Control system, air conditioning system, control method, and program

Abstract

The present invention provides on-timer operation which makes it possible to achieve both comfort and low power consumption. Provided is a control system for controlling an air conditioner, said control system comprising: a setting reception unit which receives settings for a target temperature and a specified time when the target temperature should be achieved; an optimal operation calculation unit which calculates time-series air conditioning capacity of the air conditioner such that power used by the air conditioner to achieve the target temperature at the specified time is minimized; and a control unit which operates the air conditioner on the basis of the time-series air conditioning capacity.

Description

Control system, air conditioning system, control method and program

[0001] The present disclosure relates to a control system, an air conditioning system, a control method, and a program. This disclosure claims priority based on Japanese Patent Application No. 2023-131587 filed on August 10, 2023, the contents of which are incorporated herein by reference.

[0002] A function known as "on timer operation" is provided that automatically starts operation of an air conditioner before a user-specified time and performs cooling or heating operation so that the room temperature reaches a target temperature by the specified time. Using the on timer operation, for example, by specifying the user's arrival time as a specified time, the user's home can be air-conditioned to a desired target temperature upon arrival. Patent Document 1 discloses an air conditioning system that learns the indoor temperature, outdoor temperature, and the air conditioning capacity of the air conditioner to generate a building load prediction model, inputs predicted values ​​of the indoor temperature, air conditioning capacity, and outdoor temperature at the specified time when determining the air conditioning start time into the building load prediction model to predict the building load, determines a pre-operation start time in 5-minute increments from a range of, for example, 0 to 40 minutes before the specified time based on the predicted building load, and starts operation at the determined pre-operation start time. The air conditioning system of Patent Document 1 can automatically start air conditioning earlier in rooms with high building loads than in rooms with low building loads, thereby reducing the possibility that the room temperature will not reach the target temperature at the specified time even when the on timer operation is performed when the building load is high. However, even if the target temperature can be achieved by this control, there is a possibility that power consumption will increase, for example, by starting air conditioning earlier than necessary.

[0003] Japanese Patent Application Laid-Open No. 2020-125868

[0004] There is a demand for a control method that can achieve both comfort and low power consumption in on-timer operation.

[0005] The present disclosure provides a control system, an air conditioning system, a control method, and a program that can solve the above-mentioned problems.

[0006] According to one aspect of the present disclosure, the control system is a control system for controlling an air conditioner, and includes a setting reception unit that receives settings of a target temperature and a specified time at which the target temperature should be achieved, an optimal operation calculation unit that calculates the time-series air conditioning capacity of the air conditioner so that the power consumption used by the air conditioner to achieve the target temperature at the specified time is minimized, and a control unit that operates the air conditioner based on the time-series air conditioning capacity.

[0007] According to one aspect of the present disclosure, an air conditioning system includes an air conditioner and the above-described control system.

[0008] According to one aspect of the present disclosure, a control method is a control method for an air conditioner, and includes the steps of accepting settings of a target temperature and a specified time at which the target temperature should be achieved, calculating a time-series air conditioning capacity of the air conditioner so that the power consumption used by the air conditioner to achieve the target temperature at the specified time is minimized, and operating the air conditioner based on the time-series air conditioning capacity.

[0009] According to one aspect of the present disclosure, the program causes a computer to perform the steps of accepting settings of a target temperature and a specified time at which the target temperature should be achieved, calculating the time-series air conditioning capacity of the air conditioner so that the power consumption used by the air conditioner to achieve the target temperature at the specified time is minimized, and operating the air conditioner based on the time-series air conditioning capacity.

[0010] According to the above-described control system, air conditioning system, control method, and program, a target temperature can be achieved by a specified time with low power consumption.

[0011] FIG. 1 is a diagram showing an example of an air conditioning system in an embodiment. FIG. 2 is a diagram showing an example of a room temperature prediction model in an embodiment. FIG. 3 is a block diagram showing a detailed configuration example of an optimal operation calculation unit in an embodiment. FIG. 4 is a first diagram illustrating a method for calculating an optimal air conditioning capacity in an embodiment. FIG. 5 is a second diagram illustrating a method for calculating an optimal air conditioning capacity in an embodiment. FIG. 6 is a flowchart showing an example of control of on-timer operation in an embodiment. FIG. 7 is a diagram showing an example of the hardware configuration of an air conditioning system in an embodiment.

[0012] <Embodiment> Hereinafter, a method for controlling timer-on operation according to an embodiment of the present disclosure will be described with reference to Figs.

[0013] (Configuration of Air Conditioning System) FIG. 1 is a diagram illustrating an example of an air conditioning system according to an embodiment. As shown in FIG. 1 , the air conditioning system 100 includes an air conditioner 1, a server 20, and a user terminal 30. The air conditioner 1 includes an outdoor air temperature sensor 2, an indoor temperature sensor 3, a compressor 4, and a control device 10. For example, the outdoor air temperature sensor 2 is provided in an outdoor unit (not shown), and the indoor temperature sensor 3 is provided in an indoor unit (not shown). However, the installation locations of the outdoor air temperature sensor 2 and the indoor temperature sensor 3 are not limited thereto. The outdoor air temperature sensor 2 and the indoor temperature sensor 3 are connected to the control device 10, and the control device 10 acquires the temperatures measured by the outdoor air temperature sensor 2 and the indoor temperature sensor 3. The compressor 4 is connected to the control device 10, and the control device 10 controls the compressor 4 and detects its operating status. For example, the control device 10 can operate the compressor 4 at a desired rotation speed and detect the rotation speed. The server 20 creates a room temperature prediction model used to calculate the air conditioning start time for timer-on operation. The user terminal 30 is a smartphone or the like owned by the user of the air conditioner 1, and the user can set the target temperature and specified time for the on-timer operation, give instructions to execute the on-timer operation, etc. from the user terminal 30. The control device 10, the server 20, and the user terminal 30 are connected to each other so as to be able to communicate with each other via the network NW.

[0014] (Configuration of the control device) The control device 10 includes a sensor information acquisition unit 11, a setting acceptance unit 12, an optimal operation calculation unit 13, a control unit 14, and a communication unit 15. The sensor information acquisition unit 11 acquires the outdoor air temperature measured by the outdoor air temperature sensor 2 and the indoor temperature measured by the indoor temperature sensor 3. The setting acceptance unit 12 accepts settings such as a target temperature for the air-conditioned space and a designated time by which the target temperature should be achieved. For example, the target temperature and the designated time are set using a remote control or a user terminal 30 (not shown).

[0015] The optimal operation calculation unit 13 calculates the time (referred to as the arrival time) required for the indoor temperature of the air-conditioned space to reach the target temperature and the air conditioning capacity required at that time. The air conditioning capacity refers to the heating or cooling intensity provided by the air conditioner 1. The air conditioning capacity has a positive correlation with the power used for air conditioning by the air conditioner 1 and can be calculated, for example, from the rotation speed of the compressor 4. The optimal operation calculation unit 13 calculates a time-series transition of the air conditioning capacity that will enable the target temperature to be achieved by a specified time with the lowest power consumption based on the room temperature prediction model 131 created by the room temperature prediction model creation unit 21 (described later) and the outdoor and indoor temperatures measured by the air conditioner 1, and calculates the arrival time from the result. The optimal operation calculation unit 13 converts the time-series air conditioning capacity into the rotation speed of the compressor 4 and calculates the time-series rotation speed of the compressor 4 that will enable the target temperature to be achieved by a specified time with the lowest power consumption. Furthermore, the optimal operation calculation unit 13 predicts the time series change in indoor temperature when the target temperature can be achieved by the specified time with the lowest power consumption, and provides the user with a predicted change in indoor temperature during on-timer operation by sending it to the user terminal 30.

[0016] The control unit 14 controls the compressor 4 and other components to perform air conditioning. During on-timer operation, the control unit 14 starts operation of the compressor 4 and other components the arrival time calculated by the optimal operation calculation unit 13 before the designated time set by the user, and operates the compressor 4 according to the rotation speed of the compressor 4 in a time series calculated by the optimal operation calculation unit 13 to perform cooling operation or heating operation.

[0017] The communication unit 15 communicates with the server 20 and the user terminal 30. For example, the communication unit 15 transmits to the server 20 the outside air temperature and room temperature acquired by the sensor information acquisition unit 11, the rotation speed of the compressor 4 detected by the control unit 14, and the designated time and target temperature acquired by the setting reception unit 12 as learning data required to create the room temperature prediction model 131. The communication unit 15 acquires the room temperature prediction model 131 and the like from the server 20. The communication unit 15 acquires the target temperature, designated time settings, and the like transmitted from the user terminal 30.

[0018] (Configuration of the Server) The server 20 comprises a room temperature prediction model creation unit 21 and a communication unit 22. The room temperature prediction model creation unit 21 creates a room temperature prediction model 131 for predicting the time required for the indoor temperature of the air-conditioned space to reach the target temperature. The room temperature prediction model 131 is based on the indoor temperature (K), the outdoor temperature (K), and the air conditioning capacity (W / m ２ ) is input, a prediction model is created that outputs the future indoor temperature. An example of the room temperature prediction model 131 is shown in FIG.

[0019] (Room Temperature Prediction Model) The ARX model (Auto-Regressive with eXogenous model) shown in FIG. 2 predicts the room temperature T ｉｎ (k) and the outside air temperature T Ｏ (k) from the indoor temperature T ｉｎ The deviation T obtained by subtracting (k) Ｏ―ｉｎ (k) and air conditioning capacity φ ｈ 2 is a linear multiple regression model showing the relationship between (k) and (k). When the ARX model in FIG. 2 is rearranged, the following equation (1) is obtained.

[0020] T ｉｎ (k) = a １ T ｉｎ (k-1)+a ２ T ｉｎ (k-2)+...+a ｍ T ｉｎ (km) + b １１ φ ｈ (k-1)+b １２ φ ｈ (k-2)+...+b １ｍ φ ｈ (km) + b ２１ TＯ―ｉｎ (k-1)+b ２２ T Ｏ―ｉｎ (k-2)+...+b ２ｍ T Ｏ―ｉｎ (km-m)+w ０ ...(1)

[0021] The room temperature prediction model creation unit 21 calculates the coefficient a of the formula (1) using past operation data of the air conditioner 1, that is, the outdoor air temperature measured by the outdoor air temperature sensor 2, the indoor temperature measured by the indoor temperature sensor 3, and the rotation speed of the compressor 4 instructed by the control unit 14, which are collected at predetermined intervals during a predetermined period in the past. １ , a ２ , ..., a ｍ , b １１ , b １２ , ..., b １ｍ , b ２１ , b ２２ , ..., b ２ｍ and the bias term w, which is a constant representing noise and disturbances. ０ and the step length m that can maintain high prediction accuracy are calculated. The rotation speed of the compressor 4 is calculated as the air conditioning capacity (W / m ２ ) can be converted to

[0022] During heating operation, the outside temperature T Ｏ is the non-frost temperature range (T Ｏ ≦-7℃, 5.5℃<T Ｏ ) in the case of φ ｈ (T Ｏ )=φ(-7)+((φ(7)-φ(-7))÷(7-(-7)))×(T Ｏ -(-7)) ... (2)

[0023] During heating operation, the outside temperature T Ｏ is the frosting temperature range (-7℃<T Ｏ ≦5.5℃) φ ｈ (T Ｏ )=φ(-7)+((φ(2)-φ(-7))÷(2-(-7)))×(T Ｏ -(-7)) (2') where φ(-7), φ(2), and φ(7) are the air conditioning capacities at outside temperatures of -7°C, 2°C, and 7°C, respectively.

[0024] In cooling operation φｈ (T Ｏ )=φ(35)+((φ(29)-φ(35))÷(35-29))×(35-T Ｏ ) (3) where φ(35) and φ(29) are the air conditioning capacities at outside temperatures of 35° C. and 29° C., respectively.

[0025] When the rotation speed of the compressor 4 is ncmp, and K1, K2, K3, K4, and K5 are predetermined constants, the relationship between ncmp and φ(-7), φ(2), φ(7), φ(29), and φ(35) is as follows: φ(35) = K1 × ncmp, φ(29) = K2 × ncmp, φ(-7) = K3 × ncmp, φ(2) = K4 × ncmp, φ(-7) = K5 × ncmp (4)

[0026] The room temperature prediction model creation unit 21 converts the rotation speed of the compressor 4 into φ(−7), φ(2), φ(7), φ(29), and φ(35) using equation (4), and calculates the outdoor air temperature T Ｏ By using one of the formulas (2), (2'), and (3) according to the above, the rotation speed of the compressor 4 is calculated based on the air conditioning capacity φ ｈ (T Ｏ ) to

[0027] For example, the room temperature prediction model creation unit 21 may use a coefficient a that minimizes the square of the difference (RMSE: Root Mean Squared Error) between the actual indoor temperature measurement value included in the operation data of the air conditioner 1 and the predicted indoor temperature value obtained by substituting the actual indoor temperature measurement value included in the operation data, the outdoor air temperature measurement value, and the rotation speed of the compressor 4 into equation (1). １ Alternatively, due to multicollinearity between explanatory variables, the least squares method may be used to calculate the appropriate coefficient a １ In cases where values ​​such as the coefficient a cannot be calculated or where overfitting occurs, ridge regression is used. １ If the matrix of input data is X, the matrix of output data is Y, and the matrix of coefficients is W, the least squares method is Ｔ X) －１ X Ｔ The coefficient W is calculated using Y, but ridge regression uses the unit matrix I to calculate W = (X Ｔ X-dI) －１ X ＴThe coefficient W is calculated using Y. The coefficient a is calculated using ridge regression. １ When calculating the above, the room temperature prediction model creation unit 21 appropriately sets the step length m and the ridge regression parameter d, and calculates the coefficient a １ For example, various values ​​are set for m and d, and the coefficient a is calculated for each combination of m and d. １ etc., and then, from among them, m and d that minimize the RMSE may be adopted. １ The method for calculating the coefficients a, b, c, and c etc. is not limited to the above, and any other method can be used. For the time-series data of the indoor temperature, outdoor temperature, and rotation speed of the compressor 4 used as learning data, it is preferable to use the operating data of the air conditioner 1 installed in the actual space to be air-conditioned, but this is not limited thereto, and for example, operating data of another air conditioner with equivalent capacity installed in a space with similar properties to the space to be air-conditioned may be used. The room temperature prediction model creation unit 21 calculates the coefficient a １ When the above is calculated, the calculated coefficient a １ etc. are applied to equation (1) to create a room temperature prediction model 131.

[0028] The communication unit 22 communicates with the control device 10 and the like. For example, the communication unit 22 receives the indoor temperature, the outdoor temperature, the rotation speed of the compressor 4, and the like from the control device 10, and outputs these values ​​to the room temperature prediction model creation unit 21. The room temperature prediction model creation unit 21 uses a coefficient a １ The communication unit 22 transmits the room temperature prediction model 131 created by the room temperature prediction model creation unit 21 to the control device 10.

[0029] The user terminal 30 is a mobile terminal such as a smartphone owned by a user of the air conditioner 1. The user operates the user terminal 30 to set a target temperature and a specified time, and to instruct the on timer operation. The user terminal 30 then transmits this setting information and instruction information to the control device 10 of the air conditioner 1.

[0030] 1 is an example. For example, the optimal operation calculation unit 13 may be provided in the server 20, and the calculation results may be transmitted to the control device 10, so that the server 20 performs computationally intensive processing, or the room temperature prediction model creation unit 21 may be provided in the control device 10. Information on humidity and solar radiation may be transmitted to the room temperature prediction model creation unit 21, which may then learn time-series data on the indoor temperature, outdoor temperature, the rotation speed of the compressor 4, humidity, and solar radiation to create the room temperature prediction model 131.

[0031] (Details of Optimal Operation Calculation) FIG. 3 shows a detailed configuration example of the optimal operation calculation unit 13. As shown in the figure, the optimal operation calculation unit 13 includes an optimal air conditioning capacity calculation unit 132, an optimal indoor temperature calculation unit 133, and an optimal rotation speed calculation unit 134. The optimal air conditioning capacity calculation unit 132 has a solver for solving linear programming problems and a room temperature prediction model 131. It receives input data such as upper and lower limits of the air conditioning capacity, the outdoor temperature, the indoor temperature, the target temperature, and the time until the specified time (the time until the on timer operation), analyzes what type of air conditioning operation will achieve the target temperature with the lowest power consumption by the specified time, and calculates the time-series air conditioning capacity (power consumption per unit time) and the time to reach the target temperature. The function of the optimal air conditioning capacity calculation unit 132 will be specifically described using FIGS. 4A and 4B.

[0032] As an example, let us assume that m=1 is obtained as the step length with good prediction accuracy for the indoor temperature. In this case, by rearranging the above equation (1), we obtain the following equation (1'): T ｉｎ (k+1)=a １ T ｉｎ (k) + b １１ φ ｈ (k) + b ２１ T ０ (k) + w ０ ...(1') Coefficient a of Equation (1') １ etc. are a in formula (1) １ etc., for example, coefficient a １ Then, T in Eq. (1) ｏ－ｉｎ T Ｏ -T ｉｎ After replacing it with and rearranging it, for example, (a １ -b ２１ ) to a １T in formula (1') ｉｎ is the indoor temperature, φ ｈ is the heating capacity, T ０ is the outside temperature, w ０ is a bias term. Equation (1') is an example of the room temperature prediction model 131 of the optimal air conditioning capacity calculation unit 132. The heating capacity φ ｈ , outside temperature T Ｏ When is given, the room temperature T^ after N minutes ｉｎ can be expressed by equation (5) in FIG. 4A.

[0033] The optimal air conditioning capacity calculation unit 132 solves the linear programming problem (Equation (6)) shown in FIG. 4B using Equation (5) to calculate the upper and lower limits of the heating capacity and the target temperature T ｍｉｎ and the target temperature T ｍｉｎ The optimal heating capacity φ that minimizes the total heating capacity until ｈ Equation (6) is an example of an equation for when heating operation is performed using an on-timer operation and the target temperature is reached by a specified time, and a linear programming problem can be formulated in the same way for cooling operation. c in equation (6) represents a 1 × N matrix with all elements being 1, and φ ｍｉｎ is the lower limit of the heating capacity, φ ｍａｘ represents the upper limit of the heating capacity. ｍｉｎ and φ ｍａｘ is set arbitrarily by the designer, etc. Minimize is the objective function c Ｔ φ ｈ The first constraint from the top is that the heating capacity must be minimized to a lower limit φ ｍｉｎ The second constraint is that the heating capacity must be greater than or equal to the upper limit φ ｍａｘ The third is based on the equation (5), which constrains the future indoor temperature to be T ｍｉｎ (In the case of cooling, T ｍｉｎ For example, the target temperature is set to T ＳＰ (℃), and after M (<N) minutes (M minutes is the target time), the room temperature is set to T ＳＰ In order to reach this temperature, the lower limit T ｍｉｎis set as follows: T ｍｉｎ =[0,...,273.15+T ｓｐ ,...,273.15+T ｓｐ ] Ｔ Here, if the length of one control step is, for example, one minute, then the matrix T ｍｉｎ The first 0 indicates the lower limit of the room temperature that should be met after one minute, and in this example, M 0s are lined up from the beginning. Then, the M+1th to Nth elements are 273.15+T ｓｐ By setting in this way and solving the linear programming problem of equation (6), the indoor temperature T ｓｐ For example, the optimal operation calculation unit 13 calculates the heating capacity per minute when the initial indoor temperature T ｉｎ (0) is used as the indoor temperature measured by the indoor temperature sensor 3 at the time of prediction (when starting to solve this linear programming problem), and 0 is used as the initial value φ of the heating capacity. ｈ (0), and the outdoor air temperature measured by the outdoor air temperature sensor 2 at the time of prediction is used as the initial value T Ｏ (0), and the indoor temperature after one minute is predicted by the formula (1'), and the heating capacity after one minute φ ｈ (1) is set to an arbitrary value, and the indoor temperature after one minute predicted by the formula (1') is calculated as the indoor temperature T ｉｎ (1), and the outdoor temperature at the time of prediction is used as the indoor temperature T Ｏ (1) is used, and the indoor temperature two minutes later is predicted using equation (1'). This calculation is repeated thereafter in the same manner, and the time-series indoor temperature T ｉｎ , outside temperature T Ｏ , heating capacityφ ｈ A set of time series T that satisfies Equation (6) is searched. ｉｎ , T Ｏ , φ ｈ The simplex method and the interior point method are known as methods for efficiently searching for a combination of φ. The optimal operation calculation unit 13 solves the linear programming problem shown in FIG. 4B using, for example, the simplex method or the interior point method. By solving the linear programming problem, for example, the heating capacity for each minute from the prediction time until N minutes later can be obtained. φ ｈ = [0, 0, 0, φｈ１ , φ ｈ２ , φ ｈ３ , ...] ... (7) For example, suppose a calculation result such as that shown in formula (7) is obtained. A time period when the heating capacity is 0 is a time period when the unit is not operating, and the time corresponding to a control step for which a value greater than 0 is set becomes the operation start time for timer operation. In the case of formula (7), the air conditioner 1 remains stopped until two minutes later (the first 0 indicates that the heating capacity is 0 minutes later, the second 0 indicates that the heating capacity is 1 minute later, and the third 0 indicates that the heating capacity is 0 two minutes later), and after three minutes the heating capacity φ ｈ１ The air conditioner 1 starts operation at , and after 4 minutes, the heating capacity φ ｈ２ After 5 minutes, the heating capacity is increased to φ ｈ３ By controlling the temperature in this way, the indoor temperature T ｓｐ (°C) can be achieved. The optimal operation calculation unit 13 solves the linear programming problem exemplified in equation (6) to calculate a time-series air conditioning capacity (for example, equation (7)), and outputs this data to the optimal indoor temperature calculation unit 133 and the optimal rotation speed calculation unit 134. The time-series air conditioning capacity calculated by the optimal operation calculation unit 13 is referred to as the optimal air conditioning capacity.

[0034] The optimal indoor temperature calculation unit 133 includes a room temperature prediction model 131. When the optimal air conditioning capacity is input, the optimal indoor temperature calculation unit 133 inputs the optimal air conditioning capacity, the outdoor air temperature at the time of prediction, and the indoor temperature at the time of prediction into the room temperature prediction model 131 (the indoor temperature at the time of prediction is input as the initial indoor temperature value, and the outdoor air temperature is input as the outdoor air temperature for each subsequent control step). The room temperature prediction model 131 predicts the indoor temperature for each control step up to M minutes into the future (or up to N minutes into the future). This allows, for example, a minute-by-minute change in the indoor temperature from the present (the time of prediction) to a specified time to be predicted. The predicted time series of indoor temperatures is referred to as the optimal indoor temperature. The optimal indoor temperature calculation unit 133 transmits the optimal indoor temperature to the user terminal 30 via the communication unit 15. The user terminal 30 acquires the optimal indoor temperature and displays it on the display unit. This allows the user to check the predicted change in the indoor temperature for the on-timer operation they specified.

[0035] When the optimal air-conditioning capacity is input, the optimal rotation speed calculation unit 134 converts it into a time-series rotation speed of the compressor 4 (referred to as the optimal compressor rotation speed) and outputs the converted rotation speed to the control unit 14. The optimal rotation speed calculation unit 134 calculates the rotation speed of the compressor 4 backward from the air-conditioning capacity using the above-described equations (2) to (4), and converts the time-series air-conditioning capacity into a time-series rotation speed of the compressor 4. When the on-timer operation is set, the control unit 14 controls the compressor 4 based on the optimal compressor rotation speed (e.g., the above-described equation (7)) obtained from the optimal rotation speed calculation unit 134. The control unit 14 may control the compressor 4 in accordance with the optimal compressor rotation speed, or may correct the optimal compressor rotation speed using deviation feedback control (e.g., PID control or PI control) so as to bring the difference between the optimal indoor temperature and the actual indoor temperature closer to zero, and then control the compressor 4 using the corrected rotation speed.

[0036] (Operation) Next, the operation of the air conditioning system 100 according to this embodiment will be described with reference to FIG. 5 . FIG. 5 is a flowchart showing an example of control of on-timer operation according to this embodiment. First, the room temperature prediction model creation unit 21 creates a room temperature prediction model 131 (step S11). The room temperature prediction model creation unit 21 creates the room temperature prediction model 131 based on past operating data of the air conditioner 1, such as a time series of indoor temperature, outdoor temperature, and the rotation speed of the compressor 4, and the ARX model shown in FIG. 2 . The room temperature prediction model creation unit 21 transmits the created room temperature prediction model 131 to the control device 10 via the communication unit 22. In the control device 10, the optimal operation calculation unit 13 acquires and stores the transmitted room temperature prediction model 131.

[0037] Next, the user sets the operation mode (cooling operation or heating operation), target temperature, and designated time to the control device 10 using the user terminal 30 or a remote control (not shown), and commands the ON timer operation (step S12). The setting reception unit 12 acquires the operation mode, target temperature, designated time, and command to execute the ON timer operation via the communication unit 15, and outputs this information to the optimal operation calculation unit 13.

[0038] Next, the optimal operation calculation unit 13 calculates the optimal air conditioning capacity using the optimal air conditioning capacity calculation unit 132 (step S13). The optimal air conditioning capacity calculation unit 132 acquires the outdoor air temperature measured by the outdoor air temperature sensor 2, the indoor temperature measured by the indoor temperature sensor 3, the target temperature and designated time set by the user, and the upper and lower limit values ​​of the air conditioning capacity, and calculates the optimal air conditioning capacity by solving a linear programming problem such as the example shown in FIG. 4B. The upper and lower limit values ​​of the air conditioning capacity are set in advance, and the optimal operation calculation unit 13 stores these values. The optimal air conditioning capacity calculation unit 132 outputs the optimal air conditioning capacity to the optimal indoor temperature calculation unit 133 and the optimal rotation speed calculation unit 134.

[0039] Next, the optimal operation calculation unit 13 calculates the optimal indoor temperature using the optimal indoor temperature calculation unit 133 (step S14). The optimal indoor temperature calculation unit 133 inputs the outdoor air temperature measured by the outdoor air temperature sensor 2, the indoor temperature measured by the indoor temperature sensor 3, and the initial value of the air conditioning capacity into the room temperature prediction model 131 (e.g., equation (1')) to predict the indoor temperature after one control step. The optimal indoor temperature calculation unit 133 inputs the outdoor air temperature measured by the outdoor air temperature sensor 2, the predicted value of the indoor temperature after one control step, and the air conditioning capacity after one control step based on the optimal air conditioning capacity into equation (1') to predict the indoor temperature after two control steps. Similarly, for the third control step and beyond, the optimal indoor temperature calculation unit 133 inputs the predicted indoor temperature and the air conditioning capacity specified by the optimal air conditioning capacity into the room temperature prediction model 131, and sequentially predicts the future indoor temperature for each control step.

[0040] Next, the optimal operation calculation unit 13 notifies the user of the optimal indoor temperature (step S15). The optimal operation calculation unit 13 transmits the optimal indoor temperature calculated by the optimal indoor temperature calculation unit 133 to the user terminal 30 via the communication unit 15. The user terminal 30 receives the optimal indoor temperature and outputs the received optimal indoor temperature to the display unit. This allows the user to understand how the indoor temperature will change to the target temperature through on-timer operation. The optimal indoor temperature may be transmitted to a remote control (not shown), and a predicted change in the indoor temperature may be displayed on the display unit of the remote control.

[0041] Next, the optimal operation calculation unit 13 calculates the optimal compressor rotation speed using the optimal rotation speed calculation unit 134 (step S16). The optimal rotation speed calculation unit 134 converts the optimal air-conditioning capacity acquired from the optimal air-conditioning capacity calculation unit 132 into the rotation speed of the compressor 4 using equations (2) to (4) to calculate the optimal compressor rotation speed. The optimal operation calculation unit 13 outputs the optimal compressor rotation speed to the control unit 14.

[0042] Next, the control unit 14 starts air conditioning (step S17). The control unit 14 controls the refrigerant circuit (not shown) so that air conditioning can be performed in the operation mode specified by the on-timer operation, and starts air conditioning based on the optimal compressor rotation speed. The optimal compressor rotation speed is set to, for example, the rotation speed of the compressor 4 per minute from the present (predicted time) until N minutes from now. The control unit 14 starts air conditioning at a time when a value greater than 0 is set as the rotation speed of the compressor 4, and thereafter drives the compressor 4 at the rotation speed set to the optimal compressor rotation speed. Alternatively, if there is a difference between the optimal room temperature and the actual room temperature, the control unit 14 may drive the compressor 4 at a rotation speed obtained by correcting the optimal compressor rotation speed using deviation feedback control using the difference.

[0043] 5 is an example. For example, steps S16, S14, S15, and S17 may be performed in this order, or steps S14 to S15 and steps S16 to S17 may be performed in parallel. In step S13, the outdoor air temperature at the time of prediction is always used as the outdoor air temperature. However, in cases where a change in the outdoor air temperature is predicted, predicted values ​​of the outdoor air temperature at predetermined time intervals up to N minutes from now may be obtained, or changes in the outdoor air temperature at predetermined time intervals up to N minutes from now may be predicted, and the predicted values ​​of the outdoor air temperature corresponding to the time at which the indoor temperature is predicted may be input into room temperature prediction model 131 to predict the future indoor temperature. The room temperature prediction model 131 may be created by learning time-series data on the indoor temperature, outdoor temperature, the rotation speed of the compressor 4, humidity, and amount of solar radiation. In this case, in steps S13 and S14, each process is performed using the outdoor temperature measured by the outdoor temperature sensor 2, the indoor temperature measured by the indoor temperature sensor 3, and, for example, the measured values ​​of a humidity sensor and an actinometer (not shown) installed in the room.

[0044] (Effects) As described above, according to this embodiment, the target temperature can be achieved with low power consumption by the specified time during on-timer operation. More specifically, taking into account the indoor temperature, outdoor temperature, and other factors at the time of setting the on-timer operation, the system predicts changes in room temperature based on the room temperature prediction model 131, calculates a time-series air conditioning capacity (the rotation speed of the compressor 4) that minimizes power consumption and achieves the target temperature at the specified time, and calculates the operation start time by working backward from the calculated air conditioning capacity. Then, after the operation start time, the air conditioner 1 operates based on the calculated time-series air conditioning capacity. This makes it possible to calculate the operation start time and time-series air conditioning capacity that account for changes in the target temperature and disturbances such as the outdoor temperature, thereby ensuring that the target temperature is achieved with low power consumption by the specified time.

[0045] FIG. 6 is a diagram showing an example of the hardware configuration of an air conditioning system according to an embodiment. The computer 900 includes a CPU 901, a main storage device 902, an auxiliary storage device 903, an input / output interface 904, and a communication interface 905. The above-described control device 10, server 20, and user terminal 30 are implemented in the computer 900. The above-described functions are stored in the auxiliary storage device 903 in the form of a program. The CPU 901 reads the program from the auxiliary storage device 903, loads it into the main storage device 902, and executes the above-described processing in accordance with the program. The CPU 901 allocates a storage area in the main storage device 902 in accordance with the program. The CPU 901 allocates a storage area in the auxiliary storage device 903 for storing data being processed in accordance with the program.

[0046] A program for implementing all or part of the functions of the control device 10, server 20, and user terminal 30 may be recorded on a computer-readable recording medium, and the program may be loaded into a computer system and executed to perform processing by each functional unit. Here, "computer system" includes hardware such as an OS and peripheral devices. If a WWW system is used, "computer system" also includes the homepage provision environment (or display environment). "Computer-readable recording medium" refers to portable media such as CDs, DVDs, and USBs, and storage devices such as hard disks built into the computer system. If this program is distributed to computer 900 via a communication line, the computer 900 may load the program into its main memory 902 and execute the above-described processing. The program may be for implementing part of the above-described functions, or may be capable of implementing the above-described functions in combination with a program already stored in the computer system.

[0047] As described above, several embodiments according to the present disclosure have been described. However, all of these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included within the scope and spirit of the invention, as well as the invention described in the claims and their equivalents. The control device 10 and / or server 20 are examples of a control system. Past operating data of the air conditioner 1 is an example of performance data. Equation (1) and Equation (1') are examples of linear expressions.

[0048] <Additional Notes> The control system, air conditioning system, control method, and program described in each embodiment can be understood, for example, as follows.

[0049] (1) A control system according to a first aspect is a control system for controlling an air conditioner, and includes: a setting receiving unit that receives settings of a target temperature and a designated time at which the target temperature should be achieved; an optimal operation calculation unit that calculates a time-series air conditioning capacity of the air conditioner so that the power consumption used by the air conditioner to achieve the target temperature at the designated time is minimized; and a control unit that operates the air conditioner based on the time-series air conditioning capacity. This makes it possible to achieve the target temperature by the set time with low power consumption.

[0050] (2) A control system according to a second aspect is the control system of (1), further comprising a room temperature prediction model that predicts the indoor temperature in a control step following a control step based on the indoor temperature, outdoor temperature, and air conditioning capacity in the control step, wherein the optimal operation calculation unit uses the room temperature prediction model to search for a combination of the indoor temperature, the outdoor temperature, and the air conditioning capacity in a time series that minimizes the power consumption required to achieve the target temperature at the specified time. This makes it possible to calculate a time series air conditioning capacity that enables the target temperature to be achieved by the set time with low power consumption.

[0051] (3) A control system according to a third aspect is the control system of (1) to (2), wherein the optimal operation calculation unit searches for the combination by solving a linear programming problem in which minimizing the power consumption is an objective function and achieving the target temperature at the specified time is a constraint, thereby making it possible to calculate a time-series air conditioning capacity that enables the target temperature to be achieved by the set time with low power consumption.

[0052] (4) A control system according to a fourth aspect is the control system of (1) to (3), further comprising a room temperature prediction model creation unit that creates the room temperature prediction model by calculating coefficients of the linear equation that represents the relationship between the indoor temperature, the outdoor temperature, the air conditioning capacity, and the indoor temperature in the next control step, and that creates the room temperature prediction model by minimizing a difference between the indoor temperature predicted by the linear equation and historical data of the indoor temperature, the outdoor temperature, and the air conditioning capacity, and the indoor temperature included in the historical data. This allows the room temperature prediction model to be created.

[0053] (5) A fifth aspect of the control system is the control system of any one of (1) to (4), wherein the optimal operation calculation unit predicts the change in indoor temperature according to the time-series air conditioning capacity based on the time-series air conditioning capacity and the room temperature prediction model, and outputs the predicted change in indoor temperature. This makes it possible to provide a user with a predicted change in indoor temperature when on-timer operation is set. By referring to the predicted change in indoor temperature, the user can confirm that the target temperature will be achieved by the specified time.

[0054] (6) An air conditioning system according to a sixth aspect includes the air conditioner 1 and the control system according to any one of (1) to (5).

[0055] (7) A control method according to a seventh aspect is a control method for an air conditioner, comprising the steps of: accepting settings of a target temperature and a designated time at which the target temperature should be achieved; calculating a time-series air conditioning capacity of the air conditioner so that the power consumption used by the air conditioner to achieve the target temperature at the designated time is minimized; and operating the air conditioner based on the time-series air conditioning capacity.

[0056] (8) The program according to the eighth aspect causes a computer to execute the steps of accepting settings of a target temperature and a specified time at which the target temperature should be achieved, calculating the time-series air conditioning capacity of the air conditioner so that the power consumption used by the air conditioner to achieve the target temperature at the specified time is minimized, and operating the air conditioner based on the time-series air conditioning capacity.

[0057] According to the above-described control system, air conditioning system, control method, and program, a target temperature can be achieved by a specified time with low power consumption.

[0058] 100: Air conditioning system 1: Air conditioner 2: Outdoor air temperature sensor 3: Indoor temperature sensor 4: Compressor 10: Control device 11: Sensor information acquisition unit 12: Setting reception unit 13: Optimal operation calculation unit 131: Room temperature prediction model 132: Optimal air conditioning capacity calculation unit 133: Optimal indoor temperature calculation unit 134: Optimal rotation speed calculation unit 14: Control unit 15: Communication unit 20: Server 21: Room temperature prediction model creation unit 22: Communication unit 30: User terminal 900: Computer 901: CPU 902: Main memory device 903: Auxiliary memory device 904: Input / output interface 905: Communication interface

Claims

1. A control system for controlling an air conditioner, comprising: a setting receiving unit that receives settings of a target temperature and a designated time at which the target temperature should be achieved; an optimal operation calculation unit that calculates the time-series air conditioning capacity of the air conditioner so that the power consumption used by the air conditioner to achieve the target temperature at the designated time is minimized; and a control unit that operates the air conditioner based on the time-series air conditioning capacity.

2. The control system of claim 1, further comprising a room temperature prediction model that predicts the indoor temperature in a control step following a certain control step based on the indoor temperature, outdoor temperature, and air conditioning capacity in the certain control step, wherein the optimal operation calculation unit uses the room temperature prediction model to search for a combination of the indoor temperature, the outdoor temperature, and the air conditioning capacity in a time series such that the power consumption for achieving the target temperature at the specified time is minimized.

3. The control system according to claim 2, wherein the optimal operation calculation unit searches for the combination by solving a linear programming problem with minimizing the power consumption as an objective function and achieving the target temperature at the specified time as a constraint.

4. The control system of claim 2 or 3, further comprising a room temperature prediction model creation unit that creates the room temperature prediction model by calculating coefficients of the linear equation so that the difference between the indoor temperature predicted by the linear equation and actual data of the indoor temperature, the outdoor temperature, and the air conditioning capacity in the certain control step and the indoor temperature in the next control step is minimized, the room temperature prediction model being a linear equation that shows the relationship between the indoor temperature, the outdoor temperature, and the air conditioning capacity in the certain control step, and the indoor temperature in the next control step, and the indoor temperature included in the actual data is minimized.

5. A control system as described in claim 2 or claim 3, wherein the optimal operation calculation unit predicts the change in indoor temperature according to the time-series air conditioning capacity based on the time-series air conditioning capacity and the room temperature prediction model, and outputs the predicted change in indoor temperature.

6. An air conditioning system comprising: an air conditioner; and a control system according to any one of claims 1 to 3.

7. A method for controlling an air conditioner, comprising the steps of: accepting settings of a target temperature and a designated time at which the target temperature should be achieved; calculating a time-series air conditioning capacity of the air conditioner that minimizes the power consumption used by the air conditioner to achieve the target temperature at the designated time; and operating the air conditioner based on the time-series air conditioning capacity.

8. A program that causes a computer to execute the steps of: accepting settings of a target temperature and a specified time at which the target temperature should be achieved; calculating a time-series air conditioning capacity of the air conditioner that minimizes the power consumption used by the air conditioner to achieve the target temperature at the specified time; and operating the air conditioner based on the time-series air conditioning capacity.

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