Control device, control method, and program

Abstract

This control device comprises: an extraction unit that extracts, from time-series data obtained by measuring the indoor temperature of a space to be air-conditioned, an element included in a monitoring section from a predetermined start condition to a predetermined end condition of air-conditioning control by an air conditioner; a division unit that uses, as an element of a primary delay response, an element after a point at which the indoor temperature starts falling in the monitoring section during a cooling operation and an element after a point at which the indoor temperature starts rising in the monitoring section during a heating operation, and divides elements other than the elements of the primary delay response in the monitoring section as elements of a dead time; and an estimation unit that estimates the length of the dead time as a dead time estimation value, and estimates a time constant estimation value at which a curve approximation error from the indoor temperature in the primary delay response is minimized.

Description

Control device, control method, and program

[0001] This application claims priority to Japanese Patent Application No. 2023-161587, filed on September 25, 2023, the contents of which are incorporated herein by reference.

[0002] For example, when an air conditioner is operated in cooling mode, it takes time for the room temperature to reach the target temperature set by the user after it is turned on, and the room temperature during that time is uncomfortable for the person in the room (user). For this reason, in order to make the room comfortable by the time the user arrives, people manually turn on the air conditioner (indoor unit) before the time they start to stay in the room, or use a function (warming-up operation) that automatically starts pre-cooling a certain time before (for example, 15 minutes or 30 minutes before).

[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 automatic pre-cooling start time is fixed, as in the warm-up operation described above, there is a possibility that the pre-cooling time may be too long or too short. If the air conditioner's pre-cooling time is too long, it will waste energy and ultimately lead to higher electricity bills. Also, if the pre-cooling time is too short, comfort will be reduced.

[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 first-order lag (see, for example, Patent Documents 1 and 2).

[0005] Japanese Patent Application Laid-Open No. 2001-350503 Japanese Patent Application Laid-Open No. 2002-054837

[0006] Another possibility is to use a model that learns conditions such as indoor and outdoor temperatures to predict thermal response parameters. For example, predicting thermal response parameters using a prediction model such as a neural network or random forest requires sufficient computational resources (memory and CPU). However, most typical 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 calculate thermal response parameters using a lightweight algorithm even when only limited computing resources are installed.

[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 of a space to be air-conditioned, and an estimation unit that estimates a dead time estimate of the response of the indoor temperature to the air conditioning control and a time constant estimate of a first-order lag response based on the extracted elements.

[0009] According to one aspect of the present disclosure, 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 the indoor temperature of a space to be air-conditioned; and estimating, based on the extracted elements, an estimated value of the dead time of the response of the indoor temperature to the air conditioning control and an estimated value of the time constant of the first-order lag response.

[0010] According to one aspect of the present disclosure, 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 the indoor temperature of a space to be air-conditioned; and estimating, based on the extracted elements, an estimated value of the dead time of the response of the indoor temperature to the air conditioning control and an estimated value of the time constant of the first-order lag response.

[0011] According to the above aspect, even if only limited computational resources are installed, it is possible to calculate the thermal response parameters using a lightweight algorithm.

[0012] FIG. 1 is a diagram showing the functional configuration of an air conditioning system according to the first embodiment. FIG. 2 is a diagram showing an example of a prediction model according to the first embodiment. FIG. 3 is a diagram showing an example of explanatory variables and level category assignment according to the first embodiment. FIG. 4 is a flowchart showing an example of a process for estimating a thermal response parameter according to the first embodiment. FIG. 5 is a diagram showing an example of time series data of indoor temperature according to the first embodiment. FIG. 6 is a flowchart showing an example of a process for learning a prediction model according to the first embodiment. FIG. 7 is a block diagram showing an example of a process for updating a lookup table according to the first embodiment. FIG. 8 is a flowchart showing an example of a process for predicting a predicted arrival time according to the first embodiment. FIG. 9 is a block diagram showing an example of a process for predicting a predicted arrival time according to the first embodiment. FIG. 10 is a diagram showing an example of a display of a terminal device according to the first embodiment.

[0013] <First embodiment> A control method according to the first embodiment will be described below with reference to the drawings. The control method according to this embodiment can be applied to the control of various heat source devices such as air conditioners, water heaters, and refrigerators, but the following description will be given taking the case where the control method is applied 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 a 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 each other via a network NW so as to be able to communicate with each other. The terminal device 20 is, for example, a smartphone or tablet equipped with an application that enables remote operation of the air conditioner 1.

[0015] (Functional Configuration of Control Device) The control device 10 is a computer including a CPU (Central Processing Unit) such as a microcomputer and an MPU (Micro Processing Unit). The control device 10 includes an information acquisition unit 11, an extraction unit 12, a division unit 13, 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 the monitoring period from a specified start condition to an end condition of air conditioning control by the air conditioner 1 from the time series data of the indoor temperature in the room (air-conditioned space) where the air conditioner 1 is installed.

[0018] The dividing unit 13 divides the elements in the effective section from the point where the indoor temperature in the effective section starts to decrease during cooling operation, and from the point where the indoor temperature in the effective section starts to increase during heating operation, as first-order lag response elements, and divides the elements in the effective section other than the first-order lag response as dead time elements.

[0019] The estimation unit 14 estimates the length of the dead time as a dead time estimate value L, and estimates a time constant estimate value τ that minimizes the curve approximation error with the room temperature in the first-order lag response section. Parameters including the first-order lag time constant estimate value τ and the dead time estimate value L are also referred to as thermal response parameters.

[0020] 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 estimated dead time value L and the estimated time constant value τ. The learned lookup table is stored in the memory 4.

[0021] The prediction unit 16 predicts the predicted time D from the current time until the air-conditioned space reaches the target temperature based on the trained prediction model (lookup table) read from the memory 4 and the current explanatory variables.

[0022] The control unit 17 controls the indoor unit 5 and the outdoor unit 6. Based on the set temperature and set time settings 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 before the set time or slightly before that time, and performs cooling operation or heating operation so that the set temperature is achieved by the set time.

[0023] 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 the set temperature and set time, and control signals such as powering on the air conditioner 1, from the terminal device 20.

[0024] (Regarding the Prediction Model) FIG. 2 is a diagram illustrating an example of a prediction model according to the first embodiment. FIG. 3 is a diagram illustrating an example of the assignment 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 represented by a lookup table LUT of a time constant estimate value τ and a dead time estimate value L for each operation mode. Furthermore, in the example of FIG. 2 , there are two operation modes: heating operation and cooling operation. Therefore, the lookup table LUT according to this embodiment has four tables: 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. Each table is composed of a combination of two or more explanatory variables. Each explanatory variable has multiple level categories. The level category of the explanatory variable is used as an index to identify a cell in each table.

[0025] For example, as shown in Fig. 3, assume that explanatory variable 1 is air volume and explanatory variable 2 is outdoor temperature. For example, a level category is assigned to the air volume for each air volume setting value of the fan of the indoor unit 5. Furthermore, a level category is assigned to the outdoor temperature for each range of the measurement value of the outdoor temperature sensor 2. Note that, as in the example of Fig. 3, different temperature ranges may be set for the outdoor air temperature level category during cooling operation and heating operation.

[0026] 2 and 3, each table stores in each cell an estimated value of a thermal transient parameter (time constant τ or dead time L) corresponding to a combination of airflow rate and outdoor temperature for each operating mode (heating operation or cooling operation). This allows different thermal response parameters to be updated and referenced depending on the settings of the air conditioner 1, environmental conditions, etc. Each cell also stores 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.

[0027] (Process for Estimating Thermal Response Parameters) FIG. 4 is a flowchart showing an example of a process for estimating thermal response parameters according to the first embodiment. Here, the flow of the process by which the control device 10 estimates thermal response parameters will be described with reference to FIG. 4. When the air conditioner 1 is operating, the control device 10 repeatedly performs the series of processes shown in FIG. 2 at predetermined control intervals (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.

[0028] First, the extraction unit 12 determines whether the start condition of the monitoring period has been met (step S100). The monitoring period is a period for monitoring (extracting and storing) data to estimate thermal response parameters 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 met when the indoor unit 5 is switched on or the set temperature changes.

[0029] If the start condition is not met (step S100; NO), the extraction unit 12 ends the process. On the other hand, if the start condition is met (step S100; YES), the extraction unit 12 starts extracting and storing necessary information (step S101). Thereafter, the extraction unit 12 records the measured indoor temperature values ​​(time-series data) in a buffer secured in the memory 4 at each predetermined control period. In addition, only when the extraction unit 12 starts recording the indoor temperature, it records the operating mode of the air conditioner (heating operation or cooling operation) and explanatory variables used in the learning process described below in the memory 4. The explanatory variables include, for example, the time when the measurement value storage started, the outdoor air temperature, the airflow rate of the indoor unit 5, and the temperature gap between the indoor temperature and the set temperature.

[0030] Next, the extractor 12 determines whether a predetermined termination condition is met (step S102). For example, the extractor 12 determines that the termination condition is met when the end of the buffer (the upper limit of the number or amount of time-series data) is reached, when the indoor unit 5 is switched off, when the set temperature is changed, etc.

[0031] If the termination condition is not met (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 met, 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 met (step S102; YES), the extraction unit 12 further determines whether the monitoring interval is a valid interval (step S103).

[0032] 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 by the time the buffer reaches its end, the extraction unit 12 determines that the monitoring section is invalid (step S103; NO) if the time-series data does not meet a predetermined standard (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 standard, the extraction unit 12 determines that the monitoring section is valid (step S103; YES).

[0033] Next, the dividing unit 13 divides the time series data of the monitoring section into dead time elements and first-order lag elements (step S104).

[0034] FIG. 5 is a diagram illustrating an example of time-series data of indoor temperature according to the first embodiment. FIG. 5 illustrates an example of time-series data of indoor temperature during heating operation. In the example of FIG. 5, the extraction unit 12 extracts and records time-series data of indoor temperature in a monitoring period from time t0 to time t2. The division unit 13 first identifies elements of a first-order lag response from the time-series data. For example, during heating operation, the division unit 13 considers elements after the point at which the indoor temperature begins to increase in the monitoring period to be elements of a first-order lag response. More specifically, the division unit 13 searches for an extreme value (a minimum value during heating operation) in the time-series data and determines that the indoor temperature has begun to increase when the temperature rises from the extreme value by a predetermined temperature (e.g., 1°C) or more. In this case, the division unit 13 considers the extreme value immediately before the temperature rise to be a boundary point and considers elements after the boundary point to be elements of a first-order lag response. In the example of FIG. 5, the indoor temperature rises from the minimum value by a predetermined temperature or more after time t1. Therefore, the dividing unit 13 regards the element at time t1, which is immediately before the indoor temperature starts to rise, as a boundary point, and regards the elements from time t1 to time t2 as elements of a first-order lag response.

[0035] Similarly, during cooling operation, the dividing unit 13 considers elements in the monitoring section after the point at which the indoor temperature starts to decrease as elements of a first-order lag response. More specifically, the dividing unit 13 detects an extreme value (a maximum value during cooling operation) from the time-series data, and determines that the indoor temperature has started to decrease when the temperature drops from the extreme value by a predetermined temperature (for example, 1°C) or more.

[0036] Furthermore, the dividing unit 13 considers elements other than the first-order lag response in the monitoring section to be elements of dead time. In the example of Fig. 5, the dividing unit 13 considers elements before time t1 to be elements of dead time.

[0037] Next, the estimating unit 14 estimates thermal response parameters (step S105). The estimating unit 14 according to this embodiment estimates the length of dead time and the time constant of the first-order lag response as thermal response parameters.

[0038] First, the estimator 14 obtains the length of the dead time (the number of array elements) divided from the time-series data by the divider 13 as the dead time estimated value L. In the example of FIG. 5 , the estimator 14 obtains the time from time t0 to t1 as the dead time estimated value L.

[0039] The estimator 14 also estimates the time constant τ of the first-order lag response based on the elements of the first-order lag response. For example, the estimator 14 performs a simulation (curve approximation) of the change in the indoor temperature over time for each assumed time constant value while sequentially changing the assumed time constant value from a lower limit value to an upper limit value in increments of a fixed time (e.g., one minute). Various known techniques may be used for this simulation. The estimator 14 calculates the error between the approximation curve for each assumed time constant value calculated by the simulation and the actual measured value (the first-order lag response portion of the time series divided by the divider 13), and determines the assumed time constant value that minimizes the error as the estimated time constant τ.

[0040] The estimation unit 14 stores the thermal response parameters, including the dead time estimated value L and the time constant estimated value τ, in the memory 4 in association with the previously recorded operation mode and explanatory variables (step S106). Thereafter, the estimation unit 14 clears the buffer (step S107) and ends the process.

[0041] (Prediction Model Learning Process) Fig. 6 is a flowchart showing an example of a prediction model learning process according to the first embodiment. Fig. 7 is a block diagram showing an example of a lookup table update process according to the first embodiment. Here, the flow of the process by which the control device 10 learns a prediction model will be described with reference to Figs. 6 and 7. Note that the control device 10 executes the series of processes shown in Fig. 6 each time the estimator 14 estimates a thermal response parameter.

[0042] First, the learning unit 15 reads 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).

[0043] 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).

[0044] First, the learning unit 15 selects either the heating operation table or the cooling operation table as the storage destination 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 values ​​of the explanatory variables 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 and 3, explanatory variable 1 corresponds to level 2, and explanatory variable 2 corresponds to level 3. Therefore, in this case, the learning unit 15 selects the cell where the row of level 2 of explanatory variable 1 intersects with the column of level 3 of explanatory variable 2 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 τ(tau_est) and the dead time estimate L(lag_est), respectively.

[0045] 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 values ​​recorded in the storage destination cells of each table as the past average values ​​(tau_avg_old, lag_avg_old).

[0046] Furthermore, the learning unit 15 calculates the average values ​​(tau_avg_new, lag_avg_new) of the new thermal response parameter estimates (tau_est, lag_est) and the past average values ​​(tau_avg_old, lag_avg_old) read from the storage cells. At this time, the learning unit 15 may calculate the new average values ​​by weighting the average with a weight α, as shown in the following equations (1) and (2). The weight α may be set to any value within the range of "0<α≦1", for example.

[0047] tau_avg_new=α*tau_est+(1-α)*tau_avg_old…(1)

[0048] lag_avg_new=α*lag_est+(1-α)*lag_avg_old…(2)

[0049] The learning unit 15 inputs the calculated new average values ​​(tau_avg_new, lag_avg_new) into the storage cell to update the lookup table LUT (step S203). At this time, the learning unit 15 increments the number of samples n in this storage cell by +1.

[0050] If the number of samples n of the storage cell is 0, i.e., if this is the first time that thermal response parameters have been input to this storage cell, the learning unit 15 does not calculate the average value, but instead inputs the thermal response parameter estimates (tau_est, lag_est) read from memory 4 directly into the storage cell, thereby updating the lookup table LUT (step S202).

[0051] (Prediction process of predicted arrival time) Fig. 8 is a flowchart showing an example of the prediction process of predicted arrival time according to the first embodiment. Fig. 9 is a block diagram showing an example of the prediction process of predicted arrival time according to the first embodiment. Here, the flow of the process of predicting the predicted arrival time for the indoor temperature to reach the set temperature will be described with reference to Figs. 8 and 9. Note that the control device 10 executes the series of processes shown in Fig. 8 when, for example, an application of the terminal device 20 is started or operated.

[0052] First, the information acquisition unit 11 acquires explanatory variables to be used for prediction (step S300). For example, if 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 also acquires the current outdoor temperature from the outdoor temperature sensor 2. The information acquisition unit 11 may also simultaneously acquire the current set temperature (or, if the power is OFF, the set temperature at the time of the previous operation).

[0053] 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 (or the previous operation mode, if the power is 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 step S201 in FIG. 6.

[0054] The prediction unit 16 also predicts a predicted time D until the indoor temperature of the air-conditioned space reaches the target temperature based on the read thermal response parameters (step S302). The predicted time D is calculated, for example, using the following equation (3). The target temperature is expressed, for example, as x% (any value, such as 90%, 95%, etc.) of the current set temperature. The coefficient C of the time constant τ (tau_avg) can be changed depending on the target temperature. For example, if the target temperature is 95% of the set temperature, C=3, and if the target temperature is 90% of the set temperature, C=2.3.

[0055] D=C×tau_avg+lag_avg…(3)

[0056] Next, the communication unit 18 transmits the predicted arrival time D predicted by the prediction unit 16 to the terminal device 20 (step S303).

[0057] 10 is a diagram showing an example of a display of a terminal device according to the first embodiment. For example, as shown in FIG. 10, the application screen of the terminal device 20 displays information such as current information C201 and predicted information C202, as well as 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 a 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 "Now ON / OFF button," a "Timer setting ON / OFF button," a "Timer date and time setting button," and the like.

[0058] 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 power on the air conditioner 1. The control unit 17 of the control device 10 then receives an operation instruction (power ON) from the terminal device 20 via the communication unit 18 (step S304) and controls the air conditioner 1 (indoor unit 5) to power on (step S305).

[0059] Furthermore, 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. The user may also operate the timer date and time setting to set the indoor temperature to the set temperature at any set time. In this case, the control unit 17 of the control device 10 receives an operation instruction (timer setting ON and set time) from the terminal device 20 via the communication unit 18 (step S304). The control unit 17 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 reaches the set temperature at the set time set by the user, and performs cooling or heating operation to achieve the set temperature by the set time (step S305).

[0060] The user may also change the current set temperature. For example, the user may be able to change the set temperature by tapping the set temperature portion of the current information C201. In response, the control device 10 receives an operation instruction (an instruction to change the set temperature) from the terminal device 20 via the communication unit 18. In this case, the control device 10 may return to step S302 and re-predict the predicted arrival time.

[0061] (Actions and Effects) 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 of the air-conditioned space, and an estimation unit 14 that estimates, based on the extracted elements, an estimated value L of the dead time of the indoor temperature response to the air conditioning control of the air conditioner 1 and an estimated value τ of the time constant of the first-order lag response.

[0062] The control device 10 can reduce the calculation load by estimating only two scalar quantities, the estimated dead time value L and the first-order lag time constant τ, as thermal response parameters from a limited number of elements included in the monitoring section. Therefore, even the control device 10 of the air conditioner 1 with limited calculation resources can estimate the thermal response parameters.

[0063] The control device 10 also includes a dividing unit 13 that divides the elements in the monitoring section from the point where the indoor temperature starts to decrease during cooling operation, and from the point where the indoor temperature starts to increase during heating operation, into first-order lag response elements, and divides the elements in the monitoring section other than the first-order lag response into dead time elements, and the estimating unit 14 estimates the length of the dead time element as a dead time estimated value L, and estimates the time constant that minimizes the curve approximation error with the indoor temperature in the first-order lag response as a time constant estimated value τ.

[0064] In this way, the control device 10 can estimate the time constant of the first-order lag response by omitting time-series data corresponding to the dead time, thereby reducing the calculation load. As a result, even if only limited calculation resources are installed, it is possible to calculate the thermal response parameters (dead time and the time constant of the first-order lag response) using a lightweight algorithm.

[0065] The estimation unit 14 also stores the estimated dead time value L and the estimated time constant value τ, and deletes the extracted elements (clears the buffer).

[0066] For example, when building a model for predicting thermal response parameters using machine learning, it is necessary to collect and learn a large number of samples, with each sample representing an element in a certain monitoring interval. For example, if learning is performed using 100 samples with a length of N = 120 points (time series data recording temperature changes every minute for 120 minutes), a memory for storing 120 points x 100 pieces of data is required. However, as described above, typical air conditioner control devices have limited computational resources, such as memory, making it difficult to store such a large amount of data. In contrast, in this embodiment, the estimator 14 estimates and stores the dead time estimate L and the time constant estimate τ from the elements in the monitoring interval, and then deletes the elements used for the estimation. In this way, even if the control device 10 needs to accumulate a large number (100) of samples as past data, each sample only needs to include two numerical values, the dead time estimate L and the time constant estimate τ, allowing for compressed storage of past data (2 / N compression compared to the case of using machine learning). Furthermore, it is sufficient to temporarily store one sample of the elements used to estimate the thermal response parameters, and these elements may be deleted after each estimation, and new elements may be stored in the same buffer for the next estimation. Therefore, the control device 10 can reduce the overall buffer and memory capacity used.

[0067] In addition, the control device 10 further includes a prediction unit 16 that predicts the estimated time it will take for the air-conditioned space to reach the target temperature from the current time based on a lookup table LUT that associates explanatory variables, which are conditions that affect the air conditioning of the air-conditioned space, with an estimated dead time value L and an estimated time constant value τ, and the current explanatory variables.

[0068] By doing this, the control device 10 can use the thermal response parameters calculated during operation of the air conditioner 1 to accurately predict the predicted time D until the air-conditioned space reaches the target temperature.

[0069] The control device 10 also includes a communication unit 18 that transmits the predicted arrival time D to a terminal device 20 carried by the user and receives user operation instructions from the terminal device 20, and a control unit 17 that controls the air conditioner 1 based on the operation instructions.

[0070] By doing this, the control device 10 can accept operation instructions from a user who has referenced the predicted arrival time D, and perform cooling or heating operation so that the indoor temperature approaches the set temperature before the user enters the air-conditioned space.

[0071] 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 modifications are possible. That is, in other embodiments, the order of the above-described processes may be changed as appropriate. Furthermore, some processes may be executed in parallel.

[0072] For example, in the above-described 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). However, this is not limiting. 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 for aggregation, and the server may learn a prediction model. In this case, the server performs statistical processing or machine learning on the multiple thermal response parameters to generate and update the prediction model. The server also periodically predicts the 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 lookup table LUT in the memory 4 with the thermal response parameters received from the server. In yet another embodiment, an application on the terminal device 20 may acquire the thermal response parameters from the control device 10 and learn the 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. This makes it possible to reduce the processing load of the learning unit 15.

[0073] <Additional Notes> The control device, control method, and program described in the above-described embodiment can be understood, for example, as follows.

[0074] (1) According to the first aspect, the control device 10 includes an extraction unit 12 that 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 obtained by measuring the indoor temperature of the air-conditioned space, and an estimation unit 14 that estimates, based on the extracted elements, an estimated value L of the dead time of the indoor temperature response to the air conditioning control and an estimated value τ of the time constant of the first-order lag response.

[0075] The control device 10 can reduce the calculation load by estimating only two scalar quantities, the estimated dead time value L and the first-order lag time constant τ, as thermal response parameters from a limited number of elements included in the monitoring section. Therefore, even the control device 10 of the air conditioner 1 with limited calculation resources can estimate the thermal response parameters.

[0076] (2) According to the second aspect, the control device 10 according to the first aspect further includes a dividing unit 13 that divides the elements of the monitoring section from the point at which the indoor temperature starts to decrease during cooling operation and from the point at which the indoor temperature starts to increase during heating operation into first-order lag response elements, and divides the elements of the monitoring section other than the first-order lag response into dead time elements, and the estimating unit 14 estimates the length of the dead time element as the dead time estimated value L, and estimates the time constant that minimizes the curve approximation error with the indoor temperature in the first-order lag response as the time constant estimated value τ.

[0077] In this way, the control device 10 can estimate the time constant of the first-order lag response by omitting time-series data corresponding to the dead time, thereby reducing the calculation load. As a result, even if only limited calculation resources are installed, it is possible to calculate the thermal response parameters (dead time and the time constant of the first-order lag response) using a lightweight algorithm.

[0078] (3) According to the third aspect, in the control device 10 according to the first or second aspect, the estimation unit 14 stores the estimated dead time estimate value L and the estimated time constant estimate value τ, and deletes the extracted elements.

[0079] For example, when building a model for predicting thermal response parameters using machine learning, it is necessary to collect and learn a large number of samples, with each sample representing an element in a certain monitoring interval. For example, if learning is performed using 100 samples with a length of N = 120 points (time series data recording temperature changes every minute for 120 minutes), a memory for storing 120 points x 100 pieces of data is required. However, as described above, typical air conditioner control devices have limited computational resources, such as memory, making it difficult to store such a large amount of data. In contrast, in this embodiment, the estimator 14 estimates and stores the dead time estimate L and the time constant estimate τ from the elements in the monitoring interval, and then deletes the elements used for the estimation. In this way, even if the control device 10 needs to accumulate a large number (100) of samples as past data, each sample only needs to include two numerical values, the dead time estimate L and the time constant estimate τ, allowing for compressed storage of past data (2 / N compression compared to the case of using machine learning). Furthermore, it is sufficient to temporarily store one sample of the elements used to estimate the thermal response parameters, and these elements may be deleted after each estimation, and new elements may be stored in the same buffer for the next estimation. Therefore, the control device 10 can reduce the overall buffer and memory capacity used.

[0080] (4) According to a fourth aspect, the control device 10 according to any one of the first to third aspects further includes a learning unit 15 that learns a lookup table LUT that associates explanatory variables, which are conditions that affect the air conditioning of the air-conditioned space, with an estimated dead time value L and an estimated time constant value τ, and a prediction unit 16 that predicts a predicted time D from the current time until the air-conditioned space reaches the target temperature based on the lookup table LUT and the current explanatory variables.

[0081] In this way, the control device 10 can accurately predict the predicted time D until the air-conditioned space reaches the target temperature by using the thermal response parameters calculated during operation of the air conditioner 1. Furthermore, unlike machine learning models such as neural networks and random forests, which have a heavy computational load, the control device 10 uses a lightweight lookup table that associates explanatory variables with the estimated dead time L and the estimated time constant τ as the prediction model, making it possible to sufficiently learn the prediction model and perform predictions using the prediction model even with limited computational resources.

[0082] (5) According to the fifth aspect, the control device 10 according to the fourth aspect further includes a communication unit 18 that transmits the predicted arrival time D to a terminal device 20 carried by the user and receives operation instructions from the user from the terminal device 20, and a control unit 17 that controls the air conditioner 1 based on the operation instructions.

[0083] By doing this, the control device 10 can accept operation instructions from a user who has referenced the predicted arrival time D, and perform cooling or heating operation so that the indoor temperature approaches the set temperature before the user enters the air-conditioned space.

[0084] (6) According to the sixth 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 the air conditioner 1 from time series data obtained by measuring the indoor temperature of the air-conditioned space; and estimating, based on the extracted elements, an estimated value L of the dead time of the response of the indoor temperature to the air conditioning control and an estimated value τ of the time constant of the first-order lag response.

[0085] (7) According to the seventh aspect, the program causes the control device 10 to execute the following steps: extracting 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 of the air-conditioned space; and estimating, based on the extracted elements, an estimated value L of the dead time of the indoor temperature response to the air conditioning control and an estimated value τ of the time constant of the first-order lag response.

[0086] According to the above aspect, even if only limited computational resources are installed, it is possible to calculate the thermal response parameters using a lightweight algorithm.

[0087] REFERENCE SIGNS LIST 1 Air conditioner 2 Outdoor temperature sensor 3 Indoor temperature sensor 4 Memory 5 Indoor unit 6 Outdoor unit 10 Control device 11 Information acquisition unit 12 Extraction unit 13 Division unit 14 Estimation unit 15 Learning unit 16 Prediction unit 17 Control unit 18 Communication unit 20 Terminal device 100 Air conditioning system LUT Look-up table

Claims

1. A control device comprising: an extraction unit that extracts elements included in a monitoring period from a specified start condition to an end condition of air conditioning control by an air conditioner from time series data measuring the indoor temperature in a space to be air conditioned; and an estimation unit that estimates a dead time estimate of the response of the indoor temperature to the air conditioning control and an estimated time constant of a first-order lag response based on the extracted elements.

2. The control device as described in claim 1, further comprising a division unit which divides the elements of the monitoring section from the point where the indoor temperature starts to decrease during cooling operation, and from the point where the indoor temperature starts to increase during heating operation, into first-order lag response elements, and divides the elements of the monitoring section other than the first-order lag response into dead time elements, wherein the estimation unit estimates the length of the dead time elements as a dead time estimated value, and estimates a time constant that minimizes a curve approximation error with the indoor temperature in the first-order lag response as the time constant estimated value.

3. The control device according to claim 1, wherein the estimating section stores the estimated dead time value and the estimated time constant value, and deletes the extracted element.

4. A control device as described in any one of claims 1 to 3, further comprising: 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 value and the time constant estimate value; and a prediction unit that predicts a predicted time from the current time until the air conditioned space reaches a target temperature based on the lookup table and the current explanatory variables.

5. The control device according to claim 4, further comprising: a communication unit that transmits the predicted arrival time to a terminal device carried by a user and receives operation instructions from the user from the terminal device; and a control unit that controls the air conditioner based on the operation instructions.

6. A control method comprising 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 a space to be air conditioned; and estimating a dead time estimate of the response of the indoor temperature to the air conditioning control and an estimate of the time constant of a first-order lag response based on the extracted elements.

7. A program that causes a control device to execute the steps of: extracting elements included in a monitoring period from a specified start condition to an end condition of air conditioning control by an air conditioner from time series data measuring the indoor temperature in a space to be air conditioned; and estimating a dead time estimate of the response of the indoor temperature to the air conditioning control and an estimate of the time constant of the first-order lag response based on the extracted elements.

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