown in these cases, changing the setting of target power causes significant changes to be made in the cumulative time lengths of respective suspension levels.

Proportions of cumulative time lengths of respective suspension levels are illustrated in 6b-<NUM> of <FIG> of <FIG>, as calculated by the analysis unit <NUM> analyzing the status of occurrences of suspension levels that are stored in the history information storage <NUM> by the control-need detection unit <NUM>. It may be noted that 6b-<NUM> of <FIG> of <FIG> show the proportions of cumulative time lengths of individual suspension levels when respective different target powers are set during the demand control process concerning the same demand control object <NUM>. As shown in these cases, changing the setting of target power causes significant changes to be made in the proportions of cumulative time lengths of individual suspension levels.

In the following, specific examples of history information stored in the history information storage <NUM> will be described. <FIG> is a drawing illustrating specific examples of history information.

As illustrated in <FIG>, history information <NUM> includes current target power information <NUM>. The example illustrated in <FIG> indicates that the current target power is "α".

The history information <NUM> further includes device information <NUM> about each device included in the demand control object <NUM>. The device information <NUM> includes information items that are "identification information", "device type", "operating time length", "item attribute", "thermal load", and "capacity".

The history information <NUM> further includes track-record information 730_1 about consumed power separately stored for each demand period. The history information <NUM> further includes suspension levels 730_2a separately stored for each demand period, and also includes analytic results 730_2b of the suspension levels 730_2a. Information including both the suspension levels 730_2a and the analytic results 730_2b will hereinafter be referred to as "control information".

The history information <NUM> further includes environmental information 730_3 separately stored for each demand period. Information including the device information <NUM>, the track-record information 730_1 about consumed power, and the environmental information 730_3 will hereinafter be referred to as "detected information".

In the following, a description will be given of the functional configuration of the target power calculation apparatus <NUM> according to the first embodiment. It may be noted that in what follows, the learning phase and the inference phase will be described separately.

The functional configuration in the learning phase will be described first. <FIG> is a drawing illustrating an example of the functions of the target power calculation apparatus that are implemented during the learning phase according to the first embodiment. As illustrated in <FIG>, the target power calculation apparatus <NUM> functions as the learning unit <NUM> and the input unit <NUM> in the learning phase.

The learning unit <NUM> and the input unit <NUM> retrieves, from among the history information <NUM> stored in the history information storage <NUM> of the demand controller <NUM>, the control information concerning a period in which a demand control process is performed based on a predetermined target power (i.e., the period of a demand control process using a predetermined target power). For example, the learning unit <NUM> and the input unit <NUM> retrieves the suspension levels 730_2a and the analytic results 730_2b as the control information.

The input unit <NUM> displays the retrieved control information to an administrator <NUM> of the demand control apparatus <NUM>, for example. The administrator <NUM> determines whether the target power is adequate based on past knowledge. The input unit <NUM> receives entered information indicative of whether the target power is adequate as determined by the administrator <NUM>.

The input unit <NUM> also displays the retrieved control information to users <NUM> of the rooms <NUM>, <NUM>, and <NUM>, for example. The users <NUM> determine a comfort index (inclusive of the presence or absence of comfort) based on sensations. The input unit <NUM> receives entered information indicative of the comfort indexes regarding the rooms <NUM>, <NUM>, and <NUM> as determined by the users <NUM>.

The learning unit <NUM> includes an adequacy determination model <NUM> and a compare-&-modify unit <NUM>. The learning unit <NUM> inputs the retrieved control information and the entered and received information indicative of comfort indexes into the adequacy determination model <NUM> to run the adequacy determination model <NUM>. With this arrangement, the adequacy determination model <NUM> outputs information indicative of whether the target power is adequate.

The information that indicates whether the target power is adequate and that is output from the adequacy determination model <NUM> is supplied to the compare-&-modify unit <NUM>. The compare-&-modify unit <NUM> compares:.

The compare-&-modify unit <NUM> modifies the model parameters of the adequacy determination model <NUM> based on the results of comparison.

As described above, the learning unit <NUM> performs machine learning with respect to the adequacy determination model <NUM> for finding the relationship between:.

With this arrangement, the learning unit <NUM> generates a trained adequacy determination model for determining whether the target power is adequate.

The example illustrated in <FIG> is directed to a case in which the control information and the information indicative of comfort indexes are input into the adequacy determination model <NUM>. Alternatively, only the control information or part of the control information may be input into the adequacy determination model <NUM>.

or with respect to the adequacy determination model <NUM> for finding the relationship between:.

With this arrangement, the learning unit <NUM> generates a trained adequacy determination model for determining whether the target power is adequate (it may be noted that part of the analytic results 730_2b refers to either the cumulative time lengths of suspension levels or the proportions of cumulative time lengths of suspension levels).

The functional configuration in the inference phase will be described next. <FIG> is a drawing illustrating an example of the functions of the target power calculation apparatus that are implemented during the inference phase according to the first embodiment. As illustrated in <FIG>, the target power calculation apparatus <NUM> functions as the input unit <NUM> and the inference unit <NUM> in the inference phase.

The inference unit <NUM> includes a trained adequacy determination model <NUM> generated by the learning unit <NUM>, and includes a target power modifying unit <NUM>. The learning unit <NUM> retrieves, from among history information <NUM> stored in the history information storage <NUM> of the demand controller <NUM>, the control information concerning a period in which a demand control process is performed based on a current target power (i.e., the period of a demand control process using a current target power). The control information retrieved by the inference unit <NUM> is different from the control information retrieved by the learning unit <NUM> when the learning unit <NUM> performed machine learning.

The inference unit <NUM> obtains via the input unit <NUM> information indicative of comfort indexes regarding the rooms <NUM>, <NUM>, and <NUM> as determined by the users <NUM> during the period of a demand control process executed on the current target power.

The inference unit <NUM> inputs the retrieved control information and the obtained information indicative of comfort indexes into the trained adequacy determination model <NUM> to run the trained adequacy determination model <NUM>. With this arrangement, the trained adequacy determination model <NUM> infers information indicative of whether the target power is adequate.

The target power modifying unit <NUM> corrects the target power based on the information indicative of whether the target power is adequate, which is inferred by the trained adequacy determination model <NUM>. Specifically, when the trained adequacy determination model <NUM> has inferred that the target power is adequate, the target power modifying unit <NUM> transmits the current target power to the demand control apparatus <NUM> without correcting the current target power.

When the trained adequacy determination model <NUM> has inferred that the target power is inadequate, the target power modifying unit <NUM> corrects the current target power based on a predetermined correction amount, and then transmits the corrected target power to the demand control apparatus <NUM>.

In the manner described above, the inference unit <NUM> corrects the target power by inferring whether the target power is adequate based on the information indicative of comfort indexes and the control information used during the period in which a demand control process is performed based on the current target power, thereby deriving an adequate target power.

The description given above has been directed to a case in which the inference unit <NUM> inputs control information and information indicative of comfort indexes into the trained adequacy determination model <NUM>. Alternatively, when the trained adequacy determination model <NUM> has been generated based only on control information (or part of the control information), the inference unit <NUM> inputs only control information (or part of the control information) into the trained adequacy determination model <NUM>.

The description given above has been directed to a case in which the trained adequacy determination model <NUM> and the target power modifying unit <NUM> are provided in the inference unit <NUM>. Alternatively, the trained adequacy determination model <NUM> and the target power modifying unit <NUM> may be provided separately from each other. For example, the trained adequacy determination model <NUM> may be provided in the inference unit, while the target power modifying unit <NUM> may be provided in a correction unit (not shown) outside the inference unit.

In the following, a description will be given of a target power calculation process performed by the target power calculation apparatus <NUM> according to the first embodiment. <FIG> is a flowchart illustrating the flow of a target power calculation process according to the first embodiment.

In step S1001, the learning unit <NUM> obtains control information used during the period of a demand control process during which a demand control process is performed based on a predetermined target power.

In step S1002, the input unit <NUM> receives, as entered by the users and the administrator, information indicative of one or more comfort indexes and information indicative of whether the target power is adequate during the period of a demand control process during which a demand control process is performed based on the predetermined target power.

In step S1003, the learning unit <NUM> inputs the control information into the adequacy determination model <NUM> so as to run the adequacy determination model <NUM>. The learning unit <NUM> performs machine learning with respect to the adequacy determination model <NUM> such that the information indicative of whether the target power is adequate as output from the adequacy determination model <NUM> approaches the information (supervisory data) indicative of whether the target power is adequate as input via the input unit <NUM>.

In step S1004, the inference unit <NUM> obtains control information used during the period of a predetermined demand control process.

In step S1005, the input unit <NUM> receives entered information indicative of one or more comfort indexes during the period of a predetermined demand control process.

In step S1006, the inference unit <NUM> inputs the obtained control information and information indicative of comfort indexes into the trained adequacy determination model <NUM> to run the trained adequacy determination model <NUM>. With this arrangement, the inference unit <NUM> infers information indicative of whether the target power is adequate.

In step S1007, the inference unit <NUM> corrects the target power based on the inferred outcome, and transmits the corrected target power to the demand control apparatus <NUM>. With this arrangement, the corrected target power is set in the memory in the demand control apparatus <NUM>.

In step S1008, the inference unit <NUM> determines whether to terminate the target power calculation process. Upon determining in step S1008 that the target power calculation process is to be continued (NO in step S1008), the procedure returns to step S1004.

Upon determining in step S1008 that the target power calculation process is to be terminated (YES in step S1008), the target power calculation process is terminated.

<FIG> has been directed to a case in which the learning unit <NUM> performs batch learning that modifies model parameters upon inputting control information into the adequacy determination model <NUM> in a batch. Alternatively, the learning unit <NUM> may perform sequential learning that modifies model parameters upon inputting control information into the adequacy determination model <NUM> a predetermined number of bytes at a time.

As is understood from the descriptions provided heretofore, the target power calculation apparatus <NUM> of the first embodiment is configured:.

With this arrangement, the target power calculation apparatus <NUM> of the first embodiment enables accurate determination of whether the target power is adequate, thereby successfully deriving a proper target power.

The first embodiment as described above thus provides a target power calculation apparatus, a target power calculation method, and a target power calculation program for deriving a target power that is to be set when performing a demand control process.

The first embodiment described above has been directed to a case in which, for the purpose of calculating target power, machine learning is performed to learn the relationship between control information (or part thereof) and information indicative of whether the target power is adequate. The second embodiment is differently configured such that, for the purpose of calculating target power, machine learning is performed to learn the relationship between target power and track-record information about power consumption. In the following, the second embodiment will be described with a focus on the differences from the first embodiment.

A description will first be given of the functional configuration of the target power calculation apparatus <NUM> according to the second embodiment. In what follows also, the learning phase and the inference phase will be described separately.

<FIG> is a drawing illustrating an example of the functions of the target power calculation apparatus that are implemented during the learning phase according to the second embodiment. As illustrated in <FIG>, the target power calculation apparatus <NUM> functions as the learning unit <NUM> and the input unit <NUM> in the learning phase.

The learning unit <NUM> and the input unit <NUM> retrieves, from among the history information <NUM> stored in the history information storage <NUM> of the demand controller <NUM>, the detected information concerning a period in which a demand control process is performed based on a predetermined target power (i.e., the period of a demand control process using a predetermined target power). The learning unit <NUM> and the input unit <NUM> retrieves, as the detected information, the device information <NUM>, the track-record information 730_1 about consumed power, and the environmental information 730_3.

The input unit <NUM> displays the retrieved detected information to an administrator <NUM> of the demand control apparatus <NUM>, for example. The administrator <NUM> determines, based on past knowledge, the right target power suited to be set for the given period of a demand control process. The input unit <NUM> receives an entered right target power suited to be set for the given period of a demand control process as determined by the administrator <NUM>. The input unit <NUM> also displays the retrieved control information to users <NUM> of the rooms <NUM>, <NUM>, and <NUM>, for example. The users <NUM> determine a comfort index based on sensations. The input unit <NUM> receives the entered information indicative of the comfort indexes regarding the rooms <NUM>, <NUM>, and <NUM> as determined by the users <NUM>.

The learning unit <NUM> includes a target power model <NUM> and a compare-&-modify unit <NUM>. The learning unit <NUM> inputs the retrieved control information and the entered and received information indicative of comfort indexes into the target power model <NUM> to run the target power model <NUM>. With this arrangement, the target power model <NUM> outputs a target power.

The compare-&-modify unit <NUM> compares the target power output from the target power model <NUM> with the target power (supervisory data) that is received by the input unit <NUM>. The compare-&-modify unit <NUM> modifies the model parameters of the target power model <NUM> based on the results of comparison.

As described above, the learning unit <NUM> performs machine learning with respect to the target power model <NUM> for finding the relationship between:.

With this arrangement, the learning unit <NUM> generates a trained target power model.

The example illustrated in <FIG> is such that the learning unit <NUM> inputs detected information and information indicative of comfort indexes to the target power model <NUM>. Alternatively, the learning unit <NUM> may input only the detected information or only a part of the detected information into the target power model <NUM>.

The functional configuration in the inference phase will be described next. <FIG> is a drawing illustrating an example of the functions of the target power calculation apparatus that are implemented during the inference phase according to the second embodiment. As illustrated in <FIG>, the target power calculation apparatus <NUM> functions as the input unit <NUM> and the inference unit <NUM> in the inference phase.

The inference unit <NUM> includes a trained target power model <NUM> generated by the learning unit <NUM>. The learning unit <NUM> retrieves, from among history information <NUM> stored in the history information storage <NUM> of the demand controller <NUM>, the detected information concerning a period in which a demand control process is performed based on a current target power (i.e., the period of a demand control process using a current target power). The detected information retrieved by the inference unit <NUM> is different from the detected information retrieved by the learning unit <NUM> when the learning unit <NUM> performed machine learning.

The inference unit <NUM> inputs the retrieved detected information and the obtained information indicative of comfort indexes into the trained target power model <NUM> to run the trained target power model <NUM>. With this arrangement, the trained target power model <NUM> infers a target power.

Further, the inference unit <NUM> transmits the target power inferred by the trained target power model <NUM> to the demand control apparatus <NUM>.

In the manner described above, the inference unit <NUM> infers the target power based on the information indicative of comfort indexes and the detected information observed during the period in which a demand control process is performed based on the current target power, thereby deriving an adequate target power.

The description given above has been directed to a case in which the inference unit <NUM> inputs detected information and information indicative of comfort indexes into the trained target power model <NUM>. Alternatively, when the trained target power model <NUM> has been generated based only on detected information (or part of the detected information), the inference unit <NUM> inputs only detected information (or part of the detected information) into the trained target power model <NUM>.

In the following, a description will be given of a target power calculation process performed by the target power calculation apparatus <NUM> according to the second embodiment. <FIG> is a flowchart illustrating the flow of a target power calculation process according to the second embodiment.

In step S1301, the learning unit <NUM> obtains detected information observed during the period of a demand control process executed on a predetermined target power.

In step S1302, the input unit <NUM> receives entered information indicative of one or more comfort indexes and the right target power suited to be set for the period of a predetermined demand control process executed on a predetermined target power.

In step S1303, the learning unit <NUM> inputs the detected information into the target power model <NUM> so as to run the target power model <NUM>. The learning unit <NUM> performs machine learning with respect to the target power model <NUM> such that the target power output from the target power model <NUM> approaches the target power (supervisory data) input via the input unit <NUM>.

In step S1304, the inference unit <NUM> obtains detected information observed during the period of a predetermined demand control process.

In step S1305, the input unit <NUM> receives entered information indicative of one or more comfort indexes during the period of a predetermined demand control process.

In step S1306, the inference unit <NUM> inputs the obtained detected information and the information indicative of one or more comfort indexes into the trained target power model <NUM> to run the trained target power model <NUM>. With this arrangement, the inference unit <NUM> infers a target power.

In step S1307, the inference unit <NUM> transmits the inferred target power to the demand control apparatus <NUM>. With this arrangement, the inferred target power is set in the memory in the demand control apparatus <NUM>.

As is understood from the descriptions provided heretofore, the target power calculation apparatus <NUM> of the second embodiment is configured: - to obtain track-record information about power consumed during the period of a demand control process based on the set target power, and to learn a right target power suited to be set for the period of a demand control process, according to the track-record information about consumed power.

With this arrangement, the target power calculation apparatus <NUM> of the second embodiment can derive a proper target power.

The second embodiment as described above thus provides a target power calculation apparatus, a target power calculation method, and a target power calculation program for deriving a target power that is to be set when performing a demand control process.

[Third Embodiment] - not being part of the present invention.

The first and second embodiments described above have been directed to a case in which supervisory data is used when performing machine learning with respect to a model for calculating a target power. The third embodiment, which is differently directed to reinforced learning, will be described. In the following, the third embodiment will be described with a focus on the differences from the first and second embodiments.

The system configuration of a demand control system will be described first. <FIG> is a second drawing illustrating an example of the system configuration of a demand control system. What differs from <FIG> is a target power calculation apparatus <NUM>.

The target power calculation apparatus <NUM> has a target power calculation program installed therein, which is executed to cause the target power calculation apparatus <NUM> to function as a reward determination unit <NUM>, a reinforcement learning unit <NUM>, and an input unit <NUM>.

The reward determination unit <NUM> determines rewards that the reinforcement learning unit <NUM> uses when performing reinforcement learning.

The reinforcement learning unit <NUM> performs reinforcement learning with respect to a model for calculating target power based on history information transmitted from the demand controller <NUM>. Further, the reinforcement learning unit <NUM> transmits the target power obtained by performing reinforcement learning to the demand controller <NUM>.

The input unit <NUM> receives information indicative of one or more comfort indexes regarding one or more rooms as entered by the users of the rooms, and sends the information to the reward determination unit <NUM>. This arrangement allows the rewards used in reinforcement learning performed by the reinforcement learning unit <NUM> to reflect the information indicative of one or more comfort indexes regarding the one or more rooms.

In the following, a description will be given with respect to the detail of the functional configuration of the target power calculation apparatus <NUM> according to the third embodiment. <FIG> is a drawing illustrating an example of the functional configuration of the target power calculation apparatus <NUM> according to the third embodiment.

As was described above, the target power calculation apparatus <NUM> functions as the reward determination unit <NUM>, the reinforcement learning unit <NUM>, and the input unit <NUM>.

The reward determination unit <NUM> and the input unit <NUM> retrieve, from among history information <NUM> stored in the history information storage <NUM> of the demand controller <NUM>, the control information concerning a period in which a demand control process is performed based on a current target power (i.e., the period of a demand control process using a current target power). For example, the reward determination unit <NUM> and the input unit <NUM> retrieve the suspension levels 730_2a and the analytic results 730_2b as the control information.

The input unit <NUM> displays the retrieved control information to the users <NUM> of the rooms <NUM>, <NUM>, and <NUM>, for example. The users <NUM> determine a comfort index based on sensations. The input unit <NUM> receives the entered information indicative of the comfort indexes regarding the rooms <NUM>, <NUM>, and <NUM> as determined by the users <NUM>.

The reward determination unit <NUM> obtains the information indicative of comfort indexes entered into and received by the input unit <NUM>. Further, the reward determination unit <NUM> functions as a calculation unit to calculate a reward concerning the current target power based on the retrieved control information and the obtained information indicative of comfort indexes.

The reinforcement learning unit <NUM> retrieves detected information concerning the period in which a demand control process is performed based on the current target power (i.e., the period of a demand control process using the current target power).

The reinforcement learning unit <NUM>, which is an example of the learning unit, includes a target power model <NUM>. The reinforcement learning unit <NUM> modifies the model parameters of the target power model <NUM> such that the rewards calculated by the reward determination unit <NUM> becomes a maximum. Further, the reinforcement learning unit <NUM> inputs the retrieved detected information into the target power model <NUM> whose model parameters have been modified, thereby causing the target power model <NUM> to operate. With this arrangement, the target power model <NUM> outputs a target power. The reinforcement learning unit <NUM> transmits the target power output by the target power model <NUM> to the demand control apparatus <NUM>.

In the manner described above, the reinforcement learning unit <NUM> performs reinforcement learning with respect to the target power model <NUM> such that the rewards calculated based on the information indicative of comfort indexes and the control information used during the execution of a demand control process based on the current target power become a maximum. With this arrangement, the reinforcement learning unit <NUM> can derive a proper target power.

The description given above has been directed to a case in which control information and information indicative of comfort indexes are input into the reward determination unit <NUM>. Alternatively, the reward determination unit <NUM> may be configured to receive control information (or part of the control information) only, or receive part of the control information and information indicative of comfort indexes only.

With this arrangement, the reward determination unit <NUM> can calculate rewards based on:.

The description given above has been directed to a case in which the reinforcement learning unit <NUM> inputs all the detected information into the target power model <NUM>. Alternatively, the reinforcement learning unit <NUM> may input only a part of the detected information into the target power model <NUM>.

As described above, the reinforcement learning unit <NUM> performs machine learning with respect to the target power model <NUM> for finding the relationship between:.

In the following, a description will be given of a target power calculation process performed by the target power calculation apparatus <NUM> according to the third embodiment. <FIG> is a flowchart illustrating the flow of a target power calculation process according to the third embodiment.

In step S1601, the reward determination unit <NUM> obtains control information used during the period of a demand control process executed on a current predetermined target power.

In step S1602, the input unit <NUM> receives entered information indicative of one or more comfort indexes during the period of a demand control process executed on the current target power.

In step S1603, the reward determination unit <NUM> calculates a reward concerning the target power based on the control information and the information indicative of one or more comfort indexes.

In step S1604, the reward determination unit <NUM> checks whether the calculated reward is greater than or equal to a predetermined threshold. Upon determining in step S1604 that the calculated reward is less than the predetermined threshold (NO in step S1604), the procedure proceeds to step S1605.

In step S1605, the reinforcement learning unit <NUM> performs machine learning with respect to the target power model <NUM> such that the calculated reward becomes a maximum.

In step S1606, the reinforcement learning unit <NUM> obtains detected information observed during the period of a demand control process executed on the current target power.

In step S1607, the reinforcement learning unit <NUM> inputs the obtained detected information into the target power model <NUM> so as to run the target power model <NUM>. With this arrangement, the reinforcement learning unit <NUM> outputs a target power.

In step S1608, the reinforcement learning unit <NUM> transmits the output target power to the demand control apparatus <NUM>. With this arrangement, the output target power is set in the memory in the demand control apparatus <NUM>. The procedure thereafter returns to step S1601.

Upon determining in step S1604 that the calculated reward is greater than or equal to the predetermined threshold (YES in step S1604), the target power calculation process comes to an end.

As is understood from the descriptions provided heretofore, the target power calculation apparatus <NUM> of the third embodiment is configured:.

With this arrangement, the target power calculation apparatus <NUM> of the third embodiment can derive a proper target power.

The third embodiment as described above thus provides a target power calculation apparatus, a target power calculation method, and a target power calculation program for deriving a target power that is to be set when performing a demand control process.

The third embodiment has been described with respect to a case in which the reward determination unit calculates a reward concerning a target power based on control information and information indicative of one or more comfort indexes. However, the method of calculating rewards is not limited to this. For example, the trained adequacy determination model generated in the first embodiment may be utilized to infer whether the target power is adequate based on control information and information indicative of one or more comfort indexes. A reward concerning the target power may then be calculated based on the result of inference.

The description of the embodiments provided heretofore makes no mention of the details of the models used in machine learning (i.e., the adequacy determination model and the target power model). In this regard, any types of model may be utilized as the model used in machine learning. Specifically, any types of model, such as an NN (neural network) model, a random forest model, an SVM (support vector machine) model, or the like, may be utilized.

The description of the first and second embodiments provided heretofore makes no mention of the details of how to modify model parameters when needed based on the result of comparison by the compare-&-modify unit. It may be noted that the method of modifying model parameters performed by the compare-&-modify unit should depend on the types of model.

The description of the third embodiment provided heretofore makes no mention of the details of how the reward determination unit calculates rewards. It may be noted that the reward determination unit may calculate rewards by use of any method.

The first embodiment has been described with respect to the configuration which infers whether the target power is adequate (i.e., whether it is adequate or inadequate). The configuration may further be made to infer whether a high target power causes inadequacy or a low target power causes inadequacy when the inference indicates that the target power is inadequate, for example, Alternatively, the configuration may be made to infer the degree of inadequacy. With this arrangement, the target power modifying unit can correct the target power by use of a proper correction amount.

The description of the first embodiment provided heretofore makes no mention of the conditions for terminating the target power calculation process. In this regard, it does not matter what conditions for termination are used. For example, the target power calculation process may be configured to come to an end when the trained adequacy determination model infers that the target power is adequate continuously for a certain length of time.

The second embodiment has been described with respect to a case in which the administrator <NUM> of the demand control apparatus <NUM> determines supervisory data (i.e., the right target power suited to be set) based on detected information, and the administrator <NUM> inputs the determined supervisory data into the input unit. Nonetheless, the method of inputting supervisory data (i.e., right target power suited to be set) is not limited to this. For example, the input unit may receive as supervisory data the target power that has not been modified for a certain length of time as indicated in the target-power-setting history data obtained by accumulating the history of target power settings.

The embodiments have been described with respect to a case in which the target power calculation apparatus is installed inside the building <NUM>. Notwithstanding this, the target power calculation apparatus may be situated outside the building <NUM>. In this case, the target power calculation apparatus may function as part of the demand control system for a plurality of buildings. In the above-described embodiments, the target power calculation apparatus is configured as a separate unit from the demand control apparatus <NUM>, but may alternatively be configured as a single unit together with the demand control apparatus <NUM>.

Although a description has been given of the embodiments, it may be understood that various modifications may be made to the configurations and details thereof, without departing from the spirit and scope of claims.

Claim 1:
Demand control system (<NUM>) comprising:
- a demand control object (<NUM>) including devices;
- a demand control apparatus (<NUM>) configured to control power supplied to each device such that a target power is not exceeded; and
- a target power calculation apparatus (<NUM>); characterized in that
the target power calculation apparatus (<NUM>) comprises:
a learning unit (<NUM>) comprising an adequacy determination model (<NUM>) and a compare-and-modify unit (<NUM>) and configured to perform machine learning with respect to the adequacy determination model (<NUM>) that is configured to receive, as inputs, control information and information indicative of a comfort index determined by a user, and to output information indicative of whether a target power set in a first period is adequate, the control information and the information indicative of the comfort index being obtained upon performing a demand control process in the first period based on the set target power, the compare-and-modify unit (<NUM>) configured to compare the information indicative of whether the target power is adequate as output from the adequacy determination model (<NUM>) and information indicative of whether the target power is adequate as entered into and received by an input unit (<NUM>);
an inference unit (<NUM>) including a trained adequacy determination model (<NUM>) generated by the learning unit (<NUM>),the inference unit (<NUM>) configured to input the retrieved control information and the obtained information indicative of the comfort index obtained upon performing a demand control process in a second period into the trained adequacy determination model (<NUM>)to run the trained adequacy determination model (<NUM>) , the trained adequacy determination model (<NUM>) configured to infer information indicative of whether a target power set in the second period is adequate, ; and
a correction unit configured to correct the target power set in the second period based on a predetermined correction amount upon the inference unit (<NUM>) inferring that there is inadequacy.