Control device, control method, and program

Abstract

To provide a control device that allows a look-up table capable of predicting heat response parameters for a preset temperature quickly with a low load to be learned even when only a limited calculation resource is mounted.SOLUTION: A control device includes: an extraction unit for extracting an element included in a monitoring section from a predetermined start condition to a termination condition of air conditioning control by an air conditioner from time-series data in which a room temperature of an air conditioning object space is measured; an estimation unit for estimating a time constant estimation value of a primary delay response of the air conditioning control and a dead time estimation value on the basis of time series data in the monitoring section; and a learning unit for learning a look-up table that associates an explanation variable which is a condition affecting air conditioning of the air conditioning object space with the dead time estimation value and the time constant estimation value.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a control device, a control method, and a program. [Background technology]

[0002] For example, when an air conditioner is operated in cooling mode, it takes time for the indoor temperature to reach the target temperature set by the user after it is turned on, and the indoor temperature during that time is not comfortable for the person in the room (user). For this reason, in order to make the room comfortable when the user arrives, the air conditioner (indoor unit) is manually turned on before the user begins to be in the room, or a function that automatically starts pre-cooling a certain amount of time before (for example, 15 minutes or 30 minutes before) (warming-up operation) is used.

[0003] The appropriate pre-cooling time not only differs for each air-conditioned space, such as a room, but also changes depending on conditions such as the outside temperature. Therefore, if the time at which pre-cooling starts automatically is fixed, as in the warm-up operation described above, the pre-cooling time may be too long or too short. If the pre-cooling time of the air conditioner is too long, it will waste energy and lead to higher electricity bills. Also, if the pre-cooling time is insufficient, comfort will be compromised.

[0004] In addition, air conditioners are designed to be controlled in response to changes in conditions such as outside temperature, taking into account the dead time between when an operation is performed and when the result appears, and thermal response parameters including a first-order lag (see, for example, Patent Documents 1 and 2). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2001-350503 A [Patent Document 2] JP 2002-054837 A Summary of the Invention [Problem to be solved by the invention]

[0006] Sufficient computational resources (memory and CPU) are required to calculate the thermal response parameters in real time according to the ever-changing conditions and to calculate the pre-cooling time. However, most general air conditioner control devices (microcomputers) are equipped with only limited computational resources.

[0007] An object of the present disclosure is to provide a control device, a control method, and a program that can learn a lookup table that can predict thermal response parameters to a set temperature quickly and with low load, even when only limited computing resources are installed. [Means for solving the problem]

[0008] According to one aspect of the present disclosure, the control device includes an extraction unit that extracts elements included in a monitoring period from a predetermined start condition to an end condition of air conditioning control by an air conditioner from time series data measuring the indoor temperature in the air conditioned space, an estimation unit that estimates a time constant estimate and a dead time estimate of a first order response of the air conditioning control based on the time series data of the monitoring period, and a learning unit that learns a lookup table that matches explanatory variables, which are conditions that affect the air conditioning of the air conditioned space, with the dead time estimate and the time constant estimate.

[0009] According to one aspect of the present disclosure, a control method includes the steps of: extracting elements included in a monitoring period from a predetermined start condition to an end condition of air conditioning control by an air conditioner from time series data measuring indoor temperature in a space to be air conditioned; estimating a time constant estimate and a dead time estimate of a first-order lag response of the air conditioning control based on the time series data of the monitoring period; and learning a lookup table that matches explanatory variables, which are conditions that affect the air conditioning of the space to be air conditioned, with the dead time estimate and the time constant estimate.

[0010] According to one aspect of the present disclosure, the program causes a control device to execute the steps of: extracting elements included in a monitoring period from a predetermined start condition to an end condition of air conditioning control by an air conditioner from time series data measuring the indoor temperature in the space to be air conditioned; estimating a time constant estimate and a dead time estimate of a first-order lag response of the air conditioning control based on the time series data of the monitoring period; and learning a lookup table that matches explanatory variables, which are conditions that affect the air conditioning of the space to be air conditioned, with the dead time estimate and the time constant estimate. Effect of the Invention

[0011] According to the above aspect, even if only limited computational resources are installed, it is possible to learn a lookup table capable of predicting thermal response parameters to a set temperature quickly and with low load. [Brief description of the drawings]

[0012] [Figure 1] 1 is a diagram showing a functional configuration of an air conditioning system according to a first embodiment. [Diagram 2] FIG. 2 is a diagram illustrating an example of a prediction model (lookup table) according to the first embodiment. [Diagram 3] FIG. 2 is a diagram showing an example of explanatory variables and level category assignment according to the first embodiment. [Figure 4] 5 is a flowchart showing an example of a thermal response parameter estimation process according to the first embodiment. [Diagram 5] 1 is a flowchart showing an example of a learning process of a prediction model according to the first embodiment. [Figure 6] FIG. 4 is a block diagram showing an example of a lookup table update process according to the first embodiment. [Figure 7] 10 is a flowchart showing an example of a prediction process of a predicted arrival time according to the first embodiment. [Figure 8] 4 is a block diagram showing an example of a prediction process of a predicted arrival time according to the first embodiment. FIG. [Figure 9]FIG. 4 is a diagram showing a display example of a terminal device according to the first embodiment. [Figure 10] FIG. 4 is a diagram illustrating an example of update processing of a lookup table according to the first embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] <First embodiment> Hereinafter, a control method according to the first embodiment will be described with reference to the drawings. The control method according to the present embodiment can be applied to the control of various heat source devices such as air conditioners, water heaters, and freezers, but the following description will be given taking the case of application to an air conditioner as an example.

[0014] (Overall configuration of air conditioning system) FIG. 1 is a diagram showing the functional configuration of an air conditioning system according to the first embodiment. As shown in FIG. 1, the air conditioning system 100 includes an air conditioner 1 and a terminal device 20. The air conditioner 1 includes an outdoor air temperature sensor 2, an indoor temperature sensor 3, a memory 4, an indoor unit 5, and an outdoor unit 6. The outdoor air temperature sensor 2 and the indoor temperature sensor 3 are connected to a 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 indoor unit 5 and the outdoor unit 6 are connected to the control device 10, and the control device 10 controls the indoor unit 5 and the outdoor unit 6 and detects their operating states. The control device 10 and the terminal device 20 are connected to be able to communicate with each other via a network NW. The terminal device 20 is, for example, a smartphone or tablet equipped with an application capable of remotely controlling the air conditioner 1.

[0015] (Functional configuration of the control device) The control device 10 is a computer equipped with a CPU (Central Processing Unit) such as a microcomputer or a MPU (Micro Processing Unit). The control device 10 includes an information acquisition unit 11, an extraction unit 12, an estimation unit 14, a learning unit 15, a prediction unit 16, a control unit 17, and a communication unit 18.

[0016] The information acquisition unit 11 acquires the outdoor temperature measured by the outdoor temperature sensor 2 and the indoor temperature measured by the indoor temperature sensor 3 .

[0017] The extraction unit 12 extracts elements included in a monitoring period from a predetermined start condition to an end condition of air conditioning control by the air conditioner 1 from time series data measuring the indoor temperature in the room (air conditioned space) in which the air conditioner 1 is installed.

[0018] The estimation unit 14 estimates a time constant estimate value τ and a dead time estimate value L of the first-order lag response of air conditioning control based on the time-series data of the monitoring section.

[0019] The learning unit 15 learns a prediction model (lookup table) that associates explanatory variables, which are conditions that affect the air conditioning of the air conditioned space, with the dead time estimate value L and the time constant estimate value τ. The learned lookup table is stored in the memory 4.

[0020] The prediction unit 16 predicts a predicted time D until the temperature of the air conditioned space reaches the target temperature, based on the trained prediction model (lookup table) read out from the memory 4 and the current explanatory variables.

[0021] The control unit 17 controls the indoor unit 5 and the outdoor unit 6. Based on the settings of the set temperature and set time received from the terminal device 20 via the communication unit 18 and the predicted arrival time D predicted by the prediction unit 16, the control unit 17 operates the indoor unit 5 and the outdoor unit 6 so that the air-conditioned space approaches the set temperature at the set time. For example, the control unit 17 starts air conditioning at a time that is the predicted arrival time D earlier than the set time or slightly earlier than that time, and performs cooling operation or heating operation so that the set temperature is achieved by the set time.

[0022] The communication unit 18 communicates with the terminal device 20. For example, the communication unit 18 transmits the predicted arrival time D predicted by the prediction unit 16 to the terminal device 20. The communication unit 18 may also receive setting conditions such as a set temperature and a set time, and operation instructions such as turning on the power of the air conditioner 1 from the terminal device 20.

[0023] (About the prediction model) Fig. 2 is a diagram showing an example of a prediction model (lookup table) according to the first embodiment. Fig. 3 is a diagram showing an example of an allocation of explanatory variables and level categories according to the first embodiment. As shown in FIG. 2, in this embodiment, the prediction model of the thermal response parameter is expressed by a lookup table LUT of the time constant estimate value τ and the dead time estimate value L for each operation mode. In addition, in the example of FIG. 2, the table of the time constant estimate value τ and the table of the dead time estimate value L each have a table for each operation mode (heating operation and cooling operation). Therefore, the lookup table LUT according to this embodiment has four tables, namely, a time constant table LUT1 for cooling operation, a dead time table LUT2 for cooling operation, a time constant table LUT3 for heating operation, and a dead time table LUT4 for heating operation. In the example of FIG. 2, a table using two explanatory variables is illustrated, but this is not limited to this. In other embodiments, the explanatory variable may be only one, or may be three or more. In addition, each explanatory variable has a plurality of level categories. The level category of the explanatory variable is used as an index for identifying a cell of each table.

[0024] For example, as shown in Fig. 3, assume that explanatory variable 1 is air volume and explanatory variable 2 is outdoor air temperature. For air volume, a level category is assigned for each air volume setting value of the fan of the indoor unit 5. For outdoor air temperature, a level category is assigned for each range of the measurement value of the outdoor air temperature sensor 2. Note that, as in the example of Fig. 3, the level category of the outdoor air temperature may be set to different temperature ranges for cooling operation and heating operation.

[0025] That is, in the examples of Fig. 2 and Fig. 3, each table records in each cell an estimated value of a thermal response parameter (time constant τ or dead time L) corresponding to a combination of air volume and outside air temperature for each operation mode (heating operation or cooling operation). In this way, different thermal response parameters can be updated and referenced depending on the settings of the air conditioner 1 and environmental conditions. Also, each cell records the number of samples n (the number of times the thermal response parameter is recorded). The process of updating the lookup table (learning process) and the process of referencing it (prediction process) will be described later.

[0026] (Thermal response parameter estimation process) FIG. 4 is a flowchart showing an example of a thermal response parameter estimation process according to the first embodiment. Here, the flow of the process in which the control device 10 estimates the thermal response parameters will be described with reference to Fig. 4. When the air conditioner 1 is operating, the control device 10 repeatedly executes the series of processes shown in Fig. 2 at predetermined control cycles (for example, every 50 ms). During this time, the information acquisition unit 11 sequentially acquires sensor measurement values ​​and set values ​​such as the outdoor air temperature, indoor temperature, and air volume from the outdoor air temperature sensor 2, indoor temperature sensor 3, indoor unit 5, etc.

[0027] First, the extraction unit 12 determines whether the start condition of the monitoring period has been satisfied (step S100). The monitoring period is a period for monitoring (extracting and retaining) data to estimate a thermal response parameter in the air conditioning control of the air conditioner 1. For example, the extraction unit 12 determines that the start condition of the monitoring period has been satisfied when the indoor unit 5 is switched ON or the set temperature is changed.

[0028] If the start condition is not satisfied (step S100; NO), the extraction unit 12 ends the process. On the other hand, if the start condition is satisfied (step S100; YES), the extraction unit 12 starts extracting and storing the necessary information (step S101). Thereafter, the extraction unit 12 records the measured value (time series data) of the indoor temperature in a buffer secured in the memory 4 for each predetermined control period. Also, only when the extraction unit 12 starts recording the indoor temperature, it records in the memory 4 the operation mode of the air conditioner (heating operation or cooling operation) and explanatory variables used in the learning process described below. The explanatory variables are, for example, the time when the measured value retention start point, the outdoor air temperature, the air volume of the indoor unit 5, the temperature gap between the indoor temperature and the set temperature, etc.

[0029] Next, the extractor 12 determines whether a predetermined end condition is satisfied (step S102). For example, the extractor 12 determines that the end condition is satisfied when the end of the buffer (upper limit number or upper limit amount of time-series data) is reached, when the indoor unit 5 is switched OFF, when the set temperature is changed, etc.

[0030] If the termination condition is not satisfied (step S102; NO), the extraction unit 12 temporarily terminates the process. At this time, the time series data of the indoor temperature continues to be stored. Therefore, until the extraction unit 12 determines that the termination condition is satisfied, the indoor temperature at each step of the control cycle is extracted and stored in a buffer. On the other hand, if the termination condition is satisfied (step S102; YES), the extraction unit 12 further determines whether the monitoring section is a valid section (step S103).

[0031] For example, if the length of the data (time series data of indoor temperature) recorded in the buffer is less than a threshold value, or if the indoor temperature does not reach the set temperature before the end of the buffer is reached, the extraction unit 12 determines that the monitoring section is invalid (step S103; NO) when the time series data does not meet a predetermined criterion (i.e., it is difficult to estimate the thermal response parameters based on the time series data). In this case, the extraction unit 12 clears the buffer (step S106) and ends the process. On the other hand, if the time series data meets the criterion, the extraction unit 12 determines that the monitoring section is valid (step S103; YES).

[0032] Next, the estimator 14 estimates the thermal response parameters (step S105). The estimator 14 according to the present embodiment estimates the length of dead time and the time constant of the first-order lag response as the thermal response parameters. The method of estimating the dead time estimate value L and the time constant estimate value τ of the first-order lag response may utilize known techniques.

[0033] The estimation unit 14 stores the thermal response parameters, including the dead time estimate value L and the time constant estimate value τ, in the memory 4 in association with the previously recorded operation mode and explanatory variables (step S106). After that, the estimation unit 14 clears the buffer (step S107) and ends the process.

[0034] (Predictive model learning process) Fig. 5 is a flowchart showing an example of a learning process of a prediction model according to the first embodiment. Fig. 6 is a block diagram showing an example of an update process of a lookup table according to the first embodiment. Here, a process flow for the control device 10 to learn a prediction model will be described with reference to Fig. 5 to Fig. 6. Note that the control device 10 executes a series of processes shown in Fig. 5 every time the estimation unit 14 estimates a thermal response parameter.

[0035] First, the learning unit 15 reads out from the memory 4 the estimated value of the thermal response parameter newly estimated by the estimation unit 14 and the explanatory variables recorded together with the estimated value (step S200).

[0036] Next, the learning unit 15 determines the index of the cell in which the newly estimated value of the thermal response parameter is to be stored (step S201).

[0037] First, the learning unit 15 determines the storage destination to be either the table for heating operation or the table for cooling operation based on the operation mode read from the memory 4. Furthermore, the learning unit 15 determines the index of the storage destination cell based on the value of the explanatory variable read from the memory 4. For example, assume that the operation mode is heating operation, explanatory variable 1 (air volume) is Middle, and explanatory variable 2 (outdoor temperature) is 6°C. According to the example of Figs. 2 to 3, explanatory variable 1 corresponds to level 2, and explanatory variable 2 corresponds to level 3. Therefore, in this case, the learning unit 15 determines the cell where the row of explanatory variable 1 of level 2 intersects with the column of explanatory variable 2 of level 3 in the time constant table LUT3 for heating operation and the dead time table LUT4 for heating operation shown in Fig. 2 as the storage destination cell for the time constant estimate value τ(tau_est) and the dead time estimate value L(lag_est), respectively.

[0038] Next, the learning unit 15 calculates the average value of the new estimated value and the past estimated value (step S202). First, the learning unit 15 reads out the value recorded in the storage cell of each table as the past average value (tau_avg_old, lag_avg_old).

[0039] Further, the learning unit 15 calculates an average value (tau_avg_new) between the new time constant estimate (tau_est) and the average value (tau_avg_old) of the past time constant estimates read from the storage cell. Similarly, the learning unit 15 calculates an average value (lag_avg_new) between the new dead time estimate (lag_est) and the average value (lag_avg_old) of the past dead time estimates read from the storage cell. At this time, the learning unit 15 may calculate the new average value by a weighted average with a weight α added, as in the following formulas (1) and (2). The weight α may be set to any value in the range of "0<α≦1". For example, by setting the weight α to be larger than 0.5, the influence of the new estimate value is largely reflected, and the effect of forgetting the past estimate value can be obtained. This makes it possible to make the estimate value of the thermal response parameter follow the actual value, for example, when the thermal response parameter gradually changes due to aging deterioration or the like.

[0040] tau_avg_new=α*tau_est+(1-α)*tau_avg_old …(1)

[0041] lag_avg_new=α*lag_est+(1-α)*lag_avg_old …(2)

[0042] The learning unit 15 inputs the calculated new average values ​​(tau_avg_new, lag_avg_new) into the storage destination cells of each table to update the lookup table LUT (step S203). At this time, the learning unit 15 increments the number of samples n in the storage destination cells by +1.

[0043] If the number of samples n of the storage cell is 0, that is, if this is the first time that thermal response parameters are input to this storage cell, the learning unit 15 does not calculate the average value, but inputs the thermal response parameter estimates (tau_est, lag_est) read from the memory 4 directly to the storage cell, and updates the lookup table LUT (step S202).

[0044] (Prediction process for estimated arrival time) Fig. 7 is a flowchart showing an example of a process for predicting a predicted arrival time according to the first embodiment. Fig. 8 is a block diagram showing an example of a process for predicting a predicted arrival time according to the first embodiment. Here, a process flow for predicting a predicted time for the room temperature to reach a set temperature will be described with reference to Fig. 7 and Fig. 8. Note that the control device 10 executes a series of processes shown in Fig. 7 when, for example, an application of the terminal device 20 is started or operated.

[0045] First, the information acquisition unit 11 acquires explanatory variables to be used for prediction (step S300). For example, when explanatory variable 1 is air volume and explanatory variable 2 is outdoor temperature, the information acquisition unit 11 acquires the air volume setting value of the air conditioner 1 (indoor unit 5) and acquires the current outdoor temperature from the outdoor temperature sensor 2. The information acquisition unit 11 may also acquire the current set temperature (when the power is OFF, the set temperature at the time of the previous operation) at the same time.

[0046] Next, the prediction unit 16 reads out the thermal response parameters corresponding to the obtained explanatory variables (step S301). For example, if the current operation mode (the operation mode during the previous operation in the case of power OFF) is heating operation, the prediction unit 16 reads out the parameters (tau_avg, lag_avg) corresponding to the explanatory variables from the time constant table LUT3 and the dead time table LUT4 for heating operation. The method of determining the index of the storage cell for each parameter is the same as that of step S201 in FIG. 6.

[0047] Furthermore, the prediction unit 16 predicts a predicted time D for the indoor temperature of the air-conditioned space to reach the target temperature based on the read thermal response parameters (step S302). The predicted time D is calculated, for example, by the following formula (3). The target temperature is expressed, for example, as x% (any value, such as 90%, 95%) of the current set temperature. Furthermore, the coefficient C of the time constant τ (tau_avg) is a value of 1 or more and can be changed according to the target temperature. For example, when the target temperature is 95% of the set temperature, C=3, and when the target temperature is 90% of the set temperature, C=2.3.

[0048] D = C × tau_avg + lag_avg … (3)

[0049] Next, the communication unit 18 transmits the predicted arrival time D predicted by the prediction unit 16 to the terminal device 20 (step S303).

[0050] FIG. 10 is a diagram showing a display example of the terminal device according to the first embodiment. For example, as shown in Fig. 10, information such as current information C201 and predicted information C202 is displayed on the application screen of the terminal device 20, along with an operation reception section C203 that receives remote operation of the air conditioner 1 by the user. The current information C201 includes information such as the current date and time, the indoor temperature, the outdoor temperature, and the set temperature. The predicted information C202 includes the predicted arrival time D received from the control device 10 of the air conditioner 1. The predicted information C202 may also include a predicted time obtained by adding the predicted arrival time D to the current time. The operation reception section C203 may also include a "ON / OFF NOW button", a "TIMER SETTING ON / OFF button", a "TIMER DATE AND TIMER SETTING button", etc.

[0051] The user remotely controls the air conditioner 1 via the operation reception unit C203 while referring to the current time and the predicted time D until the set temperature is reached. For example, in the example of FIG. 10, the predicted time D until the set temperature is reached is approximately 13 minutes, and the user plans to be in the air-conditioned space for about 15 minutes. In this case, the user operates the "ON / OFF NOW button" to turn on the power of the air conditioner 1. Then, the control unit 17 of the control device 10 acquires an operation instruction (power ON) from the terminal device 20 via the communication unit 18 (step S304), and controls the power ON of the air conditioner 1 (indoor unit 5) (step S305).

[0052] Also, for example, if there is time before the user enters the air-conditioned space, the user may operate the "Timer setting ON / OFF" button in the operation reception unit C203 of the terminal device 20 to turn on the timer setting. Also, the user may operate the timer date and time setting to set the room temperature to the set temperature at any set time. In this case, the control unit 17 of the control device 10 acquires an operation instruction (timer setting ON and set time) from the terminal device 20 through the communication unit 18 (step S304), and automatically starts air conditioning at a time that is the predicted arrival time D before the set time or slightly before that time so that the air-conditioned space will reach the set temperature at the set time set by the user, and performs cooling operation or heating operation so as to achieve the set temperature by the set time (step S305).

[0053] The user may also change the current set temperature. For example, the user may be able to perform an operation to change the set temperature by tapping the set temperature portion of the current information C201. Then, the control device 10 acquires an operation instruction (an instruction to change the set temperature) from the terminal device 20 through the communication unit 18. In this case, the control device 10 may return to step S302 and re-predict the predicted arrival time.

[0054] (Action, effect) As described above, the control device 10 according to this embodiment includes an extraction unit 12 that extracts elements included in a monitoring period from a predetermined start condition to an end condition from time series data obtained by measuring the indoor temperature in the space to be air conditioned, an estimation unit 14 that estimates a time constant estimate τ and a dead time estimate L of the first-order lag response of the air conditioning control based on the time series data of the monitoring period, and a learning unit 15 that learns a lookup table LUT that associates explanatory variables, which are conditions that affect the air conditioning of the space to be air conditioned, with the dead time estimate L and the time constant estimate τ.

[0055] In this way, the control device 10 can learn a lookup table that serves as a substitute for a predictive model with a simpler and lighter algorithm than learning a predictive model using a statistical method, machine learning, etc. In other words, even if the control device 10 is equipped with only limited computational resources, it can learn a lookup table LUT that can predict thermal response parameters to a set temperature quickly and with a low load.

[0056] In addition, the learning unit 15 determines a storage cell corresponding to the explanatory variable from the lookup table LUT, and updates the dead time estimate value and the time constant estimate value in the lookup table LUT by taking a weighted average of the new estimate value by the estimation unit 14 and the past estimate value recorded in the storage cell.

[0057] In this way, the control device 10 can update the lookup table LUT to reflect new estimated values ​​and gradually forget past estimated values, thereby allowing the influence of, for example, aging deterioration on the thermal response parameters to be reflected in the lookup table LUT.

[0058] Moreover, the control device 10 further includes a prediction unit 16 that predicts a predicted time D until the temperature of the air conditioned space reaches the target temperature, based on the lookup table LUT and the current explanatory variables.

[0059] In this way, the control device 10 can use the thermal response parameters of the lookup table LUT to accurately predict the predicted time D until the temperature of the air conditioned space reaches the target temperature.

[0060] <Second embodiment> Next, a second embodiment will be described with reference to Fig. 10. Components common to the above-mentioned embodiment will be given the same reference numerals and detailed description will be omitted.

[0061] In the first embodiment, the learning unit 15 updates the new thermal response parameter estimates (tau_est, lag_est) by reflecting them in only one storage destination cell. In the present embodiment, the learning unit 15 updates the storage destination cell and its surrounding cells by reflecting the new thermal response parameter estimates.

[0062] FIG. 10 is a diagram illustrating an example of update processing of the lookup table according to the first embodiment. Here, an example of updating the time constant table LUT1 for cooling operation will be described. For example, as shown in FIG. 10, the learning unit 15 determines the storage cell of the new time constant estimate value tau_est to be cell P1 (step S201 in FIG. 5). In this embodiment, in this step S201, the learning unit 15 determines at least one cell surrounding the storage cell P1 as a target cell in which the new time constant estimate value tau_est is reflected. The surrounding target cell is, for example, cell P2 adjacent to the storage cell P1 on the top, bottom, left and right. The surrounding target cells may further include cell P3 adjacent to the storage cell P1 on the diagonal line.

[0063] Next, the learning unit 15 inputs a weighted average of the new time constant estimate tau_est and the past time constant estimate tau_avg_old to the storage cell P1, as in the first embodiment (step S203 in FIG. 5). Furthermore, the learning unit 15 according to this embodiment reflects the new time constant estimate tau_est not only to the storage cell P1, but also to the target cells P2 and P3 surrounding the storage cell P1.

[0064] Specifically, when the average value tau_avg_old of the past time constant estimates is not recorded (is null) in the target cells P2 and P3, the learning unit 15 directly substitutes the new time constant estimate value tau_est into the target cells P2 and P3. On the other hand, when the average value tau_avg_old of the past time constant estimates is recorded (is non-null) in the target cells P2 and P3, the learning unit 15 calculates a new time constant average value tau_avg_new using formula (1) (step S202 in FIG. 5). At this time, the learning unit 15 calculates the average value for the target cells P2 and P3 by making the value of the weight α smaller than that of the storage destination cell P1. In addition, the learning unit 15 inputs the calculated new time constant average value tau_avg_new into the target cells P2 and P3 to update the table (step S203 in FIG. 5).

[0065] In the example of FIG. 10, among the target cells P2 and P3, the cells P2a, P2c, P3a, P3b, and P3c are null, so the learning unit 15 assigns the new time constant estimate value tau_est to these cells as is. Also, since the cells P2b and P3d are not null, the weighted average of the new time constant estimate value tau_est and the average value tau_avg_old of the past time constant estimate values ​​is assigned to the cells P2b and P3d as the new average value tau_avg_new of the time constant estimate values ​​using the formula (1). Note that the value of the weight α may be changed for each of the target cells P2 and P3 so that the value of the weight α decreases as the distance between the storage destination cell P1 and the target cell increases. In the example of FIG. 10, the value of the weight α of the target cell P2 may be set to be greater than the value of the weight α of the target cell P3.

[0066] The learning unit 15 also performs the same process on the dead time estimate value.

[0067] (Action, effect) As described above, in the control device 10 according to the second embodiment, the learning unit 15 determines at least one target cell P2, P3 surrounding the storage cell P1, and updates the dead time estimate and the time constant estimate in the lookup table LUT by taking a weighted average of the new estimate by the estimation unit 14 and the past estimates recorded in the target cells P2, P3.

[0068] For example, depending on the operation mode (cooling operation or heating operation), there may be a bias between cells with a large number of samples and cells with a small number of samples (or no samples at all). On the other hand, since the level categories of the explanatory variables of adjacent cells are close, it is considered that the estimated values ​​of the thermal response parameters are also close. Therefore, as described above, the learning unit 15 according to this embodiment can make up for the lack of samples by reflecting the new estimated values ​​of the thermal response parameters to the target cells P2 and P3 surrounding the storage destination cell P1 specified by the explanatory variables. In addition, for example, in the early stage of learning, the contents of the lookup table LUT can be quickly enriched with a small number of samples. This can reduce the possibility that it is difficult to predict the predicted arrival time D due to the lack of samples (null).

[0069] <Other embodiments> Although one 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.

[0070] For example, in the above embodiment, an example has been described in which the control device 10 (learning unit 15) of the air conditioner 1 learns a prediction model (lookup table LUT), but the present invention is not limited to this. In another embodiment, the thermal response parameters estimated by the control devices 10 (estimation units 14) of multiple air conditioners 1 may be transmitted to a server and aggregated, and the server may learn a prediction model. In this case, the server performs statistical processing or machine learning from a large number of thermal response parameters to generate and update a prediction model. In addition, the server periodically predicts thermal response parameters (time constant τ, dead time L) for each air conditioner 1 using the prediction model and transmits them to each air conditioner 1. The control device 10 (learning unit 15) of the air conditioner 1 overwrites the thermal response parameters received from the server to the lookup table LUT in the memory 4. In still another embodiment, an application of the terminal device 20 may acquire thermal response parameters from the control device 10 and learn a prediction model. The application of the terminal device 20 periodically predicts the thermal response parameters (time constant τ, dead time L) of the air conditioner 1 using a prediction model and transmits the predicted values ​​to the air conditioner 1. The control device 10 (learning unit 15) of the air conditioner 1 overwrites the lookup table LUT in the memory 4 with the thermal response parameters received from the terminal device 20. In this way, the processing load of the learning unit 15 can be reduced.

[0071] In the above embodiment, the control device 10 (prediction unit 16) of the air conditioner 1 determines a storage cell corresponding to the explanatory variable from the lookup table LUT, and predicts the predicted arrival time D based on the thermal response parameter read from the storage cell, but the present invention is not limited to this. In another embodiment, the prediction unit 16 may predict the predicted arrival time D using the average value of the estimated values ​​of the thermal response parameters of the cells surrounding the storage cell when, for example, the estimated value of the thermal response parameter is not recorded (is null) in the storage cell. For example, as in the example of FIG. 10, when the storage cell P1 is null, the prediction unit 16 predicts the predicted arrival time D using the average value of the estimated values ​​read from the surrounding target cells P2 and P3. In this way, even if there are many null cells in the lookup table LUT, for example, at the beginning of learning, it is possible to make a prediction using the estimated values ​​of the surrounding target cells P2 and P3.

[0072] <Additional Notes> The control device, the control method, and the program described in the above-described embodiment can be understood, for example, as follows.

[0073] (1) According to the first aspect, the control device 10 includes an extraction unit that extracts elements included in a monitoring period from a predetermined start condition to an end condition of air conditioning control by an air conditioner from time series data measuring the indoor temperature in the air conditioned space, an estimation unit that estimates a time constant estimate and a dead time estimate of a first-order lag response of the air conditioning control based on the time series data of the monitoring period, and a learning unit that learns a lookup table that associates explanatory variables, which are conditions that affect the air conditioning of the air conditioned space, with the dead time estimate and the time constant estimate.

[0074] In this way, the control device 10 can learn a lookup table that serves as a substitute for a predictive model with a simpler and lighter algorithm than learning a predictive model using a statistical method, machine learning, etc. In other words, even if the control device 10 is equipped with only limited computational resources, it can learn a lookup table LUT that can predict thermal response parameters to a set temperature quickly and with a low load.

[0075] (2) According to the second aspect, in the control device 10 of the first aspect, the learning unit determines a storage cell corresponding to the explanatory variable from the lookup table, and updates the dead time estimate and the time constant estimate in the lookup table by taking a weighted average of a new estimate by the estimation unit and a past estimate recorded in the storage cell.

[0076] In this way, the control device 10 can update the lookup table LUT to reflect new estimated values ​​and gradually forget past estimated values, thereby allowing the influence of, for example, aging deterioration on the thermal response parameters to be reflected in the lookup table LUT.

[0077] (3) According to a third aspect, in the control device 10 relating to the first or second aspect, the learning unit determines at least one target cell surrounding the storage cell, and updates the dead time estimate and the time constant estimate in the lookup table by taking a weighted average of a new estimate by the estimation unit and a past estimate recorded in the target cell.

[0078] For example, depending on the operation mode (cooling operation or heating operation), there may be a bias between cells with a large number of samples and cells with a small number of samples (or no samples at all). On the other hand, since the level categories of the explanatory variables are close between adjacent cells, it is considered that the estimated values ​​of the thermal response parameters are also close. Therefore, as described above, the learning unit 15 according to this embodiment can make up for the lack of samples by reflecting new estimated values ​​of the thermal response parameters also in the target cells P2 and P3 surrounding the storage destination cell P1 specified by the explanatory variables. Also, for example, in the early stage of learning, the contents of the lookup table LUT can be quickly enriched with a small number of samples.

[0079] (4) According to a fourth aspect, the control device 10 according to any one of the first to third aspects further includes a prediction unit that predicts a predicted time until the air-conditioned space reaches a target temperature based on the lookup table and the current explanatory variables.

[0080] In this way, the control device 10 can use the thermal response parameters of the lookup table LUT to accurately predict the predicted time D until the temperature of the air conditioned space reaches the target temperature.

[0081] (5) According to a fifth aspect, the control method includes the steps of: extracting elements included in a monitoring period from a predetermined start condition to an end condition of air conditioning control by an air conditioner from time series data measuring indoor temperature in a space to be air conditioned; estimating a time constant estimate and a dead time estimate of a first-order lag response of the air conditioning control based on the time series data of the monitoring period; and learning a lookup table that associates explanatory variables, which are conditions that affect the air conditioning of the space to be air conditioned, with the dead time estimate and the time constant estimate.

[0082] (6) According to a sixth aspect, the program causes the control device to execute the steps of: extracting elements included in a monitoring period from a predetermined start condition to an end condition of air conditioning control by an air conditioner from time series data measuring indoor temperature in the air conditioned space; estimating a time constant estimate and a dead time estimate of a first-order lag response of the air conditioning control based on the time series data of the monitoring period; and learning a lookup table that associates explanatory variables, which are conditions that affect the air conditioning of the air conditioned space, with the dead time estimate and the time constant estimate. [Explanation of symbols]

[0083] 1 Air conditioner 2. Outside Air Temperature Sensor 3. Indoor temperature sensor 4. Memory 5 Indoor unit 6 Outdoor unit 10 Control device 11 Information acquisition department 12 Extraction part 14 Estimation part 15 Learning Department 16 Prediction Department 17 Control section 18 Communications Department 20 Terminal Equipment 100 Air Conditioning System LUT Lookup Table

Claims

1. An extraction unit extracts elements included in the monitoring interval from a predetermined start condition to an end condition of the air conditioning control by the air conditioner, from time-series data of indoor temperature measured in the space to be air-conditioned. An estimation unit that estimates the time constant and dead time of the first-order lag response of the air conditioning control based on the time-series data of the monitoring interval, A learning unit learns a lookup table that associates explanatory variables, which are conditions that affect the air conditioning of the air-conditioned space, with the estimated dead time and the estimated time constant. Equipped with, The aforementioned learning unit, From the lookup table, determine the storage cell corresponding to the explanatory variable. The new estimated value from the estimation unit and the past estimated value recorded in the storage cell are weighted and averaged to update the dead time estimate and the time constant estimate in the lookup table. Control device.

2. The aforementioned learning unit, Determine at least one target cell surrounding the aforementioned storage destination cell, The new estimate from the estimation unit and the past estimates recorded in the target cell are weighted and averaged to update the dead time estimate and the time constant estimate in the lookup table. The control device according to claim 1.

3. The system further includes a prediction unit that predicts the estimated time it will take for the air-conditioned space to reach a target temperature, based on the lookup table and the current explanatory variables. The control device according to claim 1 or 2.

4. The steps include: extracting elements from time-series data of indoor temperature measurements in the space to be air-conditioned, within the monitoring interval from a predetermined start condition to an end condition for air conditioning control by the air conditioner; The steps include: estimating the time constant and dead time estimates of the first-order lag response of the air conditioning control based on the time-series data of the monitoring interval; The steps include learning a lookup table that associates explanatory variables, which are conditions affecting the air conditioning of the air-conditioned space, with the estimated dead time and the estimated time constant, It has, The aforementioned learning steps are: From the lookup table, determine the storage cell corresponding to the explanatory variable. The new estimated value obtained in the estimation step and the past estimated value recorded in the storage cell are weighted and averaged to update the dead time estimate and the time constant estimate in the lookup table. Control method.

5. The steps include: extracting elements from time-series data of indoor temperature measurements in the space to be air-conditioned, within the monitoring interval from a predetermined start condition to an end condition for air conditioning control by the air conditioner; The steps include: estimating the time constant and dead time estimates of the first-order lag response of the air conditioning control based on the time-series data of the monitoring interval; The steps include learning a lookup table that associates explanatory variables, which are conditions affecting the air conditioning of the air-conditioned space, with the estimated dead time and the estimated time constant, A program that causes the control device to execute, The aforementioned learning steps are: From the lookup table, determine the storage cell corresponding to the explanatory variable. The new estimated value obtained in the estimation step and the past estimated value recorded in the storage cell are weighted and averaged to update the dead time estimate and the time constant estimate in the lookup table. program.

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