Control device for air conditioner, control method for air conditioner and program
The air conditioner control device addresses the issue of overcooling or overheating during demand response periods by using a temperature prediction model to set a target temperature, ensuring the indoor temperature remains within a comfortable range and reducing power consumption.
Patent Information
- Application Number
- JP2023172421
- Authority / Receiving Office
- JP · JP
- Patent Type
- Applications
- Current Assignee / Owner
- Filing Date
- 2023-10-04
- Publication Date
- 2025-04-16
AI Technical Summary
Conventional air conditioner control methods during demand response periods often result in overcooling or overheating, leading to discomfort and inefficient power consumption, as they fail to accurately predict and adjust for varying factors such as DR period length, power consumption reduction rate, and outdoor air temperature.
An air conditioner control device that includes a demand response information acquisition unit, a prediction unit using a temperature prediction model, and a setting unit that sets a target temperature to ensure the end room temperature remains within a predetermined comfortable temperature range, thereby minimizing overcooling or overheating before and during the demand response period.
This solution effectively suppresses indoor temperature deviations from a comfortable range during demand response periods, minimizing unnecessary power consumption and maintaining comfort by accurately predicting and adjusting for temperature changes.
Smart Images
Figure 2025063351000001_ABST
Abstract
Description
[Technical field]
[0001] The present disclosure relates to an air conditioner control device, an air conditioner control method, and a program. [Background technology]
[0002] In order to respond to fluctuations in the balance between power supply and demand, a method called demand response (DR) is used in which consumers adjust their power usage. For example, when excess demand is predicted, the supplier issues a DR (downward DR) command to consumers, which requests them to reduce their power usage during the period of excess demand. In air conditioners and the like, control is performed to cut or reduce power consumption during the DR period in accordance with this DR command. This reduces the air conditioner's cooling and heating capacity during the DR period, and the indoor temperature may deviate from the set temperature, reducing comfort. For this reason, a technology has been devised that performs supercooling by lowering the set temperature by a certain temperature during cooling and superheating by raising the set temperature by a certain temperature during heating before performing DR control (see Patent Document 1 and Patent Document 2). [Prior art documents] [Patent documents]
[0003] [Patent Document 1] Patent No. 6375923 [Patent Document 2] Patent No. 2913584 Summary of the Invention [Problem to be solved by the invention]
[0004] However, how the indoor temperature changes during the DR period varies depending on various factors such as the length of the DR period, the rate of reduction in power consumption, and the outdoor temperature. As a result, with conventional technology that performs control by lowering (super cooling) or raising (super heating) the temperature by a certain amount below a preset temperature, when there is a large change in the indoor temperature due to the influence of the outdoor temperature, for example, supercooling or superheating may be insufficient, and the indoor temperature may deviate from the set temperature during the DR period, making it difficult to maintain comfort. On the other hand, when there is a small change in the indoor temperature, for example, supercooling or superheating may become excessive, resulting in wasted electricity.
[0005] An object of the present disclosure is to provide an air conditioner control device, an air conditioner control method, and a program that can reduce total power consumption throughout all time periods by minimizing overcooling or overheating before demand response while preventing the indoor temperature during a demand response period from deviating from a comfortable temperature range. [Means for solving the problem]
[0006] According to one aspect of the present disclosure, an air conditioner control device includes a demand response information acquisition unit that acquires demand response information including a start time and an end time of a demand response, a prediction unit that predicts an end indoor temperature at an end time of the demand response using a temperature prediction model that outputs a prediction of the progress of the indoor temperature due to the demand response when a start indoor temperature and a start outdoor air temperature at the start time of the demand response are input, and a setting unit that sets a target temperature by adjusting the start indoor temperature so that the end indoor temperature is within a predetermined first temperature range.
[0007] According to one aspect of the present disclosure, a method for controlling an air conditioner includes the steps of acquiring demand response information including a start time and an end time of a demand response, predicting an end indoor temperature at an end time of the demand response using a temperature prediction model that outputs a prediction of the progress of the indoor temperature due to the demand response when a start indoor temperature and a start outdoor temperature at the start time of the demand response are input, and setting a target temperature by adjusting the start indoor temperature so that the end indoor temperature is within a predetermined first temperature range.
[0008] According to one aspect of the present disclosure, the program causes an air conditioner control device to execute the following steps: acquiring demand response information including a start time and an end time of the demand response; predicting an end indoor temperature at an end time of the demand response using a temperature prediction model that outputs a prediction of the progress of the indoor temperature due to the demand response when a start indoor temperature and a start outdoor temperature at the start time of the demand response are input; and setting a target temperature by adjusting the start indoor temperature so that the end indoor temperature is within a predetermined first temperature range. Effect of the Invention
[0009] According to the above aspect, it is possible to prevent the indoor temperature during the demand response period from deviating from a comfortable temperature range, while minimizing overcooling or overheating before the demand response, thereby reducing the total power consumption throughout all time periods. [Brief description of the drawings]
[0010] [Figure 1] 1 is a diagram illustrating an example of an air conditioning system according to a first embodiment. [Diagram 2] 4 is a flowchart showing the flow of a learning method for a temperature prediction model according to the first embodiment. [Diagram 3] FIG. 4 is a diagram showing an example of learning data of the temperature prediction model according to the first embodiment. [Figure 4]4 is a flowchart showing the flow of an air conditioner control method according to the first embodiment. [Diagram 5] FIG. 2 is a diagram for explaining a method for controlling an air conditioner according to the first embodiment. [Figure 6] FIG. 2 is a first diagram showing an example of a prediction of a transition of an indoor temperature according to the first embodiment. [Figure 7] FIG. 2 is a second diagram showing an example of a prediction of a transition of an indoor temperature according to the first embodiment. [Figure 8] FIG. 11 is a third diagram showing an example of a prediction of a transition of an indoor temperature according to the first embodiment. [Figure 9] FIG. 11 is a diagram showing an example of learning data of a temperature prediction model according to the second embodiment. [Figure 10] FIG. 11 is a diagram for explaining a method for controlling an air conditioner according to a second embodiment. [Figure 11] FIG. 11 is a diagram illustrating an example of an air conditioning system according to a third embodiment. [Figure 12] FIG. 13 is a diagram showing an example of learning data of a humidity prediction model according to the third embodiment. [Figure 13] 10 is a flowchart showing the flow of an air conditioner control method according to a third embodiment. [Figure 14] FIG. 11 is a diagram illustrating an example of an air conditioning system according to a fourth embodiment. [Figure 15] 13 is a flowchart showing the flow of a learning method for a temperature prediction model according to a fourth embodiment. [Figure 16] FIG. 13 is a diagram showing an example of learning data of a humidity prediction model according to the fourth embodiment. [Figure 17] FIG. 13 is a diagram showing an example of weighting of air conditioners according to the fourth embodiment. [Figure 18] 10 is a flowchart showing the flow of an air conditioner control method according to a fourth embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0011] <First embodiment> The first embodiment will be described in detail below with reference to FIGS.
[0012] (Overall configuration of air conditioning system) FIG. 1 is a diagram showing an example of an air conditioning system according to a first embodiment. As shown in FIG. 1, the air conditioning system 100 includes an air conditioner 1 and a server 20. The air conditioner 1 includes an outdoor air temperature sensor 2, an indoor air temperature sensor 3, and a control device 10. The server 20 is, for example, a server of an electricity supplier, and when an excess demand for electricity is predicted for a certain period in the future, the server 20 distributes a demand response (DR) command to each consumer to instruct the consumer to limit electricity consumption for this period. The DR command includes a start time (start date and time) and an end time (end date and time) of the DR. The DR command is distributed to each consumer before the implementation of the DR. Note that, although FIG. 1 illustrates a configuration in which the control device 10 is installed inside the air conditioner 1, in other embodiments, some functions of the control device 10 may be installed on an external server side such as the server 20.
[0013] The outdoor air temperature sensor 2 is provided, for example, in the outdoor unit of the air conditioner 1. The indoor temperature sensor 3 is provided, for example, in the indoor unit of the air conditioner 1. Note that the outdoor air temperature sensor 2 and the indoor temperature sensor 3 may be provided in other locations as long as they are capable of measuring the outdoor air temperature and the indoor temperature, respectively.
[0014] The control device 10 is connected to an outdoor air temperature sensor 2 and an indoor temperature sensor 3, and acquires the outdoor air temperature and indoor temperature measured by each sensor. The control device 10 normally controls the air conditioner 1 so that the indoor temperature becomes the set temperature set by the user. The control device 10 is also communicably connected to a server 20 via a network NW. During the DR period, the control device 10 controls the air conditioner 1 by limiting power consumption. The control device 10 also controls the air conditioner 1 to supercool or superheat before the DR period so that the indoor temperature during the DR period does not deviate from the set temperature.
[0015] (Functional configuration of the control device) The control device 10 includes a processor 11, a memory 12, a storage 13, and a communication interface 14. The processor 11 operates according to a predetermined program to cause the control device 10 to perform various functions. The functions of the processor 11 will be described later. The memory 12 has a memory area necessary for the operation of the processor 11. The storage 13 is a so-called auxiliary storage device, such as a hard disk drive (HDD), a solid state drive (SSD), or an EEPROM. The storage 13 stores data that each part of the processor 11 acquires, generates, and refers to during processing. The communication interface 14 is an interface for transmitting and receiving various data, signals, and the like between the outdoor air temperature sensor 2, the indoor temperature sensor 3, each part of the air conditioner 1, and the server 20.
[0016] Next, a description will be given of the functions of the processor 11. The processor 11 performs the functions of a demand response information acquisition unit 110, a sensor information acquisition unit 111, a prediction unit 112, a setting unit 113, a control unit 114, and a learning unit 115 by operating according to a predetermined program.
[0017] The demand response information acquisition unit 110 (DR information acquisition unit 110) acquires demand response information (DR information) including a start time and an end time of DR. The DR information acquisition unit 110 may further acquire a demand response rate (DR rate) representing a reduction rate of power consumption during a DR period as one of the DR information. The DR rate is, for example, a reduction rate with respect to rated power consumption, and when the DR rate is 50%, the power consumption is reduced to 50% of the rated power consumption and is operated, and when the DR rate is 100%, the power consumption is set to zero (stopped). In other words, the larger the value of the DR rate, the smaller the power consumption of the air conditioner 1. The DR rate may be, for example, a fixed value designated in advance by a user. In this case, the DR information acquisition unit 110 acquires the DR rate designated in advance by the user from the storage 13. In addition, the DR rate may be a value designated by the supplier according to a supply and demand forecast of power. In this case, the DR rate is included in a DR command distributed by the supplier.
[0018] The sensor information acquisition unit 111 acquires the outdoor temperature and the indoor temperature from the outdoor temperature sensor 2 and the indoor temperature sensor 3 .
[0019] When the start indoor temperature and start outdoor temperature at the start time of DR are input, the prediction unit 112 predicts the end indoor temperature at the end time of DR using a temperature prediction model M1 that outputs a transition prediction of the indoor temperature according to DR.
[0020] The setting unit 113 sets a target temperature by adjusting the starting indoor temperature so that the ending indoor temperature is within a predetermined first temperature range. The first temperature range indicates a temperature range in which the user feels comfortable, and is, for example, a range of ±α degrees of the set temperature set by the user.
[0021] The control unit 114 controls the air conditioner 1 (DR control) by reducing power consumption at a predetermined reduction rate during the DR period based on the DR information delivered from the server 20. Furthermore, before the start of DR, the control unit 114 controls the air conditioner 1 so that the indoor temperature becomes the target indoor temperature set by the setting unit 113 at the start time of DR. Furthermore, after the end of DR, the control unit 114 controls the air conditioner 1 so that the indoor temperature becomes the set temperature set by the user.
[0022] The learning unit 115 learns the temperature prediction model M1 based on the time-series data of the outdoor temperature and the indoor temperature acquired by the sensor information acquisition unit 111 during DR control or when the operation is stopped.
[0023] (How to train a temperature prediction model) Fig. 2 is a flowchart showing the flow of a learning method for a temperature prediction model according to the first embodiment. Fig. 3 is a diagram showing an example of learning data for the temperature prediction model according to the first embodiment. Here, the flow of a learning method for the temperature prediction model M1 by the control device 10 will be described with reference to Figs. 2 and 3.
[0024] First, the control device 10 collects learning data D1 of the temperature prediction model M1 (step S101). As shown in FIG. 3, the learning data D1 includes time series data of the indoor temperature acquired from the indoor temperature sensor 3 during the DR control, the outdoor air temperature acquired from the outdoor air temperature sensor 2, and the DR rate. The time series data of the indoor temperature is data in which the indoor temperature is associated with the elapsed time (minutes) from the start of the DR control. The outdoor air temperature is the outdoor air temperature at the start of the DR control. For example, the learning unit 115 collects time series data of the outdoor air temperature and the indoor temperature acquired by the sensor information acquisition unit 111 during the DR control. In addition, when the DR control is ended, the learning unit 115 records the learning data D1 including the collected time series data of the outdoor air temperature and the indoor temperature in the storage 13. Note that, if the DR rate is not a fixed value, the learning unit 115 records the learning data D1 for each DR rate (a1%, a2%, a3%, ...). At this time, the learning data D1 also includes information on the DR rate. Furthermore, the learning unit 115 may collect data when the operation is stopped as the learning data D1 indicating a DR rate of 100% (power consumption of zero). Although an example in which the learning data D1 includes time series data of the room temperature during cooling is shown in Fig. 3, in reality, the learning data D1 also includes time series data of the room temperature during heating.
[0025] Next, the learning unit 115 learns the temperature prediction model M1 based on the multiple learning data D1 recorded in the storage 13 (step S102). When the learning unit 115 receives an input of the start outdoor temperature and start indoor temperature at the start time of DR, the learning unit 115 learns the temperature prediction model M1 so as to output a predicted transition of the indoor temperature during DR control. Furthermore, when the learning data D1 includes a DR rate, the learning unit 115 further receives an input of the DR rate and learns the temperature prediction model M1 so as to output a predicted transition of the indoor temperature when DR control is performed at the specified DR rate. The learned temperature prediction model M1 is recorded in the storage 13.
[0026] The learning unit 115 may include in the learning data D1 not only the outdoor air temperature at the start of the DR control, but also data (time series data) of the outdoor air temperature at each time point during the DR control. In this case, when the start time of the DR is input, the learning unit 115 predicts whether the outdoor air temperature will tend to rise or fall during the DR period, and learns the temperature prediction model M1 so as to predict the transition of the indoor temperature according to the change trend of the outdoor air temperature. Alternatively, when the learning unit 115 inputs a predicted value of the outdoor air temperature for each time of the DR period, the learning unit 115 may learn the temperature prediction model M1 so as to predict the transition of the indoor temperature according to the change of the outdoor air temperature (predicted value). The predicted value of the outdoor air temperature may be weather forecast information obtained from an external server (not shown). In this way, the learning unit 115 can further improve the prediction accuracy of the indoor temperature by the temperature prediction model M1.
[0027] The learning unit 115 repeatedly collects learning data D1 and learns the temperature prediction model M1 every time DR control (or operation stop) is performed on the air conditioner 1.
[0028] (Air conditioner control method) FIG. 4 is a flowchart showing the flow of the method for controlling an air conditioner according to the first embodiment. FIG. 5 is a diagram for explaining the method for controlling an air conditioner according to the first embodiment. FIG. 6 is a first diagram showing an example of a prediction of a transition of an indoor temperature according to the first embodiment. FIG. 7 is a second diagram showing an example of a prediction of a transition of an indoor temperature according to the first embodiment. FIG. 8 is a third diagram showing an example of a prediction of a transition of an indoor temperature according to the first embodiment. Here, the flow of the method for controlling the air conditioner 1 by the control device 10 will be explained with reference to FIGS. 4 to 8.
[0029] First, the DR information acquisition unit 110 acquires DR information (step S111). The DR information acquisition unit 110 acquires the DR start time and end time included in the DR command delivered from the supplier as DR information. In addition, if a DR rate is specified by the DR command, the DR information acquisition unit 110 may further acquire this DR rate as DR information.
[0030] Furthermore, the setting unit 113 sets the start indoor temperature and start outdoor temperature at the start of DR (step S112). The start indoor temperature of DR is, for example, a set temperature set by the user for the first time. The start outdoor temperature of DR may be the current outdoor temperature acquired by the sensor information acquisition unit 111 from the outdoor temperature sensor 2 if the start time of DR is less than a predetermined time from the present. Furthermore, if the start time of DR is away from the present by a predetermined time or more, the start outdoor temperature may be set based on weather forecast information acquired from an external server (not shown).
[0031] Next, the prediction unit 112 predicts the indoor end temperature at the end time of the DR using the temperature prediction model M1 (step S113). As shown in FIG. 5, the prediction unit 112 inputs the indoor start temperature and the outdoor start temperature to the temperature prediction model M1. Then, the temperature prediction model M1 outputs a transition prediction of the indoor temperature. For example, as shown in the example of FIG. 5, the temperature prediction model M1 outputs transition prediction data that predicts how the indoor start temperature will transition with elapsed time (minutes). The prediction unit 112 refers to the output transition prediction of the indoor temperature, and predicts what the indoor temperature (indoor end temperature) will be when the DR start time t0 has elapsed to the end time t1.
[0032] In addition, if the temperature prediction model M1 is trained for each DR rate, the DR rate may be further input to the temperature prediction model M1, and a predicted change in the indoor temperature when operating at this DR rate may be obtained as an output. Furthermore, if the temperature prediction model M1 further trains time-series data on the outdoor air temperature, the start time of DR may be further input to the temperature prediction model M1. Then, the temperature prediction model M1 predicts the change trend (increasing or decreasing trend) of the outdoor air temperature from the start time, and predicts the change in the indoor temperature according to this change trend of the outdoor air temperature. Also, instead of the start time of DR, a predicted value of the outdoor air temperature for each time during the DR period may be input to the temperature prediction model M1. The predicted value of the outdoor air temperature is obtained, for example, as weather forecast information from an external server (not shown). Then, the temperature prediction model M1 predicts the change in the indoor temperature according to the change in the outdoor air temperature (predicted value).
[0033] Next, the setting unit 113 determines whether the predicted indoor end temperature is within the first temperature range (step S114). FIG. 6 shows an example of a predicted transition of the indoor temperature when the set temperature T0 is set as the DR start indoor temperature during cooling operation. The first temperature range is a range of ±α degrees from the set temperature T0 (a range from the lower limit temperature TL to the upper limit temperature TU). In the example of FIG. 6, when the air conditioner 1 is operated at a DR rate specified during the DR period (during DR control), the indoor end temperature exceeds the first temperature range (upper limit temperature TU). In this case, there is a possibility that the user will not be able to spend the DR period comfortably. In this way, when the indoor end temperature exceeds the first temperature range (step S114; NO), the setting unit 113 changes the indoor start temperature (step S116). For example, the setting unit 113 lowers the indoor start temperature by 1 degree during cooling and raises the indoor start temperature by 1 degree during heating.
[0034] Furthermore, when the setting unit 113 changes the start indoor temperature, the prediction unit 112 uses the changed start indoor temperature to predict the end indoor temperature again (step S113). Then, the setting unit 113 re-determines whether the predicted end indoor temperature is within the first temperature range (step S114). The setting unit 113 and the prediction unit 112 repeat the processes of steps S116, S113, and S114 until the end indoor temperature falls within the first temperature range.
[0035] FIG. 7 shows an example of predicted transition of the indoor temperature when the start indoor temperature is lowered (for example, by 1 degree) below the user's set temperature T0 during cooling operation. In the example of FIG. 7, the end indoor temperature is within the first temperature range (upper limit temperature TU) by supercooling the indoor temperature before the start of DR. Therefore, even if the air conditioning capacity of the air conditioner 1 is reduced (or the air conditioner 1 is stopped) due to DR, the user can be comfortable. In this way, when the end indoor temperature is within the first temperature range (step S114; YES), the setting unit 113 sets the start indoor temperature used for the prediction as the target temperature before the start of DR (step S115). In this way, the setting unit 113 can suppress the indoor temperature during the DR period from changing beyond the first temperature range (the temperature range in which the user can be comfortable) while minimizing the supercooling or superheating before the start of DR.
[0036] It is preferable that the setting unit 113 sets, as the target temperature, the one that the end indoor temperature is closest to the upper limit temperature TU during cooling, and the one that the end indoor temperature is closest to the lower limit temperature TL during heating, among the multiple start indoor temperatures. In this way, it is possible to suppress excessive supercooling or superheating before the start of DR, and as a result, to suppress an increase in the total power consumption throughout the entire time period in which the air conditioner 1 is operated. For comparison, FIG. 8 shows an example in which the temperature to be lowered during supercooling is constant (for example, 2 degrees) as in the conventional technology. As shown in FIG. 8, by significantly lowering the start indoor temperature, the end indoor temperature still has a margin before exceeding the comfortable temperature range (upper limit temperature TU). In other words, it is considered that the supercooling before the start of DR is excessive. In contrast, in this embodiment, as shown in FIG. 7, the temperature to be lowered during supercooling before the start of DR can be set to a necessary minimum value according to the relationship with the outdoor air temperature, etc., rather than a fixed value, so that the power consumption during supercooling is less than that of the conventional technology (FIG. 8). Therefore, in this embodiment, the total power consumption throughout all time periods in which the air conditioner 1 is in operation can be reduced compared to the conventional technology.
[0037] The setting unit 113 may limit the target temperature so that the indoor temperature falls within a second temperature range during supercooling or superheating before the start of DR. For example, the setting unit 113 sets the second temperature range to the same range as the first temperature range (a range of ±α degrees from the set temperature). In steps S113 to S116, the setting unit 113 changes the start indoor temperature within the second temperature range and searches for a start indoor temperature (target temperature) at which the end indoor temperature falls within the first temperature range. In this way, the setting unit 113 can prevent the indoor temperature from becoming a temperature that would impair the comfort of the user due to supercooling or superheating before the start of DR.
[0038] Furthermore, the setting unit 113 may expand the second temperature range when the starting indoor temperature is within the second temperature range and the ending indoor temperature exceeds the first temperature range. In this case, the range in which the second temperature range can be expanded may be set in advance as an allowable expansion range of the second temperature range so that the user does not feel uncomfortable. For example, when the first temperature range is set to ±2 degrees from the set temperature, the setting unit 113 sets the allowable expansion range of the second temperature range to ±3 degrees from the set temperature. In this way, by allowing the indoor temperature to be slightly lower or higher to some extent in the short-term supercooling or superheating before the DR, it is possible to more reliably prevent the indoor temperature from exceeding the first temperature range during the long-term DR period.
[0039] Furthermore, the control unit 114 controls the air conditioner 1 so that the indoor temperature reaches the target temperature before the start time of DR (step S117). Thereafter, during the period from the start time to the end time of DR, the control unit 114 controls the air conditioner 1 so that the power consumption is reduced by the specified DR rate. In this way, the control device 10 supercools or superheats the indoor temperature before the start of DR, thereby making it possible to prevent the indoor temperature from deviating from the first temperature range (a comfortable temperature range) during the DR period.
[0040] (Action, effect) As described above, the control device 10 of the air conditioner 1 in this embodiment includes a DR information acquisition unit 110 that acquires DR information including the start time and end time of DR, a prediction unit 112 that predicts the end indoor temperature at the end time of DR using a temperature prediction model M1 that outputs a prediction of the change in indoor temperature due to DR when the start indoor temperature and start outdoor temperature at the start time of DR are input, and a setting unit 113 that sets a target temperature for the indoor temperature to be reached by the start time of DR so that the end indoor temperature is within a predetermined first temperature range.
[0041] In this way, the control device 10 can minimize overcooling or overheating before DR while preventing the indoor temperature during the DR period from deviating from a comfortable temperature range (first temperature range) based on the indoor temperature and outdoor temperature at the start of DR. As a result, the total power consumption of the air conditioner 1 throughout all time periods in which it is operating can be reduced.
[0042] Moreover, the setting unit 113 sets, as the target temperature, the start indoor temperature that falls within a preset second temperature range, among the start indoor temperatures at which the end indoor temperature falls within the first temperature range.
[0043] In this manner, the control device 10 can suppress the indoor temperature from changing to such an extent that comfort is impaired not only during the DR period but also during overcooling or overheating before the DR.
[0044] In addition, when the start time is input in addition to the starting indoor temperature and the starting outdoor temperature, the temperature prediction model M1 is trained to predict the trend of change in the outdoor temperature after the start time and output a prediction of the indoor temperature trend according to the predicted trend of change in the outdoor temperature.
[0045] In this way, the control device 10 can predict whether the outdoor air temperature will tend to increase or decrease during the DR period, thereby improving the prediction accuracy of the ending indoor temperature.
[0046] In addition, the temperature prediction model M1 is trained to output a prediction of the indoor temperature trend according to the change in the outdoor temperature at each time, when the starting indoor temperature, the starting outdoor temperature, and the outdoor temperature at each time from the start time to the end time are input.
[0047] In this way, the control device 10 can improve the accuracy of predicting the indoor temperature at the end of the DR period by taking into account the change in the outdoor air temperature during the DR period. The outdoor air temperature during the DR period may be obtained from weather forecast information provided by an external server.
[0048] In addition, the DR information further includes a DR rate, and the temperature prediction model M1 is trained to output a prediction of the indoor temperature trend according to the DR rate when the DR rate is input in addition to the starting indoor temperature and the starting outdoor temperature.
[0049] In this way, even if a different DR rate is specified by a user or a supplier, the control device 10 can appropriately set the target temperature so that the indoor temperature during the DR period does not deviate from a comfortable temperature range (first temperature range) while minimizing supercooling or superheating before DR according to the DR rate.
[0050] Furthermore, when there is no target temperature within the second temperature range that will result in the room temperature at the end being within the first temperature range, the setting unit 113 expands the second temperature range to set the target temperature.
[0051] In this way, the control device 10 allows the indoor temperature to be slightly lower or higher during the short period of supercooling or superheating before DR, and can more reliably prevent the indoor temperature from exceeding the first temperature range during the long DR period.
[0052] <Second embodiment> Hereinafter, a second embodiment of the present disclosure will be described with reference to Figures 9 and 10. Among the configurations of the second embodiment, configurations common to the first embodiment will be given the same reference numerals and descriptions thereof will be omitted.
[0053] (How to train a temperature prediction model) FIG. 9 is a diagram showing an example of learning data of a temperature prediction model according to the second embodiment. As shown in FIG. 9, in step S101 of FIG. 2, the learning unit 115 of this embodiment learns a temperature prediction model M1 by including time series data of the room temperature before DR control in addition to time series data of the room temperature during DR control in the learning data D1. FIG. 9 shows an example of the learning data D1 including time series data during cooling, but in reality, the learning data D1 also includes time series data during heating. In the example of FIG. 9, time t0 is the time when DR control is started, and times t-1, t-2, and t-3 are the times when the target temperature for supercooling is reached.
[0054] In addition, when the start outdoor temperature and the start indoor temperature are input in step S102 of FIG. 2, the learning unit 115 learns the temperature prediction model M1 so as to output a predicted transition of the indoor temperature during the DR period according to how much supercooling or superheating was performed at the start indoor temperature before the DR. As shown in FIG. 9, for example, during cooling, by making the time of supercooling sufficiently long before the DR, the temperature of the building structure also drops, and the building structure can function as a refrigerant during the DR period. In other words, the heat absorbed by the building structure due to supercooling or superheating can be used as a heat source during the DR period. The learning unit 115 can make the temperature prediction model M1 learn the influence of the heat absorbed by the building structure on the transition of the indoor temperature during the DR period by further including the supercooling period or the superheating period before the DR in the learning data D1.
[0055] (Air conditioner control method) Fig. 10 is a diagram for explaining a method for controlling an air conditioner according to the second embodiment. In step S113 of Fig. 4, the prediction unit 112 inputs the start time outdoor air temperature and the start time indoor temperature to the temperature prediction model M1. Then, as shown in Fig. 10, the temperature prediction model M1 outputs predicted transitions P1, P2, and P3 of the indoor temperature after the DR start time t0 when supercooling is performed t-1 minutes, t-2 minutes, and t-3 minutes before the DR.
[0056] In steps S114 to S116 of FIG. 4, the setting unit 113 sets a combination of a target temperature and a target time indicating how long before the DR the target temperature should be reached based on a combination of the start indoor temperature and the supercooling period in which the indoor temperature at the end time t1 is within the first temperature range (lower limit temperature TL to upper limit temperature TU). In the example of FIG. 10, for the indoor temperature transition predictions P2 and P3, the end indoor temperature at the end time t1 is equal to or lower than the upper limit temperature TU. Therefore, the setting unit 113 sets this start indoor temperature as the target temperature, and sets t-2 minutes or t-3 minutes before the DR start time as the target time for reaching this target temperature. The setting unit 113 sets the target time based on the shortest of the valid t-2 minutes and t-3 minutes. In the example of FIG. 10, the setting unit 113 sets t-2 minutes before the DR start time as the target time. Although FIG. 10 shows an example of cooling, the same process is performed during heating.
[0057] (Action, effect) As described above, in the control device 10 of this embodiment, the temperature prediction model M1 is further trained to output a prediction of the indoor temperature trend corresponding to the period during which supercooling or superheating was performed at the starting indoor temperature before demand response when the starting indoor temperature and the starting outdoor temperature are input, and the setting unit 113 sets the target temperature and the target time indicating how long before the start time the target temperature should be reached based on the combination of the starting indoor temperature and the period during which the ending indoor temperature is within the first temperature range.
[0058] In this manner, the control device 10 can more reliably prevent the indoor temperature during the DR period from deviating from the comfortable temperature range (first temperature range) while minimizing overcooling or overheating before the DR.
[0059] <Third embodiment> Hereinafter, a second embodiment of the present disclosure will be described with reference to Figures 11 to 13. Among the configurations of the second embodiment, configurations common to the first embodiment will be given the same reference numerals and descriptions thereof will be omitted.
[0060] (Overall configuration of air conditioning system) Fig. 11 is a diagram showing an example of an air conditioning system according to the third embodiment. As shown in Fig. 11, the air conditioner 1 according to this embodiment further includes a humidity sensor 4. The humidity sensor 4 is provided, for example, in an indoor unit of the air conditioner 1. The control device 10 (sensor information acquisition unit 111) is connected to the humidity sensor 4 and acquires the indoor humidity measured by the humidity sensor 4.
[0061] (Functional configuration of the control device) When the start humidity at the DR start time is input, the prediction unit 112 further predicts the end humidity at the DR end time using a humidity prediction model M2 that outputs a predicted transition of the indoor humidity by DR.
[0062] When there is no target temperature within the second temperature range at which the end indoor temperature is within the first temperature range, the setting unit 113 sets a target humidity to be reached by the DR start time so that the end humidity is equal to or lower than a predetermined upper limit humidity.
[0063] (How to train a humidity prediction model) FIG. 12 is a diagram showing an example of learning data of the humidity prediction model according to the third embodiment. In step S101 of FIG. 2, the learning unit 115 further collects learning data D2 of the humidity prediction model M2. As shown in FIG. 12, the learning data D2 includes, for example, time series data of humidity acquired from the humidity sensor 4 during DR control. The time series data of humidity is data in which humidity (%) is associated with the elapsed time (minutes) from the start of DR control. For example, the learning unit 115 collects time series data of humidity acquired by the sensor information acquisition unit 111 during DR control. Furthermore, when the DR control is ended, the learning unit 115 records the learning data D2 including the collected time series data of humidity in the storage 13. Furthermore, data at the time of operation stop may be collected as the learning data D2. Note that, although an example is shown in FIG. 12 in which the learning data D2 includes time series data of the room temperature during cooling, in reality, the learning data D2 also includes time series data of the room temperature during heating.
[0064] 2, the learning unit 115 further learns the humidity prediction model M2 based on the multiple pieces of learning data D2 recorded in the storage 13. When the learning unit 115 receives an input of the start humidity at the start time of the DR, the learning unit 115 learns the humidity prediction model M2 so as to output a predicted transition of the indoor humidity during the DR control.
[0065] The learning unit 115 repeatedly collects learning data D2 and learns the humidity prediction model M2 every time DR control (or operation stop) is performed on the air conditioner 1.
[0066] (Air conditioner control method) Fig. 13 is a flowchart showing the flow of a method for controlling an air conditioner according to the third embodiment. Here, the flow of a method for controlling the air conditioner 1 by the control device 10 will be described with reference to Fig. 13. Note that the example in Fig. 13 shows a control method during cooling.
[0067] First, the DR information acquisition unit 110 acquires DR information (step S311). The setting unit 113 sets the start indoor temperature and the start outdoor temperature at the start of DR (step S312). Next, the prediction unit 112 predicts the end indoor temperature at the end time of DR using the temperature prediction model M1 (step S313). The setting unit 113 also determines whether the predicted end indoor temperature is within the first temperature range (step S314). If the predicted end indoor temperature is within the first temperature range (step S314: YES), the start indoor temperature used for the prediction is set as the target temperature (step S315). These processes are the same as the processes of the first embodiment (steps S111 to S115 in FIG. 4). Thereafter, the control unit 114 controls the air conditioner 1 so that the indoor temperature becomes the target temperature before the start time of DR (step S318). This process is the same as the process of the first embodiment (step S117 in FIG. 4).
[0068] Furthermore, if the predicted end indoor temperature exceeds the first temperature range (step S314: NO), the setting unit 113 determines whether the start indoor temperature can be changed (step S316). For example, if the start indoor temperature falls within the second temperature range even if the start indoor temperature is changed (lowered), the setting unit 113 determines that the start indoor temperature can be changed (step S316: YES). In this case, the setting unit 113 changes (lowers) the start indoor temperature (step S317) and returns to step S313. This process is the same as the process in the first embodiment (step S116 in FIG. 4).
[0069] On the other hand, when the start indoor temperature is changed (decreased), if the start indoor temperature does not fall within the second temperature range, the setting unit 113 determines that the start indoor temperature cannot be changed (step S316: NO). In this case, it is difficult to prevent the end indoor temperature from exceeding the first temperature range by adjusting the start indoor temperature. However, even if the indoor temperature is the same, the user is likely to feel hot when the humidity is high and is likely to feel cool when the humidity is low. Therefore, when it is difficult to keep the end indoor temperature low by only adjusting the indoor temperature in this way, the control device 10 causes the air conditioner 1 to perform a dehumidification operation before the start of DR so that the humidity during the DR period is low. Specifically, first, the setting unit 113 sets the start indoor temperature (lower limit value of the second temperature range) used in the immediately preceding prediction as the target temperature (step S319). The setting unit 113 also sets the start humidity at the start of DR (step S320). The start humidity of the DR may be the current humidity acquired by the sensor information acquisition unit 111 from the humidity sensor 4, for example, if the start time of the DR is less than a predetermined time from the present. Also, if the start time of the DR is a predetermined time or more away from the present, the start humidity may be set based on weather forecast information acquired from an external server (not shown).
[0070] Next, the prediction unit 112 predicts the end humidity at the end time of the DR using the humidity prediction model M2 (step S321). The prediction unit 112 inputs the start humidity to the humidity prediction model M2. Then, the humidity prediction model M2 outputs a humidity transition prediction.
[0071] Next, the setting unit 113 determines whether the predicted end humidity exceeds a predetermined upper limit humidity (step S322). If the end humidity exceeds the upper limit humidity (step S322; YES), the setting unit 113 changes the start humidity (step S323). For example, the setting unit 113 reduces the start humidity by 10%.
[0072] Furthermore, when the start humidity is changed by the setting unit 113, the prediction unit 112 predicts the end humidity again using the changed start humidity (step S321). Then, the setting unit 113 re-determines whether the predicted end humidity is equal to or lower than the upper limit humidity (step S322). The setting unit 113 and the prediction unit 112 repeat the processes of steps S321 to S323 until the end humidity becomes equal to or lower than the upper limit humidity.
[0073] When the end humidity is equal to or lower than the upper limit humidity (step S322; NO), the setting unit 113 sets the start humidity used for the prediction as the target humidity before the start of DR (step S324).
[0074] Furthermore, the control unit 114 controls the air conditioner 1 so that the indoor temperature becomes the target temperature and the humidity becomes the target humidity before the start time of the DR (step S325). Thereafter, during the period from the start time to the end time of the DR, the control unit 114 controls the air conditioner 1 so that the power consumption is reduced by the specified DR rate. In this way, it is possible to prevent the indoor temperature from falling below the second temperature range due to overcooling before the start of DR, and to maintain comfort during the period before the start of DR. On the other hand, even if the indoor temperature exceeds the first temperature range (upper limit temperature TU) during the DR period, comfort can be maintained during the DR period by performing dehumidification in advance so that the humidity during the DR period does not exceed the upper limit humidity.
[0075] (Action, effect) As described above, in the control device 10 according to this embodiment, when the start humidity at the start time is input, the prediction unit 112 further predicts the end humidity at the end time using the humidity prediction model M2 which outputs a prediction of the change in indoor humidity due to demand response, and when there is no target temperature within the second temperature range at which the end indoor temperature is within the first temperature range, the setting unit 113 sets the target humidity to be reached by the start time of the demand response so that the end humidity is equal to or lower than a predetermined upper limit humidity.
[0076] In this way, it is possible to prevent the indoor temperature from falling below the second temperature range due to overcooling before the start of DR, and to maintain comfort during the period before the start of DR. On the other hand, even if the indoor temperature exceeds the first temperature range (upper limit temperature TU) during the DR period, comfort can be maintained during the DR period by performing dehumidification in advance so that the humidity during the DR period does not exceed the upper limit humidity.
[0077] <Fourth embodiment> Hereinafter, the second embodiment of the present disclosure will be described with reference to Figures 14 to 17. Among the configurations of the second embodiment, configurations common to the first embodiment will be denoted by the same reference numerals and description thereof will be omitted.
[0078] (Overall configuration of air conditioning system) Fig. 14 is a diagram showing an example of an air conditioning system according to a fourth embodiment. As shown in Fig. 14, an air conditioning system 100 according to this embodiment includes multiple air conditioners 1A and 1B. Each of the multiple air conditioners 1 conditions a different room. In the example of Fig. 14, the air conditioner 1A conditions room A on the second floor, and the air conditioner 1B conditions room B on the first floor.
[0079] In this embodiment, the control device 10 controls each of the multiple air conditioners 1A and 1B. The control device 10 adjusts the DR rate of each air conditioner 1A, 1B according to the air conditioning load of each air conditioner 1A, 1B. In the example of FIG. 14, the room A on the second floor is easily affected by the sun during the day and the room B on the first floor is less affected by the sun and the room temperature is less likely to rise. Then, since the room A on the second floor is easily affected by the sun, the air conditioning load to lower the room temperature is large during cooling and the air conditioning load to raise the room temperature is small during heating. On the other hand, since the room B on the first floor is not easily affected by the sun, the air conditioning load to lower the room temperature is small during cooling and the air conditioning load to raise the room temperature is large during heating. The control device 10 adjusts the DR rate according to the air conditioning load of each air conditioner and achieves the DR rate specified for the entire air conditioning system 100. Note that, in FIG. 14, an example in which the control device 10 controls two air conditioners 1A and 1B is shown for the sake of simplicity of explanation, but the present invention is not limited to this. The control device 10 may control three or more air conditioners 1.
[0080] (How to train a temperature prediction model) Fig. 15 is a flowchart showing the flow of a learning method for a temperature prediction model according to the fourth embodiment. Fig. 16 is a diagram showing an example of learning data for the temperature prediction model according to the fourth embodiment. Here, the flow of a learning method for the temperature prediction model M1 by the control device 10 will be described with reference to Figs. 15 to 16.
[0081] First, the control device 10 collects learning data D3 of the temperature prediction model M1 (step S401). As shown in FIG. 16, the learning data D3 is data collected for each of the air conditioners 1A and 1B, and includes time series data of the indoor temperature acquired from the indoor temperature sensor 3 during DR control, the outdoor air temperature acquired from the outdoor air temperature sensor 2, and the DR rate. The time series data of the indoor temperature is data that associates the indoor temperature with the elapsed time (minutes) from the start of DR control. Each piece of learning data D3 is given a label indicating which air conditioner the learning data corresponds to. The learning data D3 may further include a capacity (class) indicating the air conditioning capacity of each of the air conditioners 1A and 1B.
[0082] Next, the learning unit 115 learns temperature prediction models M1a, M1b for each of the air conditioners 1A, 1B based on the multiple pieces of learning data D3 recorded in the storage 13 (step S402). Specifically, when the start indoor temperature, start outdoor temperature, DR start time, and DR rate are input, the learning unit 115 learns each of the temperature prediction models M1a, M1b so as to output a transition prediction of the indoor temperature after the start time.
[0083] In addition, the learning unit 115 predicts the trends in the air conditioning load for each of the air conditioners 1A and 1B for each time period based on the predicted trends in the indoor temperature using the temperature prediction models M1a and M1b, and sets weightings for the air conditioners 1A and 1B for each DR rate and for each time period required for the entire air conditioning system 100 (step S403).
[0084] FIG. 17 is a diagram showing an example of weighting of air conditioners according to the fourth embodiment. The weighting is a weighting for the air conditioning capacity exhibited by each of the air conditioners 1A and 1B during DR control. The learning unit 115 sets a larger weighting as the air conditioning load is lower, and a smaller weighting as the air conditioning load is higher. FIG. 17 shows an example of weighting for each time period for the DR rate of each of the air conditioners 1A and 1B when the DR rate required for the entire air conditioning system 100 is 50%. For example, in a certain time period 1, there is no difference in the air conditioning load of the air conditioners 1A and 1B, and both are weighted to "1". When a DR rate of 50% is required in the time period 1, the DR rates of the air conditioners 1A and 1B are both 50%. In addition, in the time period 2, the air conditioning load of the air conditioner 1B is smaller than that of the air conditioner 1A, so the weighting of the air conditioner 1B is set to be larger than that of the air conditioner 1A. In the example of FIG. 17, the weighting of air conditioner 1A is "0.6", and the weighting of air conditioner 1B is "1.4". Then, when a DR of 50% is requested in time slot 2, the DR rate of air conditioner 1A will be 30%, and the DR rate of air conditioner 1B will be 70%. FIG. 17 only shows an example in which a DR of 50% is requested for the entire air conditioning system 100, but similar weighting is set for the other DR rates. A weighting table D4 in which weightings are set for each of air conditioners 1A and 1B is recorded in storage 13.
[0085] The learning unit 115 repeatedly collects learning data D3, learns the temperature prediction models M1a and M1b, and updates the weighting table D4 every time DR control is performed for each of the air conditioners 1A and 1B.
[0086] (Air conditioner control method) 18 is a flowchart showing the flow of a method for controlling an air conditioner according to the fourth embodiment. Here, the flow of a method for controlling the air conditioner 1 by the control device 10 will be described with reference to FIG.
[0087] First, the DR information acquisition unit 110 acquires DR information (step S411). The setting unit 113 sets the start indoor temperature and the start outdoor temperature at the start of DR for each of the rooms A and B (step S412). These processes are the same as those in the first embodiment (steps S111 to S112 in FIG. 4).
[0088] Next, the setting unit 113 refers to the weighting table D4 corresponding to the DR rate (overall DR rate) required for the entire air conditioning system 100, and reads out the weighting of each of the air conditioners 1A, 1B for the time period including the DR start time (step S413).
[0089] Furthermore, the prediction unit 112 predicts the end indoor temperature at the end time of the DR for each of the rooms A and B using the temperature prediction models M1a and M1b (step S414). At this time, the prediction unit 112 inputs, for example, the start indoor temperature, the start outdoor temperature, and the DR rate for each of the air conditioners 1A and 1B multiplied by the weighting to the overall DR rate to each of the temperature prediction models M1a and M1b, and obtains as output a predicted transition of the indoor temperature for each of the rooms A and B. If the overall DR rate is 50%, the weighting of the air conditioner 1A is 0.6, and the weighting of the air conditioner 1B is 1.4, the prediction unit 112 inputs 30% as the DR rate of the air conditioner 1A to the temperature prediction model M1a of the air conditioner 1A, and inputs 70% as the DR rate of the air conditioner 1B to the temperature prediction model M1b of the air conditioner 1B. The prediction unit 112 refers to the output predicted changes in the indoor temperatures of each of rooms A and B, and predicts what the indoor temperatures (indoor temperatures at the end) of each of rooms A and B will be when the DR start time and end time have elapsed.
[0090] Next, the setting unit 113 judges whether the predicted indoor end temperature for each of the rooms A and B is within the first temperature range (step S415). If the predicted indoor end temperature for each of the rooms A and B is within the first temperature range (step S415: YES), the setting unit 113 sets the start indoor temperature used for the prediction as the target temperature for each of the air conditioners 1A and 1B (step S416). If the predicted indoor end temperature for any of the rooms exceeds the first temperature range (step S415: NO), the setting unit 113 changes the start indoor temperature of this room (step S417). The prediction unit 112 uses the changed start indoor temperature to predict the indoor end temperature again (step S414). Then, the setting unit 113 judges again whether the predicted indoor end temperature is within the first temperature range (step S415). The setting unit 113 and the prediction unit 112 repeat the processes of steps S414, S415, and S417 until the ending indoor temperatures of all the rooms are within the first temperature range.
[0091] The control unit 114 controls the air conditioners 1A, 1B so that the indoor temperatures of the rooms A, B reach the target temperatures before the start time of the DR (step S418). After that, during the period from the start time to the end time of the DR, the control unit 114 controls the air conditioners 1A, 1B so that the power consumption is reduced by the DR rate assigned to each of the air conditioners 1A, 1B.
[0092] (Action, effect) As described above, in the control device 10 according to this embodiment, the learning unit 115 sets weighting for the demand response rate for each of the air conditioners 1A, 1B based on the predicted transition of the indoor temperature for each of the rooms A, B output by the temperature prediction models M1a, M1b of each of the multiple air conditioners 1A, 1B that condition different rooms A, B. In addition, the prediction unit 112 inputs, into the temperature prediction models M1a, M1b of each of the air conditioners 1A, 1B, a demand response rate obtained by multiplying the overall demand response rate required for the multiple air conditioners as a whole by the weighting for each of the air conditioners 1A, 1B, to predict the ending indoor temperature for each of the rooms A, B, and the setting unit 113 sets the target temperature for each of the air conditioners 1A, 1B so that the ending indoor temperature for each of the rooms A, B is within the first temperature range.
[0093] In this way, the control device 10 can increase or decrease the DR rate according to the air conditioning load of each air conditioner 1A, 1B, thereby achieving the requested overall DR rate overall, while more reliably preventing the indoor temperatures of each of rooms A, B from deviating from the comfortable temperature range (first temperature range) during the DR period.
[0094] <Other embodiments> Although the embodiment has been described in detail above with reference to the drawings, the specific configuration is not limited to the above, and various design changes are possible. That is, in other embodiments, the order of the above-mentioned processes may be changed as appropriate. Also, some of the processes may be executed in parallel.
[0095] <Additional Notes> The air conditioner control device, the air conditioner control method, and the program described in the above-described embodiments can be understood, for example, as follows.
[0096] (1) According to a first aspect, the control device 10 of the air conditioner 1 includes a demand response information acquisition unit 110 that acquires demand response information including a start time and an end time of the demand response, a prediction unit 112 that predicts an end indoor temperature at an end time of the demand response using a temperature prediction model M1 that outputs a prediction of the progress of the indoor temperature due to the demand response when a start indoor temperature and a start outdoor air temperature at the start time of the demand response are input, and a setting unit 113 that sets a target temperature by adjusting the start indoor temperature so that the end indoor temperature is within a predetermined first temperature range.
[0097] In this way, the control device 10 can minimize overcooling or overheating before DR while preventing the indoor temperature during the DR period from deviating from a comfortable temperature range (first temperature range) based on the indoor temperature and outdoor temperature at the start of DR. As a result, the total power consumption of the air conditioner 1 throughout all time periods in which it is operating can be reduced.
[0098] (2) According to the second aspect, in the control device 10 of the air conditioner 1 according to the first aspect, the setting unit 113 sets, as the target temperature, the starting indoor temperature that is within a predetermined second temperature range, among the starting indoor temperatures at which the ending indoor temperature is within the first temperature range.
[0099] In this manner, the control device 10 can suppress the indoor temperature from changing to such an extent that comfort is impaired not only during the DR period but also during overcooling or overheating before the DR.
[0100] (3) According to the third aspect, in the control device 10 of the air conditioner 1 relating to the first or second aspect, the temperature prediction model M1 is trained to predict the trend of change in the outdoor air temperature after the start time when a start time is further input, and to output a prediction of the progress of the indoor temperature according to the predicted trend of change in the outdoor air temperature.
[0101] In this way, the control device 10 can predict whether the outdoor air temperature will tend to increase or decrease during the DR period, thereby improving the prediction accuracy of the ending indoor temperature.
[0102] (4) According to the fourth aspect, in the control device 10 of the air conditioner 1 relating to the first or second aspect, the temperature prediction model M1 is trained to output a prediction of the change in indoor temperature in response to the change in the outdoor temperature at each time, when the outdoor temperature at each time from the start time to the end time is further input.
[0103] In this way, the control device 10 can improve the accuracy of predicting the indoor temperature at the end of the DR period by taking into account the change in the outdoor air temperature during the DR period. The outdoor air temperature during the DR period may be obtained from weather forecast information provided by an external server.
[0104] (5) According to a fifth aspect, in the control device 10 of the air conditioner 1 relating to any one of the first to fourth aspects, the demand response information further includes a demand response rate indicating the rate of reduction in power consumption required from the start time to the end time, and the temperature prediction model M1 is trained to output a prediction of the change in the indoor temperature according to the demand response rate when the demand response rate is further input.
[0105] In this way, even if a different DR rate is specified by a user or a supplier, the control device 10 can appropriately set the target temperature so that the indoor temperature during the DR period does not deviate from a comfortable temperature range (first temperature range) while minimizing supercooling or superheating before DR according to the DR rate.
[0106] (6) According to a sixth aspect, in the control device 10 of the air conditioner 1 relating to any one of the first to fifth aspects, the temperature prediction model M1 is further trained to output a prediction of the indoor temperature trend corresponding to the period during which supercooling or superheating was performed at the starting indoor temperature before demand response when the starting indoor temperature and the starting outdoor temperature are input, and the setting unit 113 sets the target temperature and the target time indicating how long before the start time the target temperature should be reached based on a combination of the starting indoor temperature and the period during which the ending indoor temperature is within a first temperature range.
[0107] In this manner, the control device 10 can more reliably prevent the indoor temperature during the DR period from deviating from the comfortable temperature range (first temperature range) while minimizing overcooling or overheating before the DR.
[0108] (7) According to the seventh aspect, in the control device 10 of the air conditioner 1 relating to any one of the first to sixth aspects, when the start humidity at the start time is input, the prediction unit 112 further predicts the end humidity at the end time using a humidity prediction model M2 that outputs a prediction of the change in indoor humidity due to demand response, and when there is no target temperature within the second temperature range at which the end indoor temperature is within the first temperature range, the setting unit 113 sets the target humidity to be reached by the start time of the demand response so that the end humidity is equal to or lower than a predetermined upper limit humidity.
[0109] In this way, it is possible to prevent the indoor temperature from falling below the second temperature range due to overcooling before the start of DR, and to maintain comfort during the period before the start of DR. On the other hand, even if the indoor temperature exceeds the first temperature range (upper limit temperature TU) during the DR period, comfort can be maintained during the DR period by performing dehumidification in advance so that the humidity during the DR period does not exceed the upper limit humidity.
[0110] (8) According to an eighth aspect, the control device 10 of the air conditioner 1 relating to any one of the first to seventh aspects further includes a learning unit 115 that sets a weighting for the demand response rate for each air conditioner 1A, 1B based on a predicted change in the indoor temperature for each room A, B output by the temperature prediction models M1a, M1b of each of the multiple air conditioners 1A, 1B that condition different rooms A, B. The prediction unit 112 inputs a demand response rate obtained by multiplying the overall demand response rate required for the multiple air conditioners as a whole by the weighting for each air conditioner 1A, 1B into the temperature prediction models M1a, M1b of each air conditioner 1A, 1B to predict the ending indoor temperature for each room A, B. The setting unit 113 sets a target temperature for each air conditioner 1A, 1B so that the ending indoor temperature for each room A, B is within a first temperature range.
[0111] In this way, the control device 10 can increase or decrease the DR rate according to the air conditioning load of each air conditioner 1A, 1B, thereby achieving the requested overall DR rate overall, while more reliably preventing the indoor temperatures of each of rooms A, B from deviating from the comfortable temperature range (first temperature range) during the DR period.
[0112] (9) According to a ninth aspect, a method of controlling the air conditioner 1 includes the steps of acquiring demand response information including a start time and an end time of the demand response, predicting an end indoor temperature at an end time of the demand response using a temperature prediction model M1 that outputs a prediction of the progress of the indoor temperature due to the demand response when a start indoor temperature and a start outdoor temperature at the start time of the demand response are input, and setting a target temperature by adjusting the start indoor temperature so that the end indoor temperature is within a predetermined first temperature range.
[0113] (10) According to the tenth aspect, the program causes the control device 10 of the air conditioner 1 to execute the following steps: acquiring demand response information including a start time and an end time of the demand response; predicting an end indoor temperature at an end time of the demand response using a temperature prediction model M1 that outputs a prediction of the progress of the indoor temperature due to the demand response when a start indoor temperature and a start outdoor temperature at the start time of the demand response are input; and setting a target temperature by adjusting the start indoor temperature so that the end indoor temperature is within a predetermined first temperature range. [Explanation of symbols]
[0114] 100 Air Conditioning System 1,1A,1B Air conditioner 2. Outside Air Temperature Sensor 3. Indoor temperature sensor 4 Humidity Sensor 10 Control device 11 Processors 110 Demand response information acquisition unit 110 DR information acquisition section 111 Sensor information acquisition unit 112 Prediction Department 113 Setting section 114 Control section 115 Learning Department 12. Memory 13. Storage 14 Communication Interface 20 Servers D1, D2, D3 Learning data D4 Weighting Table M1, M1a, M1b temperature prediction models M2 Humidity Prediction Model
Claims
1. a demand response information acquisition unit that acquires demand response information including a start time and an end time of the demand response; a prediction unit that predicts an end indoor temperature at an end time of the demand response by using a temperature prediction model that outputs a prediction of a transition of the indoor temperature due to the demand response when a start indoor temperature and a start outdoor temperature at a start time of the demand response are input; a setting unit that sets a target temperature by adjusting the start indoor temperature so that the end indoor temperature is within a predetermined first temperature range; An air conditioner control device comprising:
2. The setting unit sets, as the target temperature, a start indoor temperature that is within a preset second temperature range among start indoor temperatures that cause the end indoor temperature to be within the first temperature range. The control device for an air conditioner according to claim 1.
3. The temperature prediction model is trained so that, when the start time is further input, the temperature prediction model predicts a change trend of the outdoor air temperature after the start time, and outputs a transition prediction of the indoor temperature according to the predicted change trend of the outdoor air temperature. The control device for an air conditioner according to claim 1.
4. The temperature prediction model is trained so that, when an outdoor air temperature for each time from the start time to the end time is further input, a transition prediction of an indoor temperature corresponding to a change in the outdoor air temperature for each time is output. The control device for an air conditioner according to claim 1.
5. The demand response information further includes a demand response rate indicating a reduction rate of power consumption requested from the start time to the end time, The temperature prediction model is trained so that, when the demand response rate is further input, a transition prediction of the indoor temperature according to the demand response rate is output. The control device for an air conditioner according to claim 1.
6. the temperature prediction model is further trained to output, when the start indoor temperature and the start outdoor temperature are input, a prediction of a transition of the indoor temperature corresponding to a period during which supercooling or superheating was performed at the start indoor temperature before the demand response; the setting unit sets the target temperature and a target time indicating how long before the start time the target temperature should be reached, based on a combination of the start indoor temperature and a period during which the end indoor temperature falls within the first temperature range. The control device for an air conditioner according to claim 1.
7. the prediction unit, when receiving the start humidity at the start time, further predicts an end humidity at the end time using a humidity prediction model that outputs a transition prediction of the indoor humidity due to the demand response; the setting unit, when there is no target temperature that is within the second temperature range and that will cause the end-time indoor temperature to be within the first temperature range, sets a target humidity that should be reached by the start time of the demand response so that the end-time humidity is equal to or lower than a predetermined upper limit humidity. The control device for an air conditioner according to claim 2.
8. A learning unit that sets a weighting for a demand response rate for each of a plurality of air conditioners based on a predicted change in indoor temperature for each of the rooms output by a temperature prediction model for each of the plurality of air conditioners that condition different rooms, the prediction unit inputs a demand response rate obtained by multiplying an overall demand response rate required for the plurality of air conditioners as a whole by a weighting for each of the air conditioners into a temperature prediction model for each of the air conditioners, and predicts an ending indoor temperature for each of the rooms; the setting unit sets the target temperature for each of the air conditioners such that the end indoor temperature of each of the rooms is within the first temperature range. The control device for an air conditioner according to any one of claims 1 to 7.
9. obtaining demand response information including a start time and an end time of the demand response; a step of predicting an end indoor temperature at an end time of the demand response by using a temperature prediction model that outputs a prediction of a transition of the indoor temperature due to the demand response when a start indoor temperature and a start outdoor temperature at a start time of the demand response are input; setting a target temperature by adjusting the starting indoor temperature so that the ending indoor temperature is within a predetermined first temperature range; A method for controlling an air conditioner comprising the steps of:
10. obtaining demand response information including a start time and an end time of the demand response; a step of predicting an end indoor temperature at an end time of the demand response by using a temperature prediction model that outputs a prediction of a transition of the indoor temperature due to the demand response when a start indoor temperature and a start outdoor temperature at a start time of the demand response are input; setting a target temperature by adjusting the starting indoor temperature so that the ending indoor temperature is within a predetermined first temperature range; A program that causes an air conditioner control device to execute the above.
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