AIR CONDITIONING LOAD LEARNING APPARATUS AND AIR CONDITIONING LOAD PREDICTION APPARATUS

An air conditioning load learning apparatus includes an actual load acquisition unit, a first information acquisition unit, and a learning unit. The actual load acquisition unit acquires an actual air conditioning load in a target space inside a target building. The first information acquisition unit acquires first information about an operation of the target building. The learning unit generates a learning model using at least the first information as an explanatory variable and a value regarding the actual air conditioning load as an objective variable. An air conditioning load prediction apparatus includes the air conditioning load learning apparatus.

TECHNICAL FIELD

The present invention relates to an air conditioning load learning apparatus and an air conditioning load prediction apparatus.

BACKGROUND ART

To implement optimum facility design, optimum control design, and the like, at the time of updating an air conditioner, there is a need to provide a highly accurate air conditioning load prediction technique in consideration of the operation of a proposed building. In PTL 1 (Japanese Patent No. 5943255), the difference between the actual air conditioning load measurement value and the external thermal load (excluding the internal heat generation) obtained by the physical model is determined so that the internal heat generation related to the operation of the building is estimated and used for air conditioning load prediction,

SUMMARY OF INVENTION

Technical Problem

In order to predict the air conditioning load by using the method disclosed in PTL 1, there is a need to input the information about the operation of the building so as to reflect the effect of the operation of the building on the internal heat generation, or the like. However, the information about the operation of the building is typically hard to obtain and is often incomplete even if it is obtained. Therefore, the method disclosed in PTL 1 has an issue that it is difficult to grasp the effect of the operation of the building on the air conditioning load.

Solution to Problem

An air conditioning load learning apparatus according to a first aspect includes an actual load acquisition unit, a first information acquisition unit, and a learning unit. The actual load acquisition unit acquires an actual air conditioning load that is an actual air conditioning load in a target space inside a target building. The first information acquisition unit acquires first information. The first information is information about an operation of the target building. The learning unit generates a learning model using at least the first information as an explanatory variable and using a value regarding the actual air conditioning load as an objective variable.

In the air conditioning load learning apparatus according to the first aspect, the learning unit generates the learning model using at least the first information as an explanatory variable and using the value regarding the actual air conditioning load as an objective variable. Therefore, the air conditioning load learning apparatus can associate the first information with the value regarding the actual air conditioning load to learn the effect of the operation of the building on the internal heat generation, etc. As a result, the air conditioning load learning apparatus can finally grasp the effect of the operation of the building on the air conditioning load.

An air conditioning load learning apparatus according to a second aspect is the air conditioning load learning apparatus according to the first aspect and further includes a prediction load acquisition unit. The prediction load acquisition unit acquires a prediction air conditioning load that is an air conditioning load in the target space predicted by a physical model from at least a thermal property of the target building. The learning unit generates the learning model using the first information as an explanatory variable and using a difference load, which is a difference between the actual air conditioning load and the prediction air conditioning load, as an objective variable.

With such a configuration, the air conditioning load learning apparatus according to the second aspect can associate the first information with the difference load (the value regarding the actual air conditioning load) to learn the effect of the operation of the building on the internal heat generation, etc. As a result, the air conditioning load learning apparatus can finally grasp the effect of the operation of the building on the air conditioning load.

An air conditioning load learning apparatus according to a third aspect is the air conditioning load learning apparatus according to the second aspect, wherein the actual air conditioning load is an air conditioning load per unit time.

With such a configuration, the air conditioning load learning apparatus according to the third aspect can learn the effect of the operation of the building on the air conditioning load in more detail.

An air conditioning load learning apparatus according to a fourth aspect is the air conditioning load learning apparatus according to the second aspect or the third aspect and further includes a second information acquisition unit. The second information acquisition unit acquires second information. The second information is at least one of an indoor humidity, an indoor temperature, an air conditioning operating time, a post air conditioning operation start elapsed time, an outside air temperature, an outside air humidity, and solar radiation. The learning unit generates the learning model further using the second information as an explanatory variable.

With such a configuration, the air conditioning load learning apparatus according to the fourth aspect can learn the effect of the operation of the building on the outside air introduction load, the heat storage load, the solar radiation load, etc. As a result, the air conditioning load

learning apparatus can finally grasp the effect of the operation of the building on the air conditioning load.

An air conditioning load learning apparatus according to a fifth aspect is the air conditioning load learning apparatus according to any one of the second aspect to the fourth aspect, wherein the learning unit generates the learning model further using the prediction air conditioning load as an explanatory variable.

With such a configuration, the air conditioning load learning apparatus according to the fifth aspect can generate the learning model having a higher prediction accuracy.

An air conditioning load prediction apparatus according to a sixth aspect includes a difference load prediction unit and a load prediction unit. The difference load prediction unit uses the learning model of the air conditioning load learning apparatus according to the second aspect or the third aspect to predict the difference load from the first information. The load prediction unit predicts an air conditioning load in the target space based on the predicted difference load and the prediction air conditioning load.

With such a configuration, the air conditioning load prediction apparatus according to the sixth aspect can use the learning model having learned the effect of the operation of the building on internal heat generation, and the like, to predict the air conditioning load in consideration of the effect of the operation of the building.

An air conditioning load prediction apparatus according to a seventh aspect is the air conditioning load prediction apparatus according to the sixth aspect, wherein the learning model is generated based on the first information, the actual air conditioning load, and the prediction air conditioning load in a short period. The difference load prediction unit predicts, from the first information in a long period that is a period longer than the short period, the difference load in the long period, The load prediction unit predicts an air conditioning load in the long period in the target space based on the predicted difference load in the long period and the prediction air conditioning load in the long period.

With such a configuration, the air conditioning load prediction apparatus according to the seventh aspect can use the first learning model having learned with the data in the short period to predict the air conditioning load in the long period.

An air conditioning load prediction apparatus according to an eighth aspect includes a difference load prediction unit and a load prediction unit. The difference load prediction unit uses the learning model of the air conditioning load learning apparatus according to the fifth aspect to predict the difference load from the first information and the second information or the prediction air conditioning load. The load prediction unit predicts an air conditioning load in the target space based on the predicted difference load and the prediction air conditioning load.

With such a configuration, the air conditioning load prediction apparatus according to the eighth aspect can use the learning model having learned the effect of the operation of the building on the internal heat generation, the outside air introduction load, the heat storage load, the solar radiation load, and the like, to predict the air conditioning load in consideration of the effect of the operation of the building.

An air conditioning load prediction apparatus according to a ninth aspect is the air conditioning load prediction apparatus according to the eighth aspect, wherein the learning model is generated based on the first information, the second information, the actual air conditioning load, and the prediction air conditioning load in a short period. The difference load prediction unit predicts, from the first information and the second information or the prediction air conditioning load in a long period that is a period longer than the short period, the difference load in the long period. The load prediction unit predicts an air conditioning load in the long period in the target space based on the predicted difference load in the long period and the prediction air conditioning load in the long period.

With such a configuration, the air conditioning load prediction apparatus according to the ninth aspect can use the learning model having learned with the data in the short period to predict the air conditioning load in the long period.

An air conditioning load learning apparatus according to a tenth aspect is the air conditioning load learning apparatus according to the first aspect and further includes a prediction load acquisition unit. The prediction load acquisition unit acquires a prediction air conditioning load that is an air conditioning load in the target space predicted by a physical model from at least a thermal property of the target building. The learning unit generates the learning model using the prediction air conditioning load and the first information as explanatory variables and using the actual air conditioning load as an objective variable.

With such a configuration, the air conditioning load learning apparatus according to the tenth aspect can associate the first information with the actual air conditioning load (the value regarding the actual air conditioning load) to learn the effect of the operation of the building on the internal heat generation, etc. As a result, the air conditioning load learning apparatus can finally grasp the effect of the operation of the building on the air conditioning load.

An air conditioning load learning apparatus according to an eleventh aspect is the air conditioning load learning apparatus according to the tenth aspect, wherein the actual air conditioning load is an air conditioning load per unit time.

With such a configuration, the air conditioning load learning apparatus according to the eleventh aspect can learn the effect of the operation of the building on the air conditioning load in more detail.

An air conditioning load learning apparatus according to a twelfth aspect is the air conditioning load learning apparatus according to the tenth aspect or the eleventh aspect and further includes a second information acquisition unit. The second information acquisition unit acquires second information. The second information is at least one of an indoor humidity, an indoor temperature, an air conditioning operating time, a post air conditioning operation start elapsed time, an outside air temperature, an outside air humidity, and solar radiation. The learning unit generates the learning model further using the second information as an explanatory variable.

With such a configuration, the air conditioning load teaming apparatus according to the twelfth aspect can learn the effect of the operation of the building on the outside air introduction load, the heat storage load, the solar radiation load, etc. As a result, the air conditioning load learning apparatus can finally grasp the effect of the operation of the building on the air conditioning load.

An air conditioning load prediction apparatus according to a thirteenth aspect includes a load prediction unit, The load prediction unit uses the learning model generated by the learning unit in the air conditioning load learning apparatus according to the tenth aspect or the eleventh aspect to predict an air conditioning load in the target space from the prediction air conditioning load and the first information.

With such a configuration, the air conditioning load prediction apparatus according to the thirteenth aspect can use the learning model having learned the effect of the operation of the building on internal heat generation, and the like, to predict the air conditioning load in consideration of the effect of the operation of the building.

An air conditioning load prediction apparatus according to a fourteenth aspect is the air conditioning load prediction apparatus according to the thirteenth aspect, wherein the learning model is generated based on the prediction air conditioning load, the first information, and the actual air conditioning load in a short period. Based on the prediction air conditioning load and the first information in a long period that is a period longer than the short period, the load prediction unit predicts an air conditioning load in the long period in the target space.

With such a configuration, the air conditioning load prediction apparatus according to the fourteenth aspect can use the learning model having learned with the data in the short period to predict the air conditioning load in the long period.

An air conditioning load prediction apparatus according to a fifteenth aspect includes a load prediction unit. The load prediction unit uses the learning model generated by the learning unit in the air conditioning load learning apparatus according to the twelfth aspect to predict an air conditioning load in the target space from the prediction air conditioning load, the first information, and the second information.

With such a configuration, the air conditioning load prediction apparatus according to the fifteenth aspect can use the learning model having learned the effect of the operation of the building on the internal heat generation, the outside air introduction load, the heat storage load, the solar radiation load, and the like, to predict the air conditioning load in consideration of the effect of the operation of the building.

An air conditioning load prediction apparatus according to a sixteenth aspect is the air conditioning load prediction apparatus according to the fifteenth aspect, wherein the learning model is generated based on the prediction air conditioning load, the first information, the second information, and the actual air conditioning load in a short period. Based on the prediction air conditioning load, the first information, and the second information in a long period that is a period longer than the short period, the load prediction unit predicts an air conditioning load in the long period in the target space.

With such a configuration, the air conditioning load prediction apparatus according to the sixteenth aspect can use the learning model having learned with the data in the short period to predict the air conditioning load in the long period.

An air conditioning load learning apparatus according to a seventeenth aspect is the air conditioning load learning apparatus according to the first aspect and further includes an input value acquisition unit. The input value acquisition unit acquires a first input value and a second input value. The first input value is an input value including at least a thermal property of the target building to a physical model, which outputs a prediction air conditioning load that is a predicted air conditioning load in the target space. The second input value is an input value calculated by inverse calculation of the physical model using the actual air conditioning load, The learning unit generates the learning model using the first information as an explanatory variable and using a difference input value, which is a difference between the first input value and the second input value, as an objective variable.

With such a configuration, the air conditioning load learning apparatus according to the seventeenth aspect can associate the first information with the difference input value (the value regarding the actual air conditioning load) to learn the effect of the operation of the building on the internal heat generation, etc. As a result, the air conditioning load learning apparatus can finally grasp the effect of the operation of the building on the air conditioning load.

An air conditioning load learning apparatus according to an eighteenth aspect is the air conditioning load learning apparatus according to the seventeenth aspect, wherein the learning unit generates the learning model further using the first input value as an explanatory variable.

With such a configuration, the air conditioning load learning apparatus according to the eighteenth aspect can generate the learning model having a higher prediction accuracy.

An air conditioning load prediction apparatus according to a nineteenth aspect includes a difference input value prediction unit and a prediction load acquisition unit. The difference input value prediction unit uses the learning model of the air conditioning load learning apparatus according to the seventeenth aspect or the eighteenth aspect to predict the difference input value from at least the first information. The prediction load acquisition unit acquires a prediction air conditioning load that is an air conditioning load in the target space predicted by the physical model. The prediction air conditioning load is predicted by the physical model using, as an input, a third input value. The third input value is obtained by correcting the first input value using the predicted difference input value.

With such a configuration, the air conditioning load prediction apparatus according to the nineteenth aspect can use the learning model having learned the effect of the operation of the building on internal heat generation, and the like, to predict the air conditioning load in consideration of the effect of the operation of the building.

An air conditioning load learning apparatus according to a twentieth aspect is the air conditioning load learning apparatus according to the first aspect and further includes a prediction load acquisition unit and a difference input value acquisition unit. The prediction load acquisition unit acquires a prediction air conditioning load that is an air conditioning load in the target space predicted by a physical model from at least a thermal property of the target building. The difference input value acquisition unit acquires a difference input value calculated by inverse calculation of the physical model using a difference load that is a difference between the actual air conditioning load and the prediction air conditioning load, The learning unit generates the learning model using the first information as an explanatory variable and using the difference input value as an objective variable.

With such a configuration, the air conditioning load learning apparatus according to the twentieth aspect can associate the first information with the difference input value (the value regarding the actual air conditioning load) to learn the effect of the operation of the building on the internal heat generation, etc. As a result, the air conditioning load learning apparatus can finally grasp the effect of the operation of the building on the air conditioning load.

An air conditioning load learning apparatus according to a twenty-first aspect is the air conditioning load learning apparatus according to the twentieth aspect, wherein the learning unit generates the learning model further using an input value to the physical model as an explanatory variable.

With such a configuration, the air conditioning load learning apparatus according to the twenty-first aspect can generate the learning model having a higher prediction accuracy.

An air conditioning load prediction apparatus according to a twenty-second aspect includes a difference input value prediction unit, a prediction difference load acquisition unit, and a load prediction unit. The difference input value prediction unit uses the learning model of the air conditioning load learning apparatus according to the twentieth aspect or the twenty-first aspect to predict the difference input value from at least the first information. The prediction difference load acquisition unit acquires the difference load predicted by the physical model using the predicted difference input value as an input. The load prediction unit predicts an air conditioning load in the target space based on the acquired difference load and the prediction air conditioning load.

With such a configuration, the air conditioning load prediction apparatus according to the twenty-second aspect can use the learning model having learned the effect of the operation of the building on internal heat generation, and the like, to predict the air conditioning load in consideration of the effect of the operation of the building.

An air conditioning load learning apparatus according to a twenty-third aspect is the air conditioning load learning apparatus according to the first aspect and further includes an input value acquisition unit. The input value acquisition unit acquires a first input value and a second input value. The first input value is an input value including at least a thermal property of the target building to a physical model, which outputs a prediction air conditioning load that is a. predicted air conditioning load in the target space. The second input value is an input value calculated by inverse calculation of the physical model using the actual air conditioning load. The learning unit generates the learning model using the first information and the first input value as explanatory variables and using the second input value as an objective variable.

With such a configuration, the air conditioning load learning apparatus according to the twenty-third aspect can associate the first information with the second input value (the value regarding the actual air conditioning load) to learn the effect of the operation of the building on the internal heat generation, etc. As a result, the air conditioning load learning apparatus can finally grasp the effect of the operation of the building on the air conditioning load.

An air conditioning load prediction apparatus according to a twenty-fourth aspect includes an input value prediction unit and a prediction load acquisition unit. The input value prediction unit uses the learning model of the air conditioning load learning apparatus according to the twenty-Third aspect to predict the second input value from the first information and the first input value. The prediction load acquisition unit acquires a prediction air conditioning load that is an air conditioning load in the target space predicted by the physical model using the predicted second input value as an input.

With such a configuration, the air conditioning load prediction apparatus according to the twenty-fourth aspect can use the learning model having learned the effect of the operation of the building on internal heat generation, and the like, to predict the air conditioning load in consideration of the effect of the operation of the building.

DESCRIPTION OF EMBODIMENTS

(1) Overall Configuration

An air conditioning load learning apparatus110learns an air conditioning load of an air conditioning system30installed in a target building10and generates a learning model LM1. Hereinafter, a learning process by the air conditioning load learning apparatus110is referred to as an air conditioning load learning process. An air conditioning load prediction apparatus120uses the learning model LM1generated by the air conditioning load learning apparatus110to predict the air conditioning load of the air conditioning system30installed in the target building10. Hereinafter, a prediction process by the air conditioning load prediction apparatus120is referred to as an air conditioning load prediction process. The target building10is, for example, an office building. The target space11of the air conditioning system30in the target building10is, for example, an office space.

During the air conditioning load learning process and the air conditioning load prediction process, the air conditioning load learning apparatus110and the air conditioning load prediction apparatus120use thermal load calculation results by a physical model calculation device70. The physical model calculation device70calculates the thermal load of the target building10based on a physical model PM. Hereinafter, a calculation process by the physical model calculation device70is referred to as a physical model calculation process.

FIG.1is a configuration diagram of the air conditioning load learning apparatus110and the air conditioning load prediction apparatus120. As illustrated inFIG.1, the air conditioning load learning apparatus110, the air conditioning load prediction apparatus120, the physical model calculation device70, the air conditioning system30, and an air conditioning database80are communicatively coupled via a network90. According to the present embodiment, the network90is the Internet. However, the network90is not limited to the Internet as long as the air conditioning load learning apparatus110, the air conditioning load prediction apparatus120, the physical model calculation device70, and the air conditioning system30are communicatively coupled. For example, when the air conditioning load learning apparatus110, the air conditioning load prediction apparatus120, and the physical model calculation device70are installed in the target building10, the network90may be a wired or wireless LAN, etc. Furthermore, the air conditioning load learning apparatus110, the air conditioning load prediction apparatus120, and the physical model calculation device70may be implemented as a single device, and in this case, the network90is an electronic circuit, etc. Moreover, the functions of the physical model calculation device70may be implemented as functional blocks of the air conditioning load learning apparatus110and the air conditioning load prediction apparatus120.

(2) Detailed Configuration

(2-1) Air Conditioning System

The air conditioning system30forms a vapor compression refrigeration cycle to air-condition the target space11. According to the present embodiment, the air conditioning system30is a multi-air conditioning system for buildings. However, the air conditioning system30is not limited thereto, and the air conditioning system30may be of any type.

FIG.2is a schematic configuration diagram of the air conditioning system30. As illustrated inFIGS.1and2, the air conditioning system30primarily includes an indoor unit40, an outdoor unit50, a total heat exchanger20, and a control unit60. Although the one indoor unit40, the one outdoor unit50, and the one total heat exchanger20are illustrated inFIG.2, this is not a limitation, and the air conditioning system30may include a plurality of the indoor units40, a plurality of the outdoor units50, and a plurality of the total heat exchangers20. In particular, according to the present embodiment, the air conditioning system30includes the plurality of indoor units40, and the second and subsequent indoor units40are not illustrated inFIG.2.

The indoor unit40and the outdoor unit50are coupled via a liquid refrigerant connection pipe31and a gas refrigerant connection pipe32to form a refrigerant circuit33. The refrigerant circuit33includes an indoor expansion valve41and an indoor heat exchanger42of the indoor unit40. Furthermore, the refrigerant circuit33includes a compressor51, a flow direction switching mechanism52, an outdoor heat exchanger53, and an outdoor expansion valve54of the outdoor unit50.

The air conditioning system30has, as primary operating modes for the air conditioning operation, a cooling operating mode for performing a cooling operation and a heating operating mode for performing a heating operation. The cooling operation is an operation for causing the outdoor heat exchanger53to function as a condenser of the refrigerant and causing the indoor heat exchanger42to function as an evaporator of the refrigerant to cool the air in the target space11where the indoor unit40is installed. The heating operation is an operation for causing the outdoor heat exchanger53to function as an evaporator of the refrigerant and causing the indoor heat exchanger42to function as a condenser of the refrigerant to heat the air in the target space11where the indoor unit40is installed.

(2-1-1) Indoor Unit

The indoor unit40is a unit installed in the target space11. For example, the indoor unit40is a ceiling-embedded unit. As illustrated inFIG.2, the indoor unit40is coupled to the outdoor unit50via the liquid refrigerant connection pipe31and the gas refrigerant connection pipe32. The indoor unit40includes an indoor-side refrigerant circuit33aforming part of the refrigerant circuit33.

The indoor unit40primarily includes the indoor expansion valve41, the indoor heat exchanger42, an indoor fan43, various sensors, and an indoor control unit44. Various sensors included in the indoor unit40will be described below.

(2-1-1-1) Indoor Expansion Valve The indoor expansion valve41is a mechanism that adjusts the pressure and the flow rate of the refrigerant flowing through the indoor-side refrigerant circuit33a.The indoor expansion valve41is provided in a refrigerant pipe that couples the liquid side of the indoor heat exchanger42and the liquid refrigerant connection pipe31. The indoor expansion valve41is, for example, an electronic expansion valve whose opening degree is adjustable.

(2-1-1-2) Indoor Heat Exchanger

In the indoor heat exchanger42, heat is exchanged between the refrigerant flowing through the indoor heat exchanger42and the air in the target space11. The indoor heat exchanger42is, for example, a fin-and-tube heat exchanger including a plurality of heat transfer tubes and fins.

One end of the indoor heat exchanger42is coupled to the liquid refrigerant connection pipe31via a refrigerant pipe. The other end of the indoor heat exchanger42is coupled to the gas refrigerant connection pipe32via a refrigerant pipe. During the cooling operation, the refrigerant flows into the indoor heat exchanger42from the liquid refrigerant connection pipe31side, and the indoor heat exchanger42functions as an evaporator of the refrigerant. During the heating operation, the refrigerant flows into the indoor heat exchanger42from the gas refrigerant connection pipe32side, and the indoor heat exchanger42functions as a condenser of the refrigerant.

(2-1-1-3) Indoor Fan

The indoor fan43is a fan that supplies the air to the indoor heat exchanger42. The indoor fan43is, for example, a cross-flow fan. The indoor fan43is driven by an indoor fan motor43a.The number of revolutions of the indoor fan motor43acan be controlled by an inverter.

As illustrated inFIG.2, the indoor unit40includes an indoor temperature sensor45aand an indoor humidity sensor45b.

The indoor temperature sensor45ais provided on an air intake side of a casing (not illustrated) of the indoor unit40. The indoor temperature sensor45adetects the temperature of the air in the target space Il that flows into the casing of the indoor unit40(the suction temperature of the indoor unit40).

The indoor humidity sensor45bis provided on the air intake side of the casing (not illustrated) of the indoor unit40. The indoor humidity sensor45bdetects the humidity of the air in the target space11that flows into the casing of the indoor unit40. According to the present embodiment, the indoor humidity sensor45hdetects a relative humidity. However, this is not a limitation, and the indoor humidity sensor45bmay detect an absolute humidity.

(2-1-1-5) Indoor Control Unit

The indoor control unit44controls the operation of each unit included in the indoor unit40. The indoor control unit44includes a control arithmetic device and a storage device. A processor such as a CPU or a GPU may be used as the control arithmetic device. The control arithmetic device reads a program stored in the storage device and performs predetermined arithmetic processing in accordance with the program. Further, the control arithmetic device can write an arithmetic result in the storage device and read information stored in the storage device in accordance with the program. Furthermore, the indoor control unit44includes a timer. According to the present embodiment, the timer is installed in the indoor control unit44, but may be installed in an outdoor control unit57described below.

As illustrated inFIG.2, the indoor control unit44is electrically connected to the indoor expansion valve41, the indoor fan43. the indoor temperature sensor45a,and the indoor humidity sensor45hto enable exchange of control signals and information.

The indoor control unit44is configured to enable reception of various signals transmitted from a remote controller46to operate the indoor unit40. The various signals transmitted from the remote controller46include signals for giving instructions to operate or stop the indoor unit40and signals regarding various settings. The signals regarding various settings include, for example, switching signals for the operating modes and signals regarding a setting temperature or a setting humidity for the cooling operation and the heating operation,

The indoor control unit44is coupled to the outdoor control unit57of the outdoor unit50and a total heat exchange control unit21of the total heat exchanger20via a transmission line61in a state where control signals, and the like, can be exchanged. Further, instead of being coupled via the physical transmission line61, the indoor control unit44, the outdoor control unit57, and the total heat exchange control unit21may be coupled wirelessly to enable communications. The indoor control unit44, the outdoor control unit57, and the total heat exchange control unit21cooperate with each other to function as the control unit60that controls the overall operation of the air conditioning system30. The control unit60will be described below.

(2-1-2) Outdoor Unit

The outdoor unit50is installed outside the target space11. The outdoor unit50is installed, for example, on a roof floor of the target building10, in which the air conditioning system30is provided, or is installed adjacent to the target building10. As illustrated inFIG.2, the outdoor unit50is coupled to the indoor units40via the liquid refrigerant connection pipe31and the gas refrigerant connection pipe32. The outdoor unit50includes an outdoor-side refrigerant circuit33bforming part of the refrigerant circuit33.

The outdoor unit50primarily includes the compressor51, the flow direction switching mechanism52, the outdoor heat exchanger53, the outdoor expansion valve54, an accumulator55, an outdoor fan56, various sensors, and the outdoor control unit57. Various sensors included in the outdoor unit50will be described below.

Further, the outdoor unit50includes an intake pipe34a,a discharge pipe34b,a first gas refrigerant pipe34c,a liquid refrigerant pipe34d,a second gas refrigerant pipe34e,a liquid-side shutoff valve35, and a gas-side shutoff valve36. The intake pipe34acouples the flow direction switching mechanism52and an intake side of the compressor51. The intake pipe34ais provided with the accumulator55. The discharge pipe34bcouples a discharge side of the compressor51and the flow direction switching mechanism52. The first gas refrigerant pipe34ccouples the flow direction switching mechanism52and a gas side of the outdoor heat exchanger53. The liquid refrigerant pipe34dcouples a liquid side of the outdoor heat exchanger53and the liquid refrigerant connection pipe31. The liquid refrigerant pipe34dis provided with the outdoor expansion valve54. The liquid-side shutoff valve35is provided in a coupling portion between the liquid refrigerant pipe34dand the liquid refrigerant connection pipe31, The second gas refrigerant pipe34ecouples the flow direction switching mechanism52and the gas refrigerant connection pipe32. The gas-side shutoff valve36is provided in a coupling portion between the second gas refrigerant pipe34eand the gas refrigerant connection pipe32.

As illustrated inFIG.2, the compressor51is a device that takes in the low-pressure refrigerant in the refrigeration cycle from the intake pipe34a,compresses the refrigerant with a compression mechanism (not illustrated), and discharges the compressed refrigerant to the discharge pipe34b.

The compressor51is, for example, a rotary type or scroll type volume compressor. The compression mechanism of the compressor51is driven by a compressor motor51a.When the compression mechanism is driven by the compressor motor51a,the refrigerant is compressed by the compression mechanism. The compressor motor51ais a motor whose number of revolutions can be controlled by an inverter. By controlling the number of revolutions of the compressor motor51a,the volume of the compressor51is controlled.

(2-1-2-2) Flow Direction Switching Mechanism

The flow direction switching mechanism52is a mechanism that switches the flow direction of the refrigerant to change the state of the refrigerant circuit33between a first state and a second state.

When the refrigerant circuit33is in the first state, the outdoor heat exchanger53functions as a condenser of the refrigerant, and the indoor heat exchanger42functions as an evaporator of the refrigerant. The flow direction switching mechanism52sets the state of the refrigerant circuit33to the first state during the cooling operation. In other words, during the cooling operation, the flow direction switching mechanism52causes the intake pipe34ato communicate with the second gas refrigerant pipe34eand causes the discharge pipe34hto communicate with the first gas refrigerant pipe34cas indicated in the solid lines in the flow direction switching mechanism52inFIG.2.

When the refrigerant circuit33is in the second state, the outdoor heat exchanger53functions as an evaporator of the refrigerant, and the indoor heat exchanger42functions as a condenser of the refrigerant. The flow direction switching mechanism52sets the state of the refrigerant circuit33to the second state during the heating operation. In other words, during the heating operation, the flow direction switching mechanism52causes the intake pipe34ato communicate with the first gas refrigerant pipe34cand causes the discharge pipe34bto communicate with the second gas refrigerant pipe34eas indicated in the broken lines in the flow direction switching mechanism52inFIG.2.

According to the present embodiment, the flow direction switching mechanism52is a four-way switching valve.

(2-1-2-3) Outdoor Heat Exchanger

In the outdoor heat exchanger53, heat is exchanged between the refrigerant flowing inside the outdoor heat exchanger53and the air in the outdoor area where the outdoor unit50is installed. The outdoor heat exchanger53is, for example, a fin-and-tube heat exchanger including a plurality of heat transfer tubes and fins.

One end of the outdoor heat exchanger53is coupled to the liquid refrigerant pipe34d.The other end of the outdoor heat exchanger53is coupled to the first gas refrigerant pipe34c.

The outdoor heat exchanger53functions as a condenser of the refrigerant during the cooling operation and functions as an evaporator of the refrigerant during the heating operation.

(2-1-2-4) Outdoor Expansion Valve

The outdoor expansion valve54is a mechanism that adjusts the pressure and the flow rate of the refrigerant flowing through the liquid refrigerant pipe34d.As illustrated inFIG.2, the outdoor expansion valve54is provided in the liquid refrigerant pipe34d.The outdoor expansion valve54is, for example, an electronic expansion valve whose opening degree is adjustable.

The accumulator55has a gas-liquid separation function to separate the flowing refrigerant into a gas refrigerant and a liquid refrigerant. Furthermore, the accumulator55is a container that has the function to store an excess refrigerant generated in accordance with fluctuations in the operation load of the indoor unit40, etc. As illustrated inFIG.2, the accumulator55is provided in the intake pipe34a.The refrigerant flowing into the accumulator55is separated into a gas refrigerant and a liquid refrigerant, and the gas refrigerant collected in an upper space flows into the compressor51.

(2-1-2-6) Outdoor Fan

The outdoor fan56is a fan that supplies air to the outdoor heat exchanger53. Specifically, the outdoor fan56is a fan that takes in the heat source air outside the outdoor unit50into the casing (not illustrated) of the outdoor unit50, supplies the heat source air to the outdoor heat exchanger53, and discharges the air, which has exchanged heat with the refrigerant in the outdoor heat exchanger53, to the outside of the casing of the outdoor unit50. The outdoor fan56is, for example, a propeller fan. The outdoor fan56is driven by an outdoor fan motor56a.The number of revolutions of the outdoor fan motor56acan be controlled by an inverter.

As illustrated inFIG.2, the outdoor unit50includes an outdoor temperature sensor58a,an outdoor humidity sensor58b,and an outdoor solar radiation sensor58c.

The outdoor temperature sensor58ameasures the temperature of the air in the outdoor area where the outdoor unit50is installed.

The outdoor humidity sensor58bmeasures the humidity of the air in the outdoor area where the outdoor unit50is installed. According to the present embodiment, the outdoor humidity sensor58bdetects the relative humidity. However, this is not a limitation, and the outdoor humidity sensor58bmay detect the absolute humidity.

The outdoor solar radiation sensor58cmeasures the solar radiation in the outdoor area where the outdoor unit50is installed.

Furthermore, these sensors are some of the sensors, and the outdoor unit50also includes sensors that measure the refrigerant temperature, the refrigerant pressure, etc.

(2-1-2-8) Outdoor Control Unit

The outdoor control unit57controls the operation of each unit included in the outdoor unit50. The outdoor control unit57includes a control arithmetic device and a storage device. A processor such as a CPU or a GPU may be used as the control arithmetic device. The control arithmetic device reads a program stored in the storage device and performs predetermined arithmetic processing in accordance with the program. Further, the control arithmetic device can write an arithmetic result in the storage device and read information stored in the storage device in accordance with the program.

As illustrated inFIG.2, the outdoor control unit57is electrically connected to the compressor51, the flow direction switching mechanism52. the outdoor expansion valve54, the outdoor fan56, the outdoor temperature sensor58a,the outdoor humidity sensor58b, and the outdoor solar radiation sensor58cto enable exchange of control signals and information.

The outdoor control unit57is coupled to the indoor control unit44of the indoor unit40and the total heat exchange control unit21of the total heat exchanger20via the transmission line61in a state where control signals, and the like, can be exchanged. The outdoor control unit57, the indoor control unit44, and the total heat exchange control unit21cooperate with each other to function as the control unit60that controls the overall operation of the air conditioning system30. The control unit60will be described below.

(2-1-3) Total Heat Exchanger

The total heat exchanger20is a unit installed in the target space11. For example, the total heat exchanger20is a ceiling-embedded unit, The total heat exchanger20performs total heat exchange operation. The total heat exchange refers to the exchange of sensible heat and the exchange of latent heat. The total heat exchange operation is an operation to perform total heat exchange between the exhaust air discharged from the target space11to outside the room and the intake air taken into the target space11as fresh air from outside the room.

The total heat exchanger20includes the total heat exchange control unit21. The total heat exchange control unit21controls the operation of each unit included in the total heat exchanger20. The total heat exchange control unit21includes a control arithmetic device and a storage device. A processor such as a CPU or a GPU may be used as the control arithmetic device. The control arithmetic device reads a program stored in the storage device and performs predetermined arithmetic processing in accordance with the program. Further, the control arithmetic device can write an arithmetic result in the storage device and read information stored in the storage device in accordance with the program.

The total heat exchange control unit21is configured to enable reception of various signals transmitted from the remote controller46to operate the total heat exchanger20. The various signals transmitted from the remote controller46include signals for giving instructions to operate or stop the total heat exchanger20and signals regarding various settings.

(2-1-4) Control Unit

As illustrated inFIG.2, the control unit60is configured by communicatively coupling the indoor control unit44of the indoor unit40, the outdoor control unit57of the outdoor unit50, and the total heat exchange control unit21of the total heat exchanger20via the transmission line61. The control unit60controls the overall operation of the air conditioning system30when the control arithmetic devices of the indoor control unit44, the outdoor control unit57, and the total heat exchange control unit21execute programs stored in the storage devices.

As illustrated inFIG.2, the control unit60is electrically connected to the indoor expansion valve4the indoor fan43. the indoor temperature sensor45a,and the indoor humidity sensor45bof the indoor unit40. Furthermore, the control unit60is electrically connected to the compressor51, the flow direction switching mechanism52, the outdoor expansion valve54, the outdoor fan56, the outdoor temperature sensor58a,the outdoor humidity sensor58b,and the outdoor solar radiation sensors58cof the outdoor unit50.

The control unit60controls the operation and stoppage of the air conditioning system30and the operations of various devices of the air conditioning system30based on measurement signals of the various sensors45a,45b,58a,58b,58c,and the like, commands received by the indoor control unit44and the total heat exchange control unit21from the remote controller46, etc.

Furthermore, the control unit60acquires information (air conditioning information37) about the air conditioning system30from the air conditioning system30at predetermined time intervals and transmits the information to the air conditioning database80. According to the present embodiment, the predetermined time interval is per unit time. For the verification described below, the unit time is set to one hour. The air conditioning database80stores the transmitted air conditioning information37.

FIG.3is a configuration diagram of the air conditioning database80. As illustrated inFIGS.1and3, the air conditioning information37includes time information I11, date information I12, an indoor humidity I21, an indoor temperature I22, an air conditioning operating time I23, a post air conditioning operation start elapsed time I24, an outside air temperature I25, an outside air humidity I26, solar radiation I27, an actual air conditioning load140, and a total heat exchange operating time131, The air conditioning information37is not limited to these pieces of information, and may include measurement information of other sensors, etc.

The time information I11and the date information I12are the time and date when the control unit60acquires the air conditioning information37. The time information I11is, for example, a 6-digit numerical value indicating a time, such as 18 (hour) 00 (minute) 00 (second).

The date information I12is, for example, an 8-digit numerical value indicating a date, such as 2018 (year) 01 (month) 01 (day).

The indoor humidity I21is a measurement value of the indoor humidity sensor45bwhen the control unit60acquires the air conditioning information37.

The indoor temperature I22is a measurement value of the indoor temperature sensor45awhen the control unit60acquires the air conditioning information37.

The air conditioning operating time I23is a time during which the air conditioning operation is performed. In other words, the air conditioning operating time I23is a time in a state where the indoor unit40is on in the remote controller46. According to the present embodiment, the air conditioning operating time I23is a time during which the air conditioning operation is performed in a unit time from the previous acquisition of the air conditioning information37by the control unit60. The time up to a unit time is stored as the air conditioning operating time I23.

The post air conditioning operation start elapsed time124is a cumulative operating time (e.g., up to three hours) from the start of the most recent air conditioning operation after the stoppage of the air conditioning operation for a long time (e.g., five hours or more) at the time when the control unit60acquires the air conditioning information37. When the air conditioning operation has stopped when the control unit60acquires the air conditioning information37, 0 is stored as the post air conditioning operation start elapsed time I24. Even when the air conditioning operation has started, 0 is stored as the post air conditioning operation start elapsed time I24in a case where the operation does not correspond to the operation after the stoppage of the air conditioning operation for a long time or in a case where the cumulative operating time from the start of the air conditioning operation is more than a predetermined time (e.g., three hours described above). When the air conditioning operation has started, the operation corresponds to the operation after the stoppage of the air conditioning operation for a long time, and the cumulative operating time from the start of the air conditioning operation falls within a predetermined time (e.g., three hours described above), the time from the start of the most recent air conditioning operation to the time when the control unit60acquires the air conditioning information37is stored as the post air conditioning operation start elapsed time I24.

The outside air temperature I25is a measurement value of the outdoor temperature sensor58awhen the control unit60acquires the air conditioning information37.

The outside air humidity I26is a measurement value of the outdoor humidity sensor58bwhen the control unit60acquires the air conditioning information37.

The solar radiation I27is a measurement value of the outdoor solar radiation sensor58cwhen the control unit60acquires the air conditioning information37.

The actual air conditioning load140is an actual air conditioning load processed by the air conditioning system30in the target space11inside the target building10. According to the present embodiment, the actual air conditioning load140is an air conditioning capacity actual measurement value of the outdoor unit50. The air conditioning capacity actual measurement value of the outdoor unit50is calculated using a compressor curve method (CC method) based on the actual measurement value of an internal sensor of the outdoor unit50. The CC method is a method for calculating a supply capacity by using a refrigerant enthalpy difference obtained from sensor information (refrigerant temperature, refrigerant pressure, etc.) inside the multi-air conditioning system for buildings and a refrigerant circulation amount obtained from the number of revolutions of the compressor motor51ausing a compressor property curve. As the cooling load actual measurement value included in the actual air conditioning load140, a cooling capacity actual measurement value of the outdoor unit50is used as it is. As the heating load actual measurement value included in the actual air conditioning load140, the value is used, which is obtained by removing heat loss due to the liquid refrigerant connection pipe31and the gas refrigerant connection pipe32between the outdoor unit50and the indoor unit40from the heating capacity actual measurement value of the outdoor unit50. The cooling load actual measurement value and the heating load actual measurement value are calculated for each system of the outdoor unit50.

The total heat exchange operating time131is a time during which the total heat exchange operation is performed. In other words, the total heat exchange operating time131is a time in a state where the total heat exchanger20is on in the remote controller46. According to the present embodiment, the total heat exchange operating time131is a time during which the total heat exchange operation is performed in a unit time from the previous acquisition of the air conditioning information37by the control unit60. The time up to a unit time is stored as the total heat exchange operating time131.

(2-2) Physical Model Calculation Device

The physical model calculation device70includes a control arithmetic device and a. storage device. A processor such as a CPU or a GPU may be used as the control arithmetic device. The control arithmetic device reads a program stored in the storage device and performs predetermined image processing and arithmetic processing in accordance with the program. Further, the control arithmetic device can write an arithmetic result in the storage device and read information stored in the storage device in accordance with the program.

The physical model calculation device70calculates the thermal load of the target building10based on the physical model PM. FIG,4is a configuration diagram of a physical model calculation process. As illustrated inFIG.4, the physical model calculation device70inputs an input value130to the physical model PM to calculate a first prediction air conditioning load161. The first prediction air conditioning load161is a predicted air conditioning load in the target space11.

The input value130includes at least a thermal property of the target building10. The thermal property of the target building10is, for example, thermal insulation property, heat capacity, building shape, solar radiation shielding performance, amount of ventilation of the target building10, the thermal property of building materials used for obtaining them, Q value, etc. The content of the input value130depends on the physical model PM and dynamic thermal load calculation software. In addition to the thermal property of the target building10, the input value130may include, for example, outside air condition, solar radiation condition, indoor condition (e.g., indoor temperature and indoor humidity), condition of air conditioning or outside air introduction (e.g., air conditioning, on/off or ventilation introduction on/off), and information about internal heat generation, etc.

The physical model PM may be a model including a simple heat balance equation or may be a model included in existing dynamic thermal load calculation software. Examples of the model including a simple heat balance equation include the following Equation 1.

Here, Qsolaris a solar radiation load from a window, Qoutairis a through-flow thermal load from a wall, Qbuilis heat storage of indoor thermal mass. Qairis heat storage of air, Qsolaris a ventilation load, Igis internal heat generation, and ΦHVACis an air conditioning load in the target space11. The linear physical model PM such as Math. 1 makes it possible to inversely calculate the corresponding input value130from the air conditioning load in the target space11. Furthermore, the linear physical model PM such as Math. 1 makes it possible to calculate the difference in the air conditioning load in the target space11from the difference in the input value130and to inversely calculate the difference in the corresponding input value130from the difference in the air conditioning load in the target space11.

(2-3) Air Conditioning Load Learning Apparatus

FIG.5is a configuration diagram of an air conditioning load learning process. As illustrated inFIGS.1and5, the air conditioning load learning apparatus110primarily includes an actual load acquisition unit119, a prediction load acquisition unit113, a first information acquisition unit111, a second information acquisition unit112, and a learning unit114.

The air conditioning load learning apparatus110includes a control arithmetic device and a storage device. A processor such as a CPU or a GPU may be used as the control arithmetic device. The control arithmetic device reads a program stored in the storage device and performs predetermined image processing and arithmetic processing in accordance with the program. Further, the control arithmetic device can write an arithmetic result in the storage device and read information stored in the storage device in accordance with the program. The actual load acquisition unit119, the prediction load acquisition unit113, the first information acquisition unit111, the second information acquisition unit112, and the learning unit114are various functional blocks implemented by the control arithmetic device and the storage device.

(2-3-1) Actual Load Acquisition Unit

As illustrated inFIG.5, the actual load acquisition unit119acquires the actual air conditioning load140, which is the actual air conditioning load in the target space11inside the target building10, from the air conditioning information37in the air conditioning database80. According to the present embodiment, the actual air conditioning load140is an air conditioning load per unit time.

(2-3-2) Prediction Load Acquisition Unit

As illustrated inFIG.5, the prediction load acquisition unit113acquires, from the physical model calculation device70, the first prediction air conditioning load161that is an air conditioning load in the target space11predicted by the physical model PM from at least the thermal property of the target building10.

(2-3-3) First Information Acquisition Unit

As illustrated inFIG.5, the first information acquisition unit111acquires the first information I1. The first information I1is information about the operation of the target building10. According to the present embodiment, the first information I1includes the time information I11, the date information I12, day-of-week information I13, and holiday information I14. The first information I1may further include information about an operation schedule such as an event.

According to the present embodiment, the time information I11and the date information I12are acquired from the air conditioning information37in the air conditioning database80. The day-of-week information I13and the holiday information I14are calculated based on the date information I12. The day-of-week information I13is a numerical value corresponding to the day of week, such as “1” for Monday and “2” for Tuesday. The holiday information I14is a numerical value, for example, “0” for a day that is not a holiday and “1” for a holiday.

(2-3-4) Second Information Acquisition Unit

As illustrated inFIG.5, the second information acquisition unit112acquires the second information I2. The second information I2is at least one of the indoor humidity I21, the indoor temperature I22, the air conditioning operating time I23, the post air conditioning operation start elapsed time I24, the outside air temperature I25, the outside air humidity I26, and the solar radiation I27.FIG.5illustrates all of these pieces of information.

According to the present embodiment, the second information I2is acquired from the air conditioning information37in the air conditioning database80.

(2-3-5) Learning Unit

As illustrated inFIG.5, the learning unit114generates the learning model LM1using the first information I1and the second information I2as explanatory variables and using a difference load170, which is the difference between the actual air conditioning load140and the first prediction air conditioning load161, as an objective variable.

The period (learning data period) of data used for the air conditioning load learning process of the air conditioning load learning apparatus110may be shorter than the period (prediction data period) of data used for the air conditioning load prediction process of the air conditioning load prediction apparatus120. Specifically, the learning model LM1may be generated based on the first information I1, the second information I2, the actual air conditioning load140, and the first prediction air conditioning load161in a period shorter than the prediction data period.

According to the present embodiment, the learning model LM1is a regression-type multilayer perceptron. However, the learning model LM1is not limited thereto, and the learning model LM1may be any regression model. The regression-type multilayer perceptron will be described in the verification below,

(2-4) Air Conditioning Load Prediction Apparatus

FIG.6is a configuration diagram of an air conditioning load prediction process. As illustrated inFIGS.1and6, the air conditioning load prediction apparatus120primarily includes a prediction load acquisition unit123, a first information acquisition unit121, a second information acquisition unit122, a difference load prediction unit124, and a load prediction unit125.

The air conditioning load prediction apparatus120includes a control arithmetic device and a storage device. A processor such as a CPU or a GPU may be used as the control arithmetic device. The control arithmetic device reads a program stored in the storage device and performs predetermined image processing and arithmetic processing in accordance with the program. Further, the control arithmetic device can write an arithmetic result in the storage device and read information stored in the storage device in accordance with the program. The prediction load acquisition unit123, the first information acquisition unit121, the second information acquisition unit122, the difference load prediction unit124, and the load prediction unit125are various functional blocks implemented by the control arithmetic device and the storage device.

(2-4-1) Prediction Load Acquisition Unit

As illustrated inFIG.6, the prediction load acquisition unit123acquires the first prediction air conditioning load161in the same manner as the prediction load acquisition unit113in the air conditioning load learning apparatus110.

(24-2) First Information Acquisition Unit

As illustrated inFIG.6, the first information acquisition unit121acquires the first information I2in the same manner as the first information acquisition unit111in the air conditioning load learning apparatus110.

(2-4-3) Second Information Acquisition Unit

As illustrated inFIG.6, the second information acquisition unit122acquires the second information I2in the same manner as the second information acquisition unit112in the air conditioning load learning apparatus110.

(2-4-4) Difference Load Prediction Unit

As illustrated inFIG.6, the difference load prediction unit124uses the learning model LM1generated by the learning unit114to predict the difference load170from the first information I1and the second information I2.

From the first information I1and the second information I2in a period longer than the learning data period, the difference load prediction unit124may predict the difference load170in the same long period.

(2-4-5) Load Prediction Unit

As illustrated inFIG.6, the load prediction unit125predicts the air conditioning load (a second prediction air conditioning load162) in the target space11based on the predicted difference load170and the first prediction air conditioning load161(by adding or subtracting the difference load170to or from the first prediction air conditioning load161).

Based on the predicted difference load170in the long period and the first prediction air conditioning load16-1in the same long period, the load prediction unit125may predict the air conditioning load in the same long period in the target space11.

(3-1) Air Conditioning Load Learning Process

The air conditioning load learning process will be described using the flowchart ofFIG.7.

As described in Step S111the air conditioning load learning apparatus110acquires the actual air conditioning load140from the air conditioning information37in the air conditioning database80.

After acquiring the actual air conditioning load140, the air conditioning load learning apparatus110acquires the first prediction air conditioning load161from the physical model calculation device70as described in Step S112.

After acquiring the first prediction air conditioning load161, the air conditioning load learning apparatus110acquires the first information I1from the air conditioning information37in the air conditioning database80as described in Step S113.

After acquiring the first information I1, the air conditioning load teaming apparatus110acquires the second information I2from the air conditioning information37in the air conditioning database80as described in Step S114.

After acquiring the second information I2, the air conditioning load learning apparatus110calculates the difference load170, which is the difference between the actual air conditioning load140and the first prediction air conditioning load161, as described in Step S115.

After calculating the difference load170, the air conditioning load learning apparatus110generates the learning model LM1using the first information I1and the second information I2as explanatory variables and using the difference load170as an objective variable as described in Step S116.

(3-2) Air Conditioning Load Prediction Process

The air conditioning load prediction process will be described using the flowchart ofFIG.8.

As described in Step S121, the air conditioning load prediction apparatus120acquires the first prediction air conditioning load161from the physical model calculation device70.

After acquiring the first prediction air conditioning load161, the air conditioning load prediction apparatus120acquires the first information I1from the air conditioning information37in the air conditioning database80as described in Step S122.

After acquiring the first information I1, the air conditioning load prediction apparatus120acquires the second information I2from the air conditioning information37in the air conditioning database80as described in Step S123.

After acquiring the second information I2, the air conditioning load prediction apparatus120predicts the difference load170from the first information I1and the second information I2using the learning model LM1as described in Step S124.

After predicting the difference load170, the air conditioning load prediction apparatus120adds or subtracts the difference load170to or from the first prediction air conditioning load161to calculate the second prediction air conditioning load162as described in Step S125.

(4) Verification of Prediction Accuracy

(4-1) Verification Flow

The air conditioning load of the target building10is predicted using the air conditioning load learning apparatus110and the air conditioning load prediction apparatus120, and the prediction accuracy is verified.

The learning data period of this verification is the entire period of 2018 and a partial period of 2018, The prediction data period for this verification is the entire period of 2019,

In this verification, first, the air conditioning load learning apparatus110generates the learning model LM1from the first information I1, the second information I2, the actual air conditioning load140, and the first prediction air conditioning load161of 2018. Subsequently, the air conditioning load prediction apparatus120inputs the first information I1, and the second information I2of 2019 to the learning model LM1, from which the difference load170of 2019 is output. Further, the first prediction air conditioning load161of 2019 is added to the difference load170of 2019 to calculate the second prediction air conditioning load162of 2019. Finally, the actual air conditioning load140of 2019 is compared with the second prediction air conditioning load162of 2019 to verify the prediction accuracy.

(4-2) Target Building, Target Space

Table 1 below is an overview of the target building10used for this verification.

As described in Table 1, the target space11of the target building10is offices on the second to ninth floors.FIG.9is a plan view of the target building10used for this verification, As illustrated inFIG.9, the office on each floor includes 14 zones11a1to11a8,11b1to11b6.

(4-3) Air Conditioning System

The air conditioning system30for this verification is a multi-air conditioning system for buildings. As illustrated inFIG.9, the air conditioning system30includes two systems of outdoor unit50a,50bper floor. In the zones11a1to11a8, indoor units40a1to40a8are installed as a system of the outdoor unit50a.In the zones11b1to11b6, indoor units40b1to40b6are installed as a system of the outdoor unit50b.

(4-4) Various Types of Data

For this verification, hourly data is used.

(4-4-1) Actual Air conditioning Load

The actual air conditioning load140is acquired from the air conditioning information37in the air conditioning database80. In this verification, as there are two systems of the outdoor units50per floor, the sums of the cooling load actual measurement value and the heating load actual measurement value of the two systems are used as the cooling load actual measurement value and the heating load actual measurement value of each floor.

(4-4-2) First Prediction Air Conditioning Load

For this verification, Energyplus is used as dynamic thermal load calculation software for calculating the first prediction air conditioning load161. Trimble SketchUp is used to model the chamber shape. Table 2 below describes the wall and window structure of the target building10for the dynamic thermal load calculation.

Table 3 below describes various input conditions to the dynamic thermal load calculation software (the input value130to the physical model PM).

TABLE 3ItemContentWeather dataCreate a weather file for EnergyPlus from Tokyoweather data (AMEDAS) from 2018 to 2019.Chamber shapeInput based on building drawings.Adjacent buildingEstimate and input adjacent-building distance andconditionsheight from Google Maps.Air conditioningTurn on when the time-by-time indoor unit remoteon/offcontroller ON time <min> is larger than 0. The airconditioning operating time I23 is used as the indoorunit remote controller ON time.AchievedCooling: indoor unit suction temperaturetemperatureHeating: indoor unit suction temperature −2° C.(when air(considering upper and lower temperature differences)conditioningThe indoor temperature I22 is used as the indoor unitis on)suction temperature.Outside airTurn on when the time-by-time total heat exchangerintroductionremote controller ON time <min> is larger than 0. Theon/offtotal heat exchange operating time 131 is used as thetotal heat exchanger remote controller ON time.IntroducedInput based on catalogue values of the total heatoutside airexchanger 20. 375 <m3/h> per indoor unit zoneamountWall and windowInput the content of Table 2.structureDraft0.2 <times/h>Blind open/closeNeutral-colored blind. Always closed.Internal heatPersons: 0.1 <person/m2>generationIllumination: 12 <W/m2>Device heat generation: 12 <W/m2>Internal heatSummer and winter: internal heat is generated when airgenerationconditioning is on. (The ratio is determined based onscheduleenergy saving law calculation schedule)Intermediate period: energy-saving law calculationscheduleInter-zone mixing150 <m3/(m · h)>

(4-4-3) Explanatory Variable of Learning Model

Table 4 below describes the explanatory variables of the learning model LM1for this verification.

TABLE 4Explanatory variableContentOutside air temperatureUse the outside air temperature I25 of<° C.>the second information I2.Solar radiation <MJ/m2>Use the solar radiation I27 of the secondinformation I2.Relative humidity <%>Use the outside air humidity I26 of thesecond information I2.Wind velocity <m/s>Acquire values on the neighborhood ofthe target building 10 from Tokyoweather data (AMEDAS) from 2018 to2019.Precipitation <mm>Acquire values on the neighborhood ofthe target building 10 from Tokyoweather data (AMEDAS) from 2018 to2019.Time <0 to 23>Use the date information I12 and thetime information I11 of the firstinformation I1.Day of week <Monday toUse the date information I12, the day-Sunday, holiday>of-week information I13, and theholiday information I14 of the firstinformation I1.Total remote controller ONCalculate from the air conditioningtime <min> of all indoor unitsoperating time I23 of the secondinformation I2.Average suction air temperatureCalculate from the indoor temperatureof all indoor units <° C.>I22 of the second information I2.Suction air temperature of eachUse the indoor temperature I22 of theindoor unit <° C.>second information I2.

(4-4-4) Objective Variable of Learning Model

The objective variable of the learning model LM1in this verification is the difference load170obtained by subtracting the first prediction air conditioning load161from the actual air conditioning load140.

(4-5) Learning model

In this verification, the learning model LM1uses a multilayer perceptron. The multilayer perceptron is a model that enables nonlinear regression by providing a hidden layer between an input layer and an output layer for linear regression and applying a nonlinear function to a result of a weighted sum.

To create the model, the data set of 2018 is divided into a training set and a validation set, and hyperparameters (the number of hidden layers, the number of nodes in the hidden layer, and a regularization parameter) are adjusted such that the accuracy in the validation set becomes the highest. Learning is then performed again with the adjusted parameters using all the data set of 2018.

(4-6) Evaluation Index

As an error index, the CVRMSE (Coefficient of Variation of the Root Mean Square Error) described below is used.

Here, miis an actual measurement value, siis a prediction value,mis the mean of actual measurement values, and n is the number of data. According to ASHRAE Guideline 14, in the case of time-by-time data prediction, 30% or less of the CVRMSE is used as a reference value for prediction accuracy evaluation. Furthermore, as prediction accuracy verification of a peak load that is important at the time of design, a relative error of top 2.5% from the peak is also evaluated.

(4-7) Verification Result

A result of annual cooling load prediction in the future (one year of 2019) from the annual (one year of 2018) actual measurement and the short-period (one month of July 2018) actual measurement is described here.

FIG.10illustrates the CVRMSE for each floor when the future annual time-by-time cooling load is predicted from the annual actual measurement and the short-period actual measurement. InFIG.10, the accuracy of the annual actual measurement is indicated in the graph line in which the values are plotted with square marks. Further, the accuracy of the short-period actual measurement is indicated in the graph line in which the values are plotted with triangle marks, As illustrated inFIG.10, in the case of the prediction based on the annual actual measurement, the values of the CVRMSE fall within 30%, which is the reference value of ASHRAE Guideline 14, in all the floors, The mean value for all the floors in the CVRMSE is 22% in the case of the annual actual measurement and is 27% in the case of the short-period actual measurement. In the case of the short-period actual measurement, as the learning data is naturally reduced, the accuracy of the model is lowered as a whole as compared with the annual actual measurement.

FIG.11illustrates the relative error absolute values of the top 2.5% cooling load prediction values. The graph lines are represented inFIG.11in the same manner as that inFIG.10. As illustrated inFIG.11, there is a difference between the short-period actual measurement and the annual actual measurement for the sixth floor and the seventh floor, but the accuracy is almost the same for the other floors. The mean value of the relative error absolute values of the top 2.5% cooling load prediction values for all the floors is 4% in the case of the annual actual measurement and is 5% in the case of the short-period actual measurement.

FIG.12illustrates the comparison among the time-by-time cooling load actual measurement value, the prediction value by the annual actual measurement, and the dynamic thermal load calculation value of the second floor in representative summer five days (August 19 to 23). InFIG.12, the actual measurement values are indicated in a graph line in which no values are plotted. Further, the prediction values by the annual actual measurement are indicated in a graph line in which the values are plotted with square marks. The dynamic thermal load calculation values are indicated in a graph line in which the values are plotted with cross marks. As illustrated inFIG.12, it can be seen that the prediction value by the annual actual measurement using the combination of the physical model PM and the learning model LM1is closer to the actual measurement value as compared with the dynamic thermal load calculation value using only the physical model PM.

Conventionally, the difference between the actual air conditioning load measurement value and the external thermal load (excluding the internal heat generation) obtained by the physical model is determined so that the internal heat generation related to the operation of the building is estimated and used for air conditioning load prediction. In order to predict the air conditioning load according to this method, there is a need to input the information about the operation of the building so as to reflect the effect of the operation of the building on the internal heat generation, etc.

However, the information about the operation of the building is typically hard to obtain and is often incomplete even if it is obtained. Therefore, the conventional method has an issue that it is difficult to grasp the effect of the operation of the building on the air conditioning load.

In the air conditioning load learning apparatus110according to the present embodiment, the actual load acquisition unit119acquires the actual air conditioning load140that is an actual air conditioning load in the target space11inside the target building10. The first information acquisition unit111acquires the first information I1. The first information i1is information about the operation of the target building10. The prediction load acquisition unit113acquires the first prediction air conditioning load161that is an air conditioning load in the target space11predicted by the physical model PM from at least the thermal property of the target building10. The second information acquisition unit112acquires the second information I2. The second information I2is at least one of the indoor humidity I21, the indoor temperature I22, the air conditioning operating time I23, the post air conditioning operation start elapsed time I24, the outside air temperature I25, the outside air humidity126, and the solar radiation127, The learning unit114generates the learning model LM1using the first information I1and the second information I2as explanatory variables and using the difference load170. which is the difference between the actual air conditioning load140and the first prediction air conditioning load161, as an objective variable.

Therefore, the air conditioning load learning apparatus110associates the first information I1and the second information I2with the difference load170(the value regarding the actual air conditioning load140) so as to learn the effect of the operation of the building on the internal heat generation, the outside air introduction load, the heat storage load, the solar radiation load, etc. As a result, the air conditioning load learning apparatus110can finally grasp the effect of the operation of the building on the air conditioning load.

In the air conditioning load learning apparatus110according to the present embodiment, the actual air conditioning load140is an air conditioning load per unit time.

As a result, the air conditioning load learning apparatus110can learn the effect of the operation of the building on the air conditioning load in more detail.

In the air conditioning load prediction apparatus120according to the present embodiment, the difference load prediction unit124uses the learning model LM1to predict the difference load170from the first information I1and the second information I2. The load prediction unit125predicts the air conditioning load (the second prediction air conditioning load162) in the target space11based on the predicted difference load170and the first prediction air conditioning load161.

As a result, the air conditioning load prediction apparatus120can use the learning model LM1having learned the effect of the operation of the building on the internal heat generation, the outside air introduction load, the heat storage load, the solar radiation load, and the like, to predict the air conditioning load in consideration of the effect of the operation of the building.

In the air conditioning load prediction apparatus120according to the present embodiment, the learning model LM1is generated based on the first information I1, the second information I2, the actual air conditioning load140, and the first prediction air conditioning load161in the short period. From the first information I1and the second information I2in the long period that is a period longer than the short period, the difference load prediction unit124predicts the difference load170in the same long period. Based on the predicted difference load170in the long period and the first prediction air conditioning load161in the same long period, the load prediction unit125predicts the air conditioning load (the second prediction air conditioning load162) in the same long period in the target space11.

As a result, the air conditioning load prediction apparatus120can use the learning model LM1having learned with the data in the short period to predict the air conditioning load in the long period.

According to the present embodiment, the first information I1, the second information I2, and the actual air conditioning load140are acquired from the air conditioning information37in the air conditioning database80. However, these pieces of information may be directly acquired from the air conditioning system30.

According to the present embodiment, the outside air temperature I25, the outside air humidity I26, and the solar radiation I27, which are the second information I2, are acquired from the air conditioning information37in the air conditioning database80. However, these pieces of information may be acquired from weather data (AMEDAS) on the neighborhood of the target building10, etc.

According to the present embodiment, the learning unit114generates the learning model LM1using the first information I1and the second information I2as explanatory variables and using the difference load170, which is the difference between the actual air conditioning load140and the first prediction air conditioning load161, as an objective variable. However, the learning unit114may generate the learning model LM1using the first prediction air conditioning load161as an explanatory variable instead of the second information I2. Further, the learning unit114may generate the learning model LM1using the first information I1, the second information I2, and the first prediction air conditioning load161as explanatory variables. As a result, the air conditioning load learning apparatus110can generate the learning model LM1with a higher prediction accuracy.

In this case, the difference load prediction unit124uses the learning model LM1to predict the difference load170from the first information I1and the second information I2or the first prediction air conditioning load161. Furthermore, from the first information I1and the second information I2or the first prediction air conditioning load161in the long period that is a period longer than the short period, the difference load prediction unit124predicts the difference load170in the same long period.

Although the embodiment of the present disclosure has been described above, it is understood that various modifications may be made to forms and details without departing from the spirit and scope of the present disclosure described in claims.

A different part from the first embodiment is primarily described below. Therefore, the present embodiment is basically the same as the first embodiment except for the contents described according to the present embodiment.

(1) Overall Configuration

FIG.13is a configuration diagram of an air conditioning load learning apparatus210and an air conditioning load prediction apparatus220. As illustrated inFIG.13, the air conditioning load learning apparatus210, the air conditioning load prediction apparatus220, the physical model calculation device70, the air conditioning system30, and the air conditioning database80are communicatively coupled via the network90.

(2) Detailed Configuration

(2-1) Air Conditioning System

The air conditioning system30and the air conditioning database80are the same as those according to the first embodiment.

(2-2) Physical Model Calculation Device

The physical model calculation device70calculates the thermal load of the target building10based on the physical model PM.FIG.14is a configuration diagram of the physical model calculation process. As illustrated inFIG.14, the physical model calculation device70inputs an input value230to the physical model PM to calculate a first prediction air conditioning load261. The input value230includes at least a thermal property of the target building10. The first prediction air conditioning load261is a predicted air conditioning load in the target space11.

(2-3) Air Conditioning Load Learning Apparatus

FIG.15is a configuration diagram of an air conditioning load learning process. As illustrated inFIGS.13and15, the air conditioning load learning apparatus210primarily includes an actual load acquisition unit219, a prediction load acquisition unit213, a first information acquisition unit211, a second information acquisition unit212, and a learning unit214.

The air conditioning load learning apparatus300includes a control arithmetic device and a storage device. A processor such as a CPU or a GPU may be used as the control arithmetic device. The control arithmetic device reads a program stored in the storage device and performs predetermined image processing and arithmetic processing in accordance with the program, Further, the control arithmetic device can write an arithmetic result in the storage device and read information stored in the storage device in accordance with the program. The actual load acquisition unit219, the prediction load acquisition unit213, the first information acquisition unit211, the second information acquisition unit212, and the learning unit214are various functional blocks implemented by the control arithmetic device and the storage device.

(2-3-1) Actual Load Acquisition Unit

As illustrated inFIG.15, the actual load acquisition unit219acquires an actual air conditioning load240, which is an actual air conditioning load in the target space11inside the target building10, from the air conditioning information37in the air conditioning database80. According to the present embodiment, the actual air conditioning load240is an air conditioning load per unit time.

(2-3-2) Prediction Load Acquisition Unit

As illustrated inFIG.15, the prediction load acquisition unit213acquires, from the physical model calculation device70, the first prediction air conditioning load261that is an air conditioning load in the target space11predicted by the physical model PM from at least the thermal property of the target building10.

(2-3-3) First Information Acquisition Unit

As illustrated inFIG.15, the first information acquisition unit211acquires the first information I1from the air conditioning information37in the air conditioning database80. The first information I1is information about the operation of the target building10.

(2-3-4) Second Information Acquisition Unit

As illustrated inFIG.15, the second information acquisition unit212acquires the second information I2from the air conditioning information37in the air conditioning database80. The second information I2is at least one of the indoor humidity I21, the indoor temperature I22, the air conditioning operating time I23, the post air conditioning operation start elapsed time I24, the outside air temperature I25, the outside air humidity I26, and the solar radiation I27.FIG.15describes all of these pieces of information.

(2-3-5) Learning Unit

As illustrated inFIG.15, the learning unit214generates a learning model LM2using the first prediction air conditioning load261, the first information I1, and the second information I2as explanatory variables and using the actual air conditioning load240as an objective variable.

The period (learning data period) of data used for the air conditioning load learning process of the air conditioning load learning apparatus210may be shorter than the period (prediction data period) of data used for the air conditioning load prediction process of the air conditioning load prediction apparatus220. Specifically, the learning model LM2may be generated based on the first prediction air conditioning load261, the first information I1, the second information I2, and the actual air conditioning load240in a period shorter than the prediction data period.

(2-4) Air Conditioning Load Prediction Apparatus

FIG.16is a configuration diagram of an air conditioning load prediction process. As illustrated inFIGS.13and16, the air conditioning load prediction apparatus220primarily includes a prediction load acquisition unit223, a first information acquisition unit221, a second information acquisition unit222, and a load prediction unit225.

The air conditioning load prediction apparatus220includes a control arithmetic device and a storage device. A processor such as a CPU or a GPU may be used as the control arithmetic device. The control arithmetic device reads a program stored in the storage device and performs predetermined image processing and arithmetic processing in accordance with the program. Further, the control arithmetic device can write an arithmetic result in the storage device and read information stored in the storage device in accordance with the program. The prediction load acquisition unit223, the first information acquisition unit221, the second information acquisition unit222, and the load prediction unit225are various functional blocks implemented by the control arithmetic device and the storage device.

(2-4-1) Prediction Load Acquisition Unit

As illustrated inFIG.16, the prediction load acquisition unit223acquires the first prediction air conditioning load261in the same manner as the prediction load acquisition unit213in the air conditioning load learning apparatus210.

(2-4-2) First Information Acquisition Unit

As illustrated inFIG.16, the first information acquisition unit221acquires the first information I1in the same manner as the first information acquisition unit211in the air conditioning load learning apparatus210.

(2-4-3) Second Information Acquisition Unit

As illustrated inFIG.16, the second information acquisition unit222acquires the second information I2in the same manner as the second information acquisition unit212in the air conditioning load learning apparatus210.

(2-4-4) Load Prediction Unit

As illustrated inFIG.16, the load prediction unit225uses the learning model LM2generated by the learning unit214to predict the air conditioning load (a second prediction air conditioning load262) in the target space11from the first prediction air conditioning load261, the first information I1, and the second information I2.

Based on the first prediction air conditioning load261, the first information I1, and the second information I2in a period longer than the learning data period, the load prediction unit225may predict the air conditioning load in the same long period in the target space11.

(3-1) Air Conditioning Load Learning Process

The air conditioning load learning process will be described using the flowchart ofFIG.17.

As described in Step S211, the air conditioning load learning apparatus210acquires the actual air conditioning load240from the air conditioning information37in the air conditioning database80.

After acquiring the actual air conditioning load240, the air conditioning load learning apparatus210acquires the first prediction air conditioning load261from the physical model calculation device70as described in Step S212.

After acquiring the first prediction air conditioning load261, the air conditioning load learning apparatus210acquires the first information I1from the air conditioning information37in the air conditioning database80as described in Step S213.

After acquiring the first information I1, the air conditioning load learning apparatus210acquires the second information I2from the air conditioning information37in the air conditioning database80as described in Step S214.

After acquiring the second information I2, the air conditioning load learning apparatus210generates the learning model LM2using the first prediction air conditioning load261, the first information I1, and the second information I2as explanatory variables and using the actual air conditioning load240as an objective variable as described in Step S215.

(3-2) Air Conditioning Load Prediction Process

The air conditioning load prediction process will be described using the flowchart ofFIG.18.

As described in Step S221, the air conditioning load prediction apparatus220acquires the first prediction air conditioning load261from the physical model calculation device70.

After acquiring the first prediction air conditioning load261, the air conditioning load prediction apparatus220acquires the first information I1from the air conditioning information37in the air conditioning database80as described in Step S222.

After acquiring the first information I1, the air conditioning load prediction apparatus220acquires the second information I2from the air conditioning information37in the air conditioning database80as described in Step S223.

After acquiring the second information I2, the air conditioning load prediction apparatus220uses the learning model LM2to predict the second prediction air conditioning load262in the target space11from the first prediction air conditioning load261, the first information I1, and the second information I2as described in Step S224.

In the air conditioning load learning apparatus210according to the present embodiment, the actual load acquisition unit219acquires the actual air conditioning load240, which is an actual air conditioning load in the target space11inside the target building10. The first information acquisition unit211acquires the first information I1. The first information I1is information about the operation of the target building10. The prediction load acquisition unit213acquires the first prediction air conditioning load261that is an air conditioning load in the target space11predicted by the physical model PM from at least the thermal property of the target building10. The second information acquisition unit212acquires the second information I2. The second information I2is at least one of the indoor humidity I21, the indoor temperature I22, the air conditioning operating time I23, the post air conditioning operation start elapsed time I24. the outside air temperature I25, the outside air humidity I26, and the solar radiation I27. The learning unit214generates the learning model LM2using the first prediction air conditioning load261, the first information I1, and the second information I2as explanatory variables and using the actual air conditioning load240as an objective variable.

Thus, the air conditioning load learning apparatus210associates the first information I1and the second information I2with the actual air conditioning load240(the value regarding the actual air conditioning load240) so as to learn the effect of the operation of the building on the internal heat generation, the outside air introduction load, the heat storage load, the solar radiation load, etc. As a result, the air conditioning load learning apparatus210can finally grasp the effect of the operation of the building on the air conditioning load.

In the air conditioning load learning apparatus210according to the present embodiment, the actual air conditioning load240is an air conditioning load per unit time.

As a result, the air conditioning load learning apparatus210can learn the effect of the operation of the building on the air conditioning load in more detail.

In the air conditioning load prediction apparatus220according to the present embodiment, the load prediction unit225uses the learning model LM2to predict the air conditioning load (the second prediction air conditioning load262) in the target space11from the first prediction air conditioning load261, the first information I1, and the second information I2.

As a result, the air conditioning load prediction apparatus220can use the learning model LM2having learned the effect of the operation of the building on the internal heat generation, the outside air introduction load, the heat storage load, the solar radiation load, and the like, to predict the air conditioning load in consideration of the effect of the operation of the building.

In the air conditioning load prediction apparatus220according to the present embodiment, the learning model LM2is generated based on the first prediction air conditioning load261, the first information I1, the second information I2, and the actual air conditioning load240in a short period. Based on the first prediction air conditioning load261, the first information I1, and the second information I2in the long period that is a period longer than the short period, the load prediction unit225predicts the air conditioning load (the second prediction air conditioning load262) in the same long period in the target space11.

As a result, the air conditioning load prediction apparatus220can use the learning model LM2having learned with the data in the short period to predict the air conditioning load in the long period.

Although the embodiment of the present disclosure has been described above, it is understood that various modifications may be made to forms and details without departing from the spirit and scope of the present disclosure described in claims.

A different part from the first embodiment is primarily described below. Therefore, the present embodiment is basically the same as the first embodiment except for the contents described according to the present embodiment.

(1) Overall Configuration

FIG.19is a configuration diagram of an air conditioning load learning apparatus310and an air conditioning load prediction apparatus320. As illustrated inFIG.19, the air conditioning load learning apparatus310, the air conditioning load prediction apparatus320, the physical model calculation device70, the air conditioning system30, and the air conditioning database80are communicatively coupled via the network90.

(2) Detailed Configuration

(2-1) Air Conditioning System

The air conditioning system30and the air conditioning database80are the same as those according to the first embodiment.

(2-2) Physical Model Calculation Device

The physical model calculation device70calculates the thermal load of the target building10based on the physical model PM. The present embodiment uses the physical model PM that can inversely calculate the corresponding input value from the air conditioning load in the target space11.

FIG.20is a configuration diagram of the physical model calculation process. As illustrated inFIG.20, the physical model calculation device70transmits a first input value331, which is an input value to the physical model PM, to the air conditioning load learning apparatus310and thus acquires an actual air conditioning load340corresponding to the first input value331. The physical model calculation device70uses the actual air conditioning load340to calculate a second input value332by inverse calculation of the physical model PM. Furthermore, the physical model calculation device70transmits the first input value331to the air conditioning load prediction apparatus320and thus inputs a third input value333received from the air conditioning load prediction apparatus320to the physical model PM, and calculates a prediction air conditioning load360. The first input value331, the second input value332, and the third input value333include at least the thermal property of the target building10. The prediction air conditioning load360is a predicted air conditioning load in the target space11.

(2-3) Air Conditioning Load Learning Apparatus

FIG.21is a configuration diagram of an air conditioning load learning process. As illustrated inFIGS.19and21, the air conditioning load learning apparatus310primarily includes an actual load acquisition unit319, an input value acquisition unit316, a first information acquisition unit311, and a learning unit314.

The air conditioning load learning apparatus310includes a control arithmetic device and a storage device. A processor such as a CPU or a GPU may be used as the control arithmetic device. The control arithmetic device reads a program stored in the storage device and performs predetermined image processing and arithmetic processing in accordance with the program. Further, the control arithmetic device can write an arithmetic result in the storage device and read information stored in the storage device in accordance with the program. The actual load acquisition unit319, the input value acquisition unit316, the first information acquisition unit311, and the learning unit314are various functional blocks implemented by the control arithmetic device and the storage device.

(2-3-1) Actual Load Acquisition Unit

As illustrated inFIG.21, the actual load acquisition unit319acquires, from the air conditioning information37in the air conditioning database80, the actual air conditioning load340, which is an actual air conditioning load in the target space11inside the target building10, corresponding to the first input value331acquired from the physical model calculation device70.

(2-3-2) Input Value Acquisition Unit

As illustrated inFIG.21, the input value acquisition unit316acquires the first input value331and the second input value332from the physical model calculation device70.

(2-3-3) First Information Acquisition Unit

As illustrated inFIG.21, the first information acquisition unit311acquires the first information I1from the air conditioning information37in the air conditioning database80. The first information I1is information about the operation of the target building10.

(2-3-4) Learning Unit

As illustrated inFIG.21, the learning unit314generates a learning model LM3using the first information I1as an explanatory variable and using a difference input value350, which is the difference between the first input value331and the second input value332, as an objective variable.

(2-4) Air Conditioning Load Prediction Apparatus

FIG.22is a configuration diagram of an air conditioning load prediction process. As illustrated inFIGS.19and22, the air conditioning load prediction apparatus320primarily includes a first information acquisition unit321, a difference input value prediction unit326, a prediction load acquisition unit323, and an input value acquisition unit329.

The air conditioning load prediction apparatus320includes a control arithmetic device and a storage device. A processor such as a CPU or a GPU may be used as the control arithmetic device. The control arithmetic device reads a program stored in the storage device and performs predetermined image processing and arithmetic processing in accordance with the program,

Further, the control arithmetic device can write an arithmetic result in the storage device and read information stored in the storage device in accordance with the program. The first information acquisition unit321, the difference input value prediction unit326, the prediction load acquisition unit323, and the input value acquisition unit329are various functional blocks implemented by the control arithmetic device and the storage device.

(2-4-1) First Information Acquisition Unit

As illustrated inFIG.22, the first information acquisition unit321acquires the first information Il in the same manner as the first information acquisition unit311in the air conditioning load learning apparatus310.

(2-4-2) Difference Input Value Prediction Unit

As illustrated inFIG.22, the difference input value prediction unit326predicts the difference input value350from the first information I1by using the learning model LM3generated by the learning unit314.

(2-4-3) Input Value Acquisition Unit

As illustrated inFIG.22, the input value acquisition unit329acquires the first input value331in the same manner as the input value acquisition unit316in the air conditioning load learning apparatus310.

(2-4-4) Prediction Load Acquisition Unit

As illustrated inFIG.22, the prediction load acquisition unit323acquires the prediction air conditioning load360that is an air conditioning load in the target space11predicted by the physical model PM. The prediction air conditioning load360is predicted by the physical model PM using the third input value333as an input. The third input value333is obtained by correcting the first input value331using the predicted difference input value350(by adding or subtracting the difference input value350to or from the first input value331).

(3-1) Air Conditioning Load Learning Process

The air conditioning load learning process will be described using the flowchart ofFIG.23.

As described in Step S311, the air conditioning load learning apparatus310acquires the first input value331from the physical model calculation device70.

After acquiring the first input value331, the air conditioning load learning apparatus310acquires the actual air conditioning load340corresponding to the first input value331from the air conditioning information37in the air conditioning database80as described in Step S312.

After acquiring the actual air conditioning load340, the air conditioning load learning apparatus310transmits the actual air conditioning load340to the physical model calculation device70and acquires the second input value332as described in Step S313.

After acquiring the second input value332, the air conditioning load learning apparatus310calculates the difference input value350that is the difference between the first input value331and the second input value332as described in Step S314.

After calculating the difference input value350, the air conditioning load learning apparatus310acquires the first information I1from the air conditioning information37in the air conditioning database80as described in Step S315.

After calculating the difference input value350and acquiring the first information I1, the air conditioning load learning apparatus310generates the learning model LM3using the first information I1as an explanatory variable and using the difference input value350as an objective variable as described in Step S316.

(3-2) Air Conditioning Load Prediction Process

The air conditioning load prediction process will be described using the flowchart ofFIG.24.

The air conditioning load prediction apparatus320acquires the first information I1from the air conditioning information37in the air conditioning database80as described in Step S321.

After acquiring the first information I1, the air conditioning load prediction apparatus320uses the learning model LM3to predicts the difference input value350from the first information I1as described in Step S322.

After predicting the difference input value350, the air conditioning load prediction apparatus320acquires the first input value331from the physical model calculation device70as described in Step S323.

After acquiring the first input value331, the air conditioning load prediction apparatus320adds or subtracts the difference input value350to or from the first input value331to calculate the third input value333as described in Step S324.

After calculating the third input value333, the air conditioning load prediction apparatus320acquires, from the physical model calculation device70, the prediction air conditioning load360predicted by the physical model PM using the third input value333as an input as described in Step S325.

In the air conditioning load learning apparatus310according to the present embodiment, the actual load acquisition unit319acquires the actual air conditioning load340that is an actual air conditioning load in the target space11inside the target building10. The first information acquisition unit311acquires the first information I1. The first information I1is information about the operation of the target building10. The input value acquisition unit316acquires the first input value331and the second input value332. The first input value331is an input value including at least the thermal property of the target building10to the physical model PM that outputs the prediction air conditioning load360that is a predicted air conditioning load in the target space11. The second input value332is an input value calculated by inverse calculation of the physical model PM using the actual air conditioning load340. The learning unit314generates the learning model LM3using the first information I1as an explanatory variable and using the difference input value350, which is the difference between the first input value331and the second input value332, as an objective variable.

Therefore, the air conditioning load learning apparatus310associates the first information I1with the difference input value350(the value regarding the actual air conditioning load340) so as to learn the effect of the operation of the building on internal heat generation, etc. As a result, the air conditioning load learning apparatus310can finally grasp the effect of the operation of the building on the air conditioning load.

In the air conditioning load prediction apparatus320according to the present embodiment, the difference input value prediction unit326uses the learning model LM3to predict the difference input value350from the first information I1. The prediction load acquisition unit323acquires the prediction air conditioning load360that is an air conditioning load in the target space11predicted by the physical model PM. The prediction air conditioning load360is predicted by the physical model PM using the third input value333as an input. The third input value333is obtained by correcting the first input value331using the predicted difference input value350.

As a result, the air conditioning load prediction apparatus320can use the learning model LM2having learned the effect of the operation of the building on internal heat generation, and the like, to predict the air conditioning load in consideration of the effect of the operation of the building.

According to the present embodiment, the learning unit314generates the learning model LM3using the first information I1as an explanatory variable and using the difference input value350, which is the difference between the first input value331and the second input value332, as an objective variable. However, the learning unit314may further generate the learning model LM3using the first input value331as an explanatory variable.

As a result, the air conditioning load learning apparatus310can generate the learning model LM3haying a higher prediction accuracy.

In this case, the difference input value prediction unit326uses the learning model LM3to predict the difference input value350from the first information I1and the first input value331.

Although the embodiment of the present disclosure has been described above, it is understood that various modifications may be made to forms and details without departing from the spirit and scope of the present disclosure described in claims.

A different part from the first embodiment is primarily described below. Therefore, the present embodiment is basically the same as the first embodiment except for the contents described according to the present embodiment.

(1) Overall Configuration

FIG.25is a configuration diagram of an air conditioning load learning apparatus410and an air conditioning load prediction apparatus420. As illustrated inFIG.25, the air conditioning load learning apparatus410, the air conditioning load prediction apparatus420, the physical model calculation device70, the air conditioning system30, and the air conditioning database80are communicatively coupled via the network90.

(2) Detailed Configuration

(2-1) Air Conditioning System

The air conditioning system30and the air conditioning database80are the same as those according to the first embodiment.

(2-2) Physical Model Calculation Device

The physical model calculation device70calculates the thermal load of the target building10based on the physical model PM. The present embodiment uses the physical model PM that can inversely calculate the corresponding input value from the air conditioning load in the target space I1. Furthermore, the present embodiment uses the physical model PM that can calculate the difference in the air conditioning load in the target space11from the difference in the input value and can also inversely calculate the difference in the corresponding input value from the difference in the air conditioning load in the target space11.

FIG.26is a configuration diagram of the physical model calculation process. As illustrated inFIG.26, the physical model calculation device70inputs an input value430to the physical model PM to calculate a first prediction air conditioning load461. The input value430includes at least the thermal property of the target building10. The first prediction air conditioning load461is a predicted air conditioning load in the target space11. Further, the physical model calculation device70transmits the first prediction air conditioning load461to the air conditioning load learning apparatus410and thus uses a difference load470received from the air conditioning load learning apparatus410to calculate a difference input value450by inverse calculation of the physical model PM. Further, the physical model calculation device70inputs the difference input value450received from the air conditioning load prediction apparatus420to the physical model PM and thus calculate the difference load470.

(2-3) Air Conditioning Load Learning Apparatus

FIG.27is a configuration diagram of an air conditioning load learning process. As illustrated inFIGS.25and27, the air conditioning load learning apparatus410primarily includes an actual load acquisition unit419, a prediction load acquisition unit413, a difference input value acquisition unit417, a first information acquisition unit411, and a learning unit414.

The air conditioning load learning apparatus410includes a control arithmetic device and a storage device. A processor such as a CPU or a GPU may be used as the control arithmetic device. The control arithmetic device reads a program stored in the storage device and performs predetermined image processing and arithmetic processing in accordance with the program. Further, the control arithmetic device can write an arithmetic result in the storage device and read information stored in the storage device in accordance with the program. The actual load acquisition unit419, the prediction load acquisition unit413, the difference input value acquisition unit417, the first information acquisition unit411, and the learning unit414are various functional blocks implemented by the control arithmetic device and the storage device.

(2-3-1) Actual Load Acquisition Unit

As illustrated inFIG.27, the actual load acquisition unit419acquires an actual air conditioning load440, which is an actual air conditioning load in the target space11inside the target building10, from the air conditioning information37in the air conditioning database80.

(2-3-2) Prediction Load Acquisition Unit

As illustrated inFIG.27, the prediction load acquisition unit413acquires the first prediction air conditioning load461that is an air conditioning load in the target space11predicted by the physical model PM from at least the thermal property of the target building10.

(2-3-3) Difference Input Value Acquisition Unit

As illustrated inFIG.27, the difference input value acquisition unit417acquires the difference input value450calculated by inverse calculation of the physical model PM using the difference load470that is the difference between the actual air conditioning load440and the first prediction air conditioning load460.

(2-3-4) First Information Acquisition Unit

As illustrated inFIG.27, the first information acquisition unit411acquires the first information I1. The first information I1is information about the operation of the target building10.

(2-3-5) Learning Unit

As illustrated inFIG.27, the learning unit414generates a learning model LM4using the first information I1as an explanatory variable and using the difference input value450as an objective variable.

(2-4) Air Conditioning Load Prediction Apparatus

FIG.28is a configuration diagram of an air conditioning load prediction process. As illustrated inFIGS.25and28, the air conditioning load prediction apparatus420primarily includes a first information acquisition unit421, a difference input value prediction unit426, a prediction difference load acquisition unit427, a prediction load acquisition unit423, and a load prediction unit425.

The air conditioning load prediction apparatus420includes a control arithmetic device and a storage device. A processor such as a CPU or a GPU may be used as the control arithmetic device. The control arithmetic device reads a program stored in the storage device and performs predetermined image processing and arithmetic processing in accordance with the program. Further, the control arithmetic device can write an arithmetic result in the storage device and read information stored in the storage device in accordance with the program. The first information acquisition unit421, the difference input value prediction unit426, the prediction difference load acquisition unit427, the prediction load acquisition unit423, and the load prediction unit425are various functional blocks implemented by the control arithmetic device and the storage device.

(2-4-1) First Information Acquisition Unit

As illustrated inFIG.28, the first information acquisition unit421acquires the first information I1in the same manner as the first information acquisition unit411in the air conditioning load learning apparatus410.

(2-4-2) Difference Input Value Prediction Unit

As illustrated inFIG.28, the difference input value prediction unit426uses the learning model LM4generated by the learning unit414to predict the difference input value450from the first information I1.

(2-4-3) Prediction Difference Load Acquisition Unit

As illustrated inFIG.28, the prediction difference load acquisition unit427acquires the difference load470predicted by the physical model PM using the predicted difference input value450as an input.

(2-4-4) Prediction Load Acquisition Unit

As illustrated inFIG.28, the prediction load acquisition unit423acquires the first prediction air conditioning load461in the same manner as the prediction load acquisition unit413in the air conditioning load learning apparatus410.

(2-4-5) Load Prediction Unit

As illustrated inFIG.28, the load prediction unit425predicts the air conditioning load (a second prediction air conditioning load462) in the target space11based on the acquired difference load470and the first prediction air conditioning load461(by adding or subtracting the difference load470to or from the first prediction air conditioning load461).

(3-1) Air Conditioning Load Learning Process

The air conditioning load learning process will be described using the flowchart ofFIG.29.

As described in Step S411, the air conditioning load learning apparatus410acquires the actual air conditioning load440from the air conditioning information37in the air conditioning database80.

After acquiring the actual air conditioning load440, the air conditioning load learning apparatus410acquires the first prediction air conditioning load461from the physical model calculation device70as described in Step S412.

After acquiring the first prediction air conditioning load461, the air conditioning load learning apparatus410calculates the difference load470, which is the difference between the actual air conditioning load440and the first prediction air conditioning load460, as described in Step S413.

After calculating the difference load470, the air conditioning load learning apparatus410acquires the difference input value450calculated by inverse calculation of the physical model PM using the difference load470as described in Step S414.

After calculating the difference input value450, the air conditioning load learning apparatus410acquires the first information11from the air conditioning information37in the air conditioning database80as described in Step S415.

After acquiring the first information I1, the air conditioning load learning apparatus410generates the learning model LM4using the first information I1as an explanatory variable and using the difference input value450as an objective variable as described in Step S416.

(3-2) Air Conditioning Load Prediction Process

The air conditioning load prediction process will be described using the flowchart ofFIG.30.

The air conditioning load prediction apparatus420acquires the first information I1from the air conditioning information37in the air conditioning database80as described in Step S421.

After acquiring the first information I1, the air conditioning load prediction apparatus420uses the learning model LM4to predict the difference input value450from the first information I1as described in Step S422.

After predicting the difference input value450, the air conditioning load prediction apparatus420acquires the difference load470predicted by the physical model PM using the difference input value450as an input as described in Step S423.

After acquiring the difference load470, the air conditioning load prediction apparatus420acquires the first prediction air conditioning load461from the physical model calculation device70as described in Step S424.

After acquiring the first prediction air conditioning load461, the air conditioning load prediction apparatus420adds or subtracts the difference load470to or from the first prediction air conditioning load461to calculate the second prediction air conditioning load462as described in Step S425.

In the air conditioning load learning apparatus410according to the present embodiment, the actual load acquisition unit419acquires the actual air conditioning load440that is an actual air conditioning load in the target space11inside the target building10. The first information acquisition unit411acquires the first information I1. The first information I1is information about the operation of the target building10. The prediction load acquisition unit413acquires the first prediction air conditioning load460that is an air conditioning load in the target space11predicted by the physical model PM from at least the thermal property of the target building10. The difference input value acquisition unit417acquires the difference input value450calculated by, inverse calculation of the physical model PM using the difference load470. The learning unit414generates the learning model LM4using the first information I1as an explanatory variable and using the difference input value450as an objective variable.

Therefore, the air conditioning load learning apparatus410associates the first information I1with the difference input value450(the value regarding the actual air conditioning load440) so as to learn the effect of the operation of the building on internal heat generation, etc. As a result, the air conditioning load learning apparatus410can finally grasp the effect of the operation of the building on the air conditioning load.

In the air conditioning load prediction apparatus420according to the present embodiment, the difference input value prediction unit426uses the learning model LM4to predict the difference input value450from the first information I1. The prediction difference load acquisition unit427acquires the difference load470predicted by the physical model PM using the predicted difference input value450as an input. The load prediction unit425predicts an air conditioning load (the second prediction air conditioning load462) in the target space I1based on the acquired difference load470and the first prediction air conditioning load461.

As a result, the air conditioning load prediction apparatus420can use the learning model LM4having learned the effect of the operation of the building on internal heat generation, and the like, to predict the air conditioning load in consideration of the effect of the operation of the building.

According to the present embodiment, the learning unit414generates the learning model LM4using the first information I1as an explanatory variable and using the difference input value450as an objective variable. However, the learning unit414may further generate the learning model LM4using the input value430to the physical model PM as an explanatory variable.

As a result, the air conditioning load learning apparatus410can generate the learning model LM4having a higher prediction accuracy.

In this case, the difference input value prediction unit426uses the learning model LM4to predict the difference input value450from the first information I1and the input value430.

Although the embodiment of the present disclosure has been described above, it is understood that various modifications may be made to forms and details without departing from the spirit and scope of the present disclosure described in claims.

A different part from the first embodiment is primarily described below. Therefore, the present embodiment is basically the same as the first embodiment except for the contents described according to the present embodiment.

(1) Overall Configuration

FIG.31is a configuration diagram of an air conditioning load learning apparatus510and an air conditioning load prediction apparatus520. As illustrated inFIG.31, the air conditioning load learning apparatus510, the air conditioning load prediction apparatus520, the physical model calculation device70, the air conditioning system30, and the air conditioning database80are communicatively coupled via the network90.

(2) Detailed Configuration

(2-1) Air Conditioning System

The air conditioning system30and the air conditioning database80are the same as those according to the first embodiment.

(2-2) Physical Model Calculation Device

The physical model calculation device70calculates the thermal load of the target building10based on the physical model PM. The present embodiment uses the physical model PM that may inversely calculate the corresponding input value from the air conditioning load in the target space11.

FIG.32is a configuration diagram of the physical model calculation process. As illustrated inFIG.32, the physical model calculation device70transmits a first input value531, which is an input value to the physical model PM, to the air conditioning load learning apparatus510and thus acquires an actual air conditioning load540corresponding to the first input value531. The physical model calculation device70uses the actual air conditioning load540to calculate a second input value532by inverse calculation of the physical model PM. Furthermore, the physical model calculation device70transmits the first input value531to the air conditioning load prediction apparatus520and thus inputs the second input value532received from the air conditioning load prediction apparatus520to the physical model PM to calculate a prediction air conditioning load560. The first input value531and the second input value532include at least the thermal property of the target building10, The prediction air conditioning load560is a predicted air conditioning load in the target space11.

(2-3) Air Conditioning Load Learning Apparatus

FIG.33is a configuration diagram of an air conditioning load learning process. As illustrated inFIGS.31and33, the air conditioning load learning apparatus510primarily includes an actual load acquisition unit519, an input value acquisition unit516, a first information acquisition unit511, and a learning unit514.

The air conditioning load learning apparatus310includes a control arithmetic device and a storage device. A processor such as a CPU or a GPU may be used as the control arithmetic device. The control arithmetic device reads a program stored in the storage device and performs predetermined image processing and arithmetic processing in accordance with the program. Further, the control arithmetic device can write an arithmetic result in the storage device and read information stored in the storage device in accordance with the program. The actual load acquisition unit519, the input value acquisition unit516, the first information acquisition unit511, and the learning unit514are various functional blocks implemented by the control arithmetic device and the storage device.

(2-3-1) Actual Load Acquisition Unit

As illustrated inFIG.33. the actual load acquisition unit519acquires, from the air conditioning information37in the air conditioning database80, the actual air conditioning load540, which is an actual air conditioning load in the target space11inside the target building10, corresponding to the first input value531acquired from the physical model calculation device70.

(2-3-2) Input Value Acquisition Unit

As illustrated inFIG.33, the input value acquisition unit516acquires the first input value531and the second input value532from the physical model calculation device70.

(2-3-3) First Information Acquisition Unit

As illustrated inFIG.33, the first information acquisition unit511acquires the first information I1. The first information I1is information about the operation of the target building10.

(2-34) Learning Unit

As illustrated inFIG.33, the learning unit514generates a learning model LM5using the first information I1and the first input value531as explanatory variables and using the second input value532as an objective variable.

(2-4) Air Conditioning Load Prediction Apparatus

FIG.34is a configuration diagram of an air conditioning load prediction process. As illustrated inFIGS.31and34, the air conditioning load prediction apparatus520primarily includes a first information acquisition unit521, an input value prediction unit528, a prediction load acquisition unit523, and an input value acquisition unit529.

The air conditioning load prediction apparatus520includes a control arithmetic device and a storage device. A processor such as a CPU or a GPU may be used as the control arithmetic device. The control arithmetic device reads a program stored in the storage device and performs predetermined image processing and arithmetic processing in accordance with the program. Further, the control arithmetic device can write an arithmetic result in the storage device and read information stored in the storage device in accordance with the program. The first information acquisition unit521, the input value prediction unit528, the prediction load acquisition unit523, and the input value acquisition unit529are various functional blocks implemented by the control arithmetic device and the storage device.

(2-4-1) First Information Acquisition Unit

As illustrated inFIG.34, the first information acquisition unit521acquires the first information I1in the same manner as the first information acquisition unit511in the air conditioning load learning apparatus510.

(2-4-2) Input Value Acquisition Unit

As illustrated inFIG.34, the input value acquisition unit529acquires the first input value531in the same manner as the input value acquisition unit516in the air conditioning load learning apparatus510.

(2-4-3) Input Value Prediction Unit

As illustrated inFIG.34, the input value prediction unit528uses the learning model LM5generated by the learning unit514to predict the second input value532from the first information I1and the first input value531.

(2-4-4) Prediction Load Acquisition Unit

As illustrated inFIG.34, the prediction load acquisition unit523acquires the prediction air conditioning load560that is an air conditioning load in the target space11predicted by the physical model PM using the predicted second input value532as an input.

(3-1) Air Conditioning Load Learning Process

The air conditioning load learning process will be described using the flowchart ofFIG.35.

As described in Step S511, the air conditioning load learning apparatus510acquires the first input value531from the physical model calculation device70.

After acquiring the first input value531, the air conditioning load learning apparatus510acquires the actual air conditioning load540corresponding to the first input value531from the air conditioning information37in the air conditioning database80as described in Step S512.

After acquiring the actual air conditioning load540, the air conditioning load learning apparatus510transmits the actual air conditioning load540to the physical model calculation device70and acquires the second input value532as described in Step S513.

After acquiring the second input value532, the air conditioning load learning apparatus510acquires the first information I1from the air conditioning information37in the air conditioning database80as described in Step S514. After calculating the difference input value350and acquiring the first information I1, the air conditioning load learning apparatus510generates the learning model LM5using the first information I1and the first input value531as explanatory variables and using the second input value532as an objective variable as described in Step S515.

(3-2) Air Conditioning Load Prediction Process

The air conditioning load prediction process will be described using the flowchart ofFIG.36.

The air conditioning load prediction apparatus520acquires the first information I1from the air conditioning information37in the air conditioning database80as described in Step S521.

After acquiring the first information I1, the air conditioning load prediction apparatus520acquires the first input value531from the physical model calculation device70as described in Step S522.

After acquiring the first input value531, the air conditioning load prediction apparatus520uses the learning model LM5to predict the second input value532from the first information I1and the first input value531as described in Step S523.

After predicting the second input value532, the air conditioning load prediction apparatus520acquires the prediction air conditioning load560that is an air conditioning load in the target space11predicted by the physical model PM using the second input value532as an input as described in Step S524.

In the air conditioning load learning apparatus510according to the present embodiment, the actual load acquisition unit519acquires the actual air conditioning load540that is an actual air conditioning load in the target space11inside the target building10. The first information acquisition unit511acquires the first information I1. The first information I1is information about the operation of the target building10. The input value acquisition unit516acquires the first input value531and the second input value532from the physical model calculation device70. The first input value531is an input value including at least the thermal property of the target building10to the physical model PM that outputs the prediction air conditioning load560that is a predicted air conditioning load in the target space11. The second input value532is an input value calculated by inverse calculation of the physical model PM using the actual air conditioning load540. The learning unit514generates the learning model LM5using the first information I1and the first input value531as explanatory variables and using the second input value532as an objective variable.

Therefore, the air conditioning load learning apparatus510associates the first information I1with the second input value532(the value regarding the actual air conditioning load540) so as to learn the effect of the operation of the building on internal heat generation, etc. As a result, the air conditioning load learning apparatus510can finally grasp the effect of the operation of the building on the air conditioning load.

In the air conditioning load prediction apparatus520according to the present embodiment, the input value prediction unit528uses the learning model LM5to predict the second input value532from the first information I1and the first input value531, The prediction load acquisition unit523acquires the prediction air conditioning load560that is an air conditioning load in the target space11predicted by the physical model PM using the second input value532as an input.

As a result, the air conditioning load prediction apparatus520can use the learning model LM5having learned the effect of the operation of the building on internal heat generation, and the like, to predict the air conditioning load in consideration of the effect of the operation of the building.

Although the embodiment of the present disclosure has been described above, it is understood that various modifications may be made to forms and details without departing from the spirit and scope of the present disclosure described in claims.

REFERENCE SIGNS LIST

110,210,310,410,510Air conditioning load learning apparatus

111,211,311,411,511First information acquisition unit

112,212Second information acquisition unit

113,213,323,413,523Prediction load acquisition unit

119,219,319,419,519Actual load acquisition unit

120,220,320,420,520Air conditioning load prediction apparatus

124Difference load prediction unit

125,225,425Load prediction unit

140,240,340,440,540Actual air conditioning load

161,261,360,461,560Prediction air conditioning load

316,516Input value acquisition unit

326,426Difference input value prediction unit

331,531First input value

332,532Second input value

333Third input value

350,450Difference input value

417Difference input value acquisition unit

427Prediction difference load acquisition unit

528Input value prediction unit

11First information

12Second information

PM Physical model

CITATION LIST

Patent Literature