Controller of air conditioning facility, method for controlling air conditioning facility, and program
Patent Information
- Application Number
- JP2023133892
- Authority / Receiving Office
- JP · JP
- Patent Type
- Applications
- Current Assignee / Owner
- Filing Date
- 2023-08-21
- Publication Date
- 2025-03-06
Smart Images
Figure 2025029337000001_ABST
Abstract
Description
[Technical field]
[0001] The present disclosure relates to a control device for an air conditioning system, a control method for an air conditioning system, and a program. [Background technology]
[0002] In facilities (e.g., server rooms) where many heat generating devices such as IT devices (e.g., servers and communication devices) are installed, air conditioning equipment is provided to cool the heat generating devices (see Patent Documents 1 and 2). In such air conditioning equipment, the settings (e.g., temperature) are generally changed according to the season, taking into account power efficiency and other factors. [Prior art documents] [Patent documents]
[0003] [Patent Document 1] Patent No. 5801732 [Patent Document 2] JP 2023-26104 A Summary of the Invention [Problem to be solved by the invention]
[0004] In conventional air conditioning equipment control methods, the set conditions are constant during a given season. However, the heat load of the heating element (such as the rise in room temperature due to heat generated by the heating element) changes not only with the season, but also with the time of day. Therefore, in reality, the original ideal set conditions change from moment to moment, even during the course of a day.
[0005] An object of the present disclosure is to provide a control device for air conditioning equipment, a control method for air conditioning equipment, and a program that can operate the air conditioning equipment under optimal setting conditions in accordance with the heat load that changes from moment to moment. [Means for solving the problem]
[0006] According to one aspect of the present disclosure, an air conditioning equipment control device is a control device for an air conditioning equipment including an air conditioner installed in a facility in which multiple heat generating elements are arranged, and includes an acquisition unit that acquires measurement values including a temperature measured at a predetermined position in the facility and setting conditions of the air conditioning equipment including a set temperature of the air conditioner, a prediction unit that predicts future heat loads corresponding to each of multiple setting condition candidates using a model in which the measurement values and the setting conditions are explanatory variables and the heat load due to the heat generated by the heating element is a target variable, and a control unit that selects, from the setting condition candidates, a setting condition candidate that has obtained a predicted value that satisfies an environmental condition that specifies a reference value or reference range for the heat load, and controls the air conditioning equipment based on the selected candidate setting conditions.
[0007] According to one aspect of the present disclosure, a method for controlling air conditioning equipment is a method for controlling air conditioning equipment including an air conditioner installed in a facility in which a plurality of heating elements are arranged, and includes the steps of: acquiring measurement values including a temperature measured at a predetermined position in the facility and setting conditions of the air conditioning equipment including a set temperature of the air conditioner; predicting future heat loads corresponding to each of a plurality of candidate setting conditions using a model in which the measurement values and the setting conditions are explanatory variables and the heat load due to the heat generated by the heating elements is a target variable; and selecting, from the candidate setting conditions, a candidate setting condition for which a predicted value that satisfies an environmental condition that specifies a reference value or reference range for the heat load is obtained, and controlling the air conditioning equipment based on the selected candidate setting conditions.
[0008] According to one aspect of the present disclosure, the program causes a control device of an air conditioning equipment including an air conditioner installed in a facility where multiple heating elements are arranged to execute the steps of acquiring measurement values including a temperature measured at a predetermined position in the facility and setting conditions of the air conditioning equipment including a set temperature of the air conditioner, predicting future heat loads corresponding to each of multiple setting condition candidates using a model in which the measurement values and the setting conditions are explanatory variables and the heat load due to the heat generated by the heating elements is a target variable, and selecting, from the setting condition candidates, a setting condition candidate that has obtained a predicted value that satisfies an environmental condition that specifies a reference value or reference range for the heat load, and controlling the air conditioning equipment based on the selected candidate setting conditions. Effect of the Invention
[0009] According to the above aspect, the air conditioning equipment can be operated under optimal setting conditions in response to the heat load that changes from moment to moment. [Brief description of the drawings]
[0010] [Figure 1] 1 is a diagram showing an overall configuration of an air conditioning system according to a first embodiment. [Diagram 2] 2 is a block diagram showing a functional configuration of a control device according to the first embodiment. FIG. [Diagram 3] 5 is a flowchart showing an example of processing by the control device according to the first embodiment. [Figure 4] FIG. 2 is a diagram illustrating an example of a model according to the first embodiment. [Diagram 5] FIG. 2 is a diagram for explaining functions of a control device according to the first embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0011] <First embodiment> Hereinafter, the embodiments will be described in detail with reference to the drawings.
[0012] (Overall composition) FIG. 1 is a diagram showing the overall configuration of an air conditioning system according to the first embodiment. As shown in FIG. 1, the air conditioning system 1 includes a plurality of heating elements 2, an air conditioning facility 3, and a control device 10.
[0013] The heating element 2 is, for example, IT equipment such as a server or a communication device arranged in a facility R. The facility R is, for example, a server room. The heating element 2 has a fan 20 that takes in cold air from the outside and expels air heated by the heat generated by the heating element 2. As shown in FIG. 1, the heating elements 2 are arranged in a row in the left-right direction of the paper and in a row in the up-down direction of the paper, sandwiching an aisle between them. At this time, the heating elements 2 are arranged so that the intake sides of the fans 20 of each heating element 2 face each other and the exhaust sides face each other in the up-down direction of the paper. The aisle in which the intake sides of the fans 20 are arranged is also referred to as a cold aisle CA, and the aisle in which the exhaust sides enter is also referred to as a hot aisle HA.
[0014] The heat generating element 2 is provided with an intake temperature sensor 21 that measures the intake temperature of the heat generating element 2 (fan 20) and an exhaust temperature sensor 22 that measures the exhaust temperature. In other embodiments, instead of or in addition to the intake temperature sensor 21 and the exhaust temperature sensor 22, a temperature sensor that measures the temperature of the CPU, GPU, or the like of the heat generating element 20 (hereinafter collectively referred to as CPU temperature) may be provided.
[0015] The air conditioning equipment 3 has at least one air conditioner 30. The air conditioner 30 draws in air within the facility R, cools it using a heat exchanger (not shown), and discharges it into the facility R. The number of air conditioners 30 may be changed as desired depending on the size of the facility R, the number of heating elements 2, etc.
[0016] The air conditioner 30 is also provided with an intake temperature sensor 31 that measures the temperature of the intake air, and an exhaust temperature sensor 32 that measures the temperature of the exhaust air (cooled air).
[0017] The air conditioning equipment 3 may further include at least one humidifier 35. The humidifier 3 adjusts the humidity within the facility R. The number of humidifiers 35 may be changed as desired depending on the size of the facility R, etc.
[0018] The fan 20 of the heating element 2 may also be used as one of the air conditioning units 3.
[0019] In addition, the facility R is provided with a room temperature sensor 51 for measuring the room temperature of the facility R, and a humidity sensor 52 for measuring the humidity of the facility R.
[0020] The control device 10 controls the air conditioning equipment 3 so that the environment in the facility R satisfies predefined environmental conditions. The environmental conditions are, for example, target values for the room temperature of the facility R, the humidity of the facility R, etc., which will be described in detail later.
[0021] (Functional configuration of the control device) FIG. 2 is a block diagram showing a functional configuration of the control device according to the first embodiment. As shown in FIG. 2, the control device 10 includes a processor 11, a memory 12, a storage 13, and a communication interface 14.
[0022] The processor 11 operates according to a predetermined program to cause the control device 10 to perform various functions. The functions of the processor 11 will be described later.
[0023] The memory 12 has a memory area necessary for the operation of the processor 11 .
[0024] The storage 13 is a so-called auxiliary storage device, such as a hard disk drive (HDD), a solid state drive (SSD), etc. The storage 13 stores data that each part of the processor 11 acquires, generates, and refers to during processing.
[0025] The communication interface 14 is an interface for transmitting and receiving various data (measurement values), control signals, and the like between each sensor and the air conditioning equipment 3 (air conditioner 30, humidifier 35).
[0026] Next, a description will be given of the functions of the processor 11. The processor 11 performs the functions of an acquisition unit 111, a prediction unit 112, a control unit 113, and a learning unit 114 by operating according to a program prepared in advance.
[0027] The acquisition unit 111 acquires the measurement values of each sensor.
[0028] The measurement value of the sensor is, for example, a temperature measured at a predetermined position in the facility R. The temperature measured at a predetermined position in the facility R is at least one of the room temperature of the facility R measured by the temperature sensor 51, the intake temperature of the air conditioner 30 measured by the intake temperature sensor 31, the exhaust temperature of the air conditioner 30 measured by the exhaust temperature sensor 32, the intake temperature of the heating element 2 (fan 20) measured by the intake temperature sensor 21, the exhaust temperature of the heating element 2 (fan 20) measured by the exhaust temperature sensor 22, and the temperature of the heating element (CPU temperature). In addition, if the air conditioning equipment 3 has a humidifier 35, the measurement value of the sensor may further include the humidity of the facility R measured by the humidity sensor 51.
[0029] The prediction unit 112 predicts the future heat load corresponding to each of the multiple setting condition candidates using a model M that uses the measured values and the setting conditions of the air conditioning equipment 3 as explanatory variables and the heat load due to the heat generation of the heating element 2 as a response variable. The model M is a model that has been trained in advance by the training unit 114 described below.
[0030] The setting conditions of the air conditioning equipment 3 include, for example, the set temperature of the air conditioner 30. The setting conditions of the air conditioning equipment 3 may also include the set air volume of the air conditioner 30. If the air conditioning equipment 3 has multiple air conditioners 30, the setting conditions may also include the number of air conditioners 30 in operation. In this case, the setting conditions include, in addition to the number of air conditioners in operation, the ON (operating) / OFF (not operating) setting of each air conditioner 30, and the set temperature and set air volume of each air conditioner 30 in operation.
[0031] Furthermore, when the air conditioning equipment 3 has a humidifier 35, the setting conditions may include the number of humidifiers in operation. In this case, the setting conditions include the ON (operation) / OFF (non-operation) setting of each humidifier 35 in addition to the number of humidifiers in operation.
[0032] Furthermore, the set conditions may include the air volume or rotation speed of the fan 20 of each heating element 2 .
[0033] The thermal load includes at least one of the power usage effectiveness (PUE) of the facility R affected by the heat generation of the heating element 2, the room temperature of the facility R, the intake temperature of the air conditioner 30, the exhaust temperature of the air conditioner 30, the intake temperature of the heating element 2 (fan 20), the exhaust temperature of the heating element 2 (fan 20), and the temperature (CPU temperature) of the heating element 2. Furthermore, if the air conditioning equipment 3 has a humidifier 35, the thermal load may further include the humidity of the facility R.
[0034] The control unit 113 selects, from among the setting condition candidates, a setting condition candidate that has obtained a predicted value that satisfies the environmental condition that specifies the reference value of the heat load, and controls the air conditioning equipment 3 based on the selected setting candidate condition.
[0035] The environmental conditions are the reference values (upper or lower limit values) or reference ranges (range from the lower limit value to the upper limit value) of the heat loads described above. The reference values or reference ranges of each heat load are set arbitrarily depending on the number, arrangement, specifications, etc. of the heating elements 2 and air conditioning equipment 3.
[0036] The learning unit 114 uses time-series data of the measurement values of each sensor and the heat load calculated from the measurement values and the set conditions of the air conditioning equipment 3 as learning data, and machine-learns the model M. In the learning phase, the learning unit 114 learns the model M using a predetermined number of time-series data in advance and records it in the storage 13.
[0037] Furthermore, after the learning phase is completed and the operation phase is started, the learning unit 114 re-learns and updates the model M. Specifically, the learning unit 114 calculates the actual value of the thermal load at a certain evaluation time from the measurement values and the set conditions acquired at the evaluation time, and learns and updates the model M based on the error between the actual value and the predicted value of the thermal load at the evaluation time predicted in the past. The evaluation time is, for example, the current time, but may also be a time in the past (such as n minutes before the current time).
[0038] The predetermined program executed by the processor 11 is stored in a computer-readable recording medium. The computer-readable recording medium refers to a magnetic disk, a magneto-optical disk, a CD-ROM, a DVD-ROM, a semiconductor memory, etc. The computer program may be distributed to a computer via a communication line, and the computer that receives the program may execute the program. The program may be for realizing part of the above-mentioned functions. The program may be a so-called difference file (difference program) that can realize the above-mentioned functions in combination with a program already recorded in the computer system.
[0039] (Control device processing flow) FIG. 3 is a flowchart illustrating an example of processing by the control device according to the first embodiment. Hereinafter, the details of the processing of the control device 10 will be described with reference to Fig. 3. It is assumed that the learning phase has been completed at the time of Fig. 3. Therefore, Fig. 3 shows the processing of the control device 10 in the operation phase.
[0040] The acquisition unit 111 acquires the measurement value at the current time from each sensor (step S01).
[0041] Next, the control device 10 controls the air conditioning equipment 3 (step S02) and updates the model M (step S03). Note that, although an example in which steps S02 and S03 are executed in parallel is shown in Fig. 3, this is not limiting. In other embodiments, step S03 may be executed after step S02, or step S02 may be executed after step S03.
[0042] First, a description will be given of the control process S02 of the air conditioning equipment 3. The prediction unit 112 reads out the model M recorded in the storage 13, inputs the measurement value at the current time acquired in step S01 and candidate setting conditions for the air conditioning equipment 3 to be applied after the current time to the model M, and obtains a predicted value of the future heat load as an output (step S21).
[0043] FIG. 4 is a diagram illustrating an example of a model according to the first embodiment. As shown in FIG. 4, the model M according to this embodiment is a neural network that inputs explanatory variables and outputs objective variables. Here, for simplicity of explanation, the explanatory variables input to the model M are the setting conditions consisting of three combinations of the number of air conditioners in operation, the set temperature of each air conditioner 30 in operation, and the number of humidifiers in operation, and two measured values of the room temperature and humidity of the facility R. In addition, the objective variables output from the model M are the power usage effectiveness (PUE) representing the heat load in the future (for example, 30 minutes later), the room temperature of the facility R, and the predicted value of the humidity of the facility R. The PUE is an index obtained by dividing the power consumption in the entire facility R by the power consumption of the heating element 2. The smaller the PUE value, the higher the efficiency. The storage 13 of the control device 10 records the actual set values of the number of air conditioners in operation, the set temperature of the air conditioners, and the number of humidifiers in operation at each time, as well as the actual measured values of the room temperature, humidity, and PUE. In addition, the learned model M recorded in the storage 13 is machine-learned using these past time-series data as learning data in the learning phase.
[0044] In the operation phase shown in FIG. 3, in step S21, the room temperature and humidity at the current time (measured values acquired in step S01) and setting condition candidates to be applied from the current time onwards (combination of the number of operating air conditioners, the set temperature of the air conditioners, and the number of operating humidifiers) are input to model M as explanatory variables. The objective variables output from model M are the predicted values of future PUE, room temperature, and humidity when the setting condition candidates are applied. In other words, the prediction unit 112 can predict how the future heat load will change when the setting conditions are changed. In this embodiment, the prediction unit 112 inputs multiple setting condition candidates to model M and obtains a predicted value of the future heat load corresponding to each setting condition candidate as an output.
[0045] Next, the control unit 113 selects optimal setting conditions that satisfy the environmental conditions of the facility R, based on the setting condition candidates and the predicted value of the future heat load (step S22).
[0046] The environmental conditions of facility R are, for example, a reference value (upper limit or lower limit) or a reference range (range from upper limit to lower limit) of the thermal load. In the above example, it is assumed that the reference value of room temperature (upper limit X degrees or less) and the reference value of humidity (lower limit Y%) among the thermal loads are set as the environmental conditions to be satisfied. The control unit 113 selects the setting condition candidate that satisfies the environmental conditions for both room temperature and humidity and has the smallest PUE as the optimal setting condition (step S22).
[0047] FIG. 5 is a diagram for explaining functions of the control device according to the first embodiment. Furthermore, the control unit 113 may generate a response surface indicating the sensitivity of the setting conditions to the heat load in the current state (room temperature, humidity) as shown in Fig. 5, and identify the optimal setting conditions using the response surface. Fig. 5 shows (a) the predicted PUE value, (b) the predicted room temperature value, and (c) the predicted humidity value corresponding to each combination of setting conditions. Note that Fig. 5 illustrates a response surface indicating the correspondence between the combination of the set temperatures of the two air conditioners 30 (air conditioners 1 and 2) among the setting conditions and the heat load, but similar response surfaces are generated for other combinations of setting conditions.
[0048] For some heat loads, a reference value (upper limit or lower limit) or a reference range (range from upper limit to lower limit) is predefined as an environmental condition of the facility R. For example, a reference value C1 of room temperature (for example, X degrees or less) and a reference value C2 of humidity (for example, Y% or more) are predefined as environmental conditions. The control unit 113 narrows down the setting condition candidates that satisfy both the room temperature and humidity based on the response surfaces of room temperature and humidity shown in (b) to (c) of FIG. 5 and the environmental conditions (the reference value C1 of room temperature and the reference value C2 of humidity). The control unit 113 also refers to the response surface of PUE shown in (a) of FIG. 5 and selects the setting condition candidate with the smallest PUE as the optimal setting condition (step S22). The control unit 113 records the specified setting condition and the predicted value of the corresponding heat load in the storage 13.
[0049] Next, the control unit 113 outputs a control signal to each of the air conditioning equipment 3 (air conditioner 30, humidifier 35) based on the selected setting conditions (step S23).
[0050] By repeatedly executing the above process, the control device 10 can predict the heat load that changes from moment to moment and operate the air conditioning equipment 3 under optimal setting conditions to minimize power (electricity consumption).
[0051] Returning to Fig. 3, the update process S03 of the model M in the learning unit 114 will be described. First, the learning unit 114 identifies the thermal load at the current time (time t) based on the measurement values acquired in step S01 (step S31). Here, similar to step S02, the thermal load is assumed to be the room temperature, humidity, and PUE of the facility R. The measurement values acquired in step S01 may be used as is for the room temperature and humidity. The PUE is calculated by measuring the power consumption of the heating element 2 and the power consumption of the entire facility R, for example, with a power meter (not shown).
[0052] Next, the learning unit 114 learns the error between the "predicted value of the thermal load at time t" predicted in the past and the "actual measured value of the thermal load at time t" and updates the model M (step S32). For example, it is assumed that the control unit 113 predicts the thermal load 30 minutes from now. In this case, the learning unit 114 reads out the predicted value of the thermal load predicted by the control unit 113 30 minutes before time t (the current time) from the storage 13 and finds the error from the actual measured value of the thermal load identified in step S31. Then, the learning unit 114 updates the model M by learning the weights so as to minimize an evaluation function consisting of this error, for example.
[0053] The learning unit 114 re-learns and updates the model M from time to time in this way, thereby making it possible to improve the accuracy of prediction of the thermal load by the model M.
[0054] (Action, effect) As described above, the control device 10 of the air conditioning equipment 3 in this embodiment includes an acquisition unit 111 that acquires measurement values measured by each sensor and the setting conditions of the air conditioning equipment 3, a prediction unit 112 that predicts the future heat load corresponding to each of multiple setting condition candidates using a model M that has the measurement values and the setting conditions as explanatory variables and the heat load due to heat generation by the heating element 2 as a target variable, and a control unit 113 that selects, from the setting condition candidates, a setting condition candidate that has obtained a predicted value that satisfies the environmental conditions, and controls the air conditioning equipment 3 based on the selected candidate setting conditions.
[0055] In this way, the control device 10 can predict the heat load that changes from moment to moment, and operate the air conditioning equipment 3 under optimal setting conditions to minimize power (electricity consumption).
[0056] In addition, the control device 10 further includes a learning unit 114 that identifies an actual value of the thermal load at the evaluation time from the measurement values and set conditions acquired at the evaluation time, and learns and updates the model M based on the error between the identified actual value of the thermal load and a predicted value of the thermal load at the evaluation time predicted in the past.
[0057] In this way, the control device 10 can improve the prediction accuracy of the heat load by the model M the more the heating element 2 and the air conditioning equipment 3 are operated.
[0058] The measured values are at least one of the room temperature of the facility R, the intake temperature of the air conditioner 30, the exhaust temperature of the air conditioner 30, the intake temperature of the heating element 2, the exhaust temperature of the heating element 2, and the temperature of the heating element 2 (CPU temperature).
[0059] In this way, the control device 10 can predict the heat load and select the optimal setting conditions by using various temperatures measured in the facility R. In other words, the control device 10 can use the temperature sensors already installed in the facility R, the heating element 2, and the air conditioning equipment 3, thereby reducing the cost of introducing new sensors.
[0060] The set conditions further include at least one of the number of operating air conditioners 30, the air volume of the air conditioners 30, and the air volume or rotation speed of the fan 20 of the heating element 2.
[0061] In this way, the control device 10 can control the air conditioning equipment 3 by combining various setting conditions according to the number and functions of the air conditioners 30, the specifications of the fans 20 of the heating elements 2, etc. Also, by combining multiple setting conditions, the control device 10 can finely control the air conditioning equipment 3 so that the future heat load is in a more appropriate state.
[0062] The thermal load also includes at least one of the room temperature of facility R, the intake temperature of air conditioner 30, the exhaust temperature of air conditioner 30, the intake temperature of heating element 2, the exhaust temperature of heating element 2, and the temperature of heating element 2 (CPU temperature), as well as the PUE of facility R, and control unit 113 selects a setting condition candidate in which the predicted values of the thermal load other than PUE satisfy the environmental conditions and the predicted value of PUE is minimized.
[0063] In this way, the control device 10 can control the air conditioning equipment 3 so that the future heat load is in a more appropriate state and the PUE is minimized.
[0064] In addition, the air conditioning equipment 3 further includes a plurality of humidifiers 35, the measurement values and the heat load further include the humidity of the facility R, and the set conditions further include the number of humidifiers 35 in operation.
[0065] In this way, the control device 10 can control the air conditioning equipment 3 so that not only the temperature but also the humidity within the facility R is kept at an appropriate state.
[0066] <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.
[0067] <Additional Notes> The air conditioning equipment control device, the air conditioning equipment control method, and the program described in the above-described embodiments can be understood, for example, as follows.
[0068] (1) According to the first aspect, a control device 10 of an air conditioning equipment 3 including an air conditioner 30 provided in a facility R in which a plurality of heating elements 2 are arranged includes an acquisition unit 111 that acquires measurement values including a temperature measured at a predetermined position in the facility R and setting conditions of the air conditioning equipment 3 including a set temperature of the air conditioner 30, a prediction unit 112 that predicts a future heat load corresponding to each of a plurality of setting condition candidates using a model M in which the measurement values and the setting conditions are explanatory variables and the heat load due to heat generation by the heating element 2 is a target variable, and a control unit 113 that selects, from the setting condition candidates, a setting condition candidate that has obtained a predicted value that satisfies an environmental condition that specifies a reference value or reference range of the heat load, and controls the air conditioning equipment 3 based on the selected setting candidate conditions.
[0069] In this way, the control device 10 can predict the heat load that changes from moment to moment, and operate the air conditioning equipment 3 under optimal setting conditions to minimize power (electricity consumption).
[0070] (2) According to the second aspect, the control device 10 of the air conditioning equipment 3 relating to the first aspect further includes a learning unit 114 that identifies an actual value of the thermal load at the evaluation time from the measurement values and the set conditions acquired at the evaluation time, and learns and updates a model based on the error between the identified actual value of the thermal load and a predicted value of the thermal load at the evaluation time predicted in the past.
[0071] In this way, the control device 10 can improve the prediction accuracy of the heat load by the model M the more the heating element 2 and the air conditioning equipment 3 are operated.
[0072] (3) According to the third aspect, in the control device 10 of the air conditioning equipment 3 relating to the first or second aspect, the temperature measured at a predetermined position in the facility R is at least one of the room temperature of the facility R, the suction temperature of the air conditioner 30, the exhaust temperature of the air conditioner 30, the intake temperature of the heating element 2, the exhaust temperature of the heating element 2, and the temperature of the heating element 2.
[0073] In this way, the control device 10 can predict the heat load and select the optimal setting conditions by using various temperatures measured in the facility R. In other words, the control device 10 can use the temperature sensors already installed in the facility R, the heating element 2, and the air conditioning equipment 3, thereby reducing the cost of introducing new sensors.
[0074] (4) According to a fourth aspect, in the control device 10 of the air conditioning equipment 3 relating to any one of the first to third aspects, the set conditions further include at least one of the number of operating air conditioners 30 and the air volume of the air conditioner 30.
[0075] In this way, the control device 10 can control the air conditioning equipment 3 by combining various setting conditions according to the number and functions of the air conditioners 30, the specifications of the fans 20 of the heating elements 2, etc. Also, by combining multiple setting conditions, the control device 10 can finely control the air conditioning equipment 3 so that the future heat load is in a more appropriate state.
[0076] (5) According to a fifth aspect, in the control device 10 of the air conditioning equipment 3 relating to any one of the first to fourth aspects, the air conditioning equipment 3 includes a fan 20 provided in the heating element 2, and the setting conditions further include the air volume or rotation speed of the fan 20.
[0077] (6) According to a sixth aspect, in the control device 10 of the air conditioning equipment 3 relating to any one of the first to fifth aspects, the thermal load includes at least one of the room temperature of the facility R, the intake temperature of the air conditioner 30, the exhaust temperature of the air conditioner 30, the intake temperature of the heating element 2, the exhaust temperature of the heating element 2, and the temperature of the heating element 2, and the power usage efficiency (PUE) of the facility R, and the control unit 113 selects a setting condition candidate where the predicted value of the thermal load other than the power usage efficiency satisfies the environmental conditions and the predicted value of the power usage efficiency is minimized.
[0078] In this way, the control device 10 can control the air conditioning equipment 3 so that the future heat load is in a more appropriate state and the PUE is minimized.
[0079] (7) According to a seventh aspect, in the control device 10 of the air conditioning equipment 3 relating to any one of the first to sixth aspects, the air conditioning equipment 3 further includes a plurality of humidifiers 35, the measurement values and the thermal load further include the humidity of the facility R, and the setting conditions further include the number of humidifiers in operation.
[0080] In this way, the control device 10 can control the air conditioning equipment 3 so that not only the temperature but also the humidity within the facility R is kept at an appropriate state.
[0081] (8) According to an eighth aspect, a method for controlling an air conditioning equipment 3 including an air conditioner 30 installed in a facility R in which a plurality of heating elements 2 are arranged includes the steps of: acquiring measurement values including a temperature measured at a predetermined position in the facility R and setting conditions of the air conditioning equipment 3 including a set temperature of the air conditioner 30; predicting future heat loads corresponding to each of a plurality of candidate setting conditions using a model M having the measurement values and the setting conditions as explanatory variables and the heat load due to heat generation by the heating elements 2 as a target variable; and selecting, from the candidate setting conditions, a candidate setting condition for which a predicted value that satisfies an environmental condition that specifies a reference value or reference range for the heat load has been obtained, and controlling the air conditioning equipment 3 based on the selected candidate setting conditions.
[0082] (9) According to the ninth aspect, the program causes a control device 10 of an air conditioning equipment 3 including an air conditioner 30 installed in a facility R in which a plurality of heating elements 2 are arranged to execute the steps of acquiring measurement values including a temperature measured at a predetermined position in the facility R and setting conditions of the air conditioning equipment 3 including a set temperature of the air conditioner 30, predicting future heat loads corresponding to each of a plurality of candidate setting conditions using a model M in which the measurement values and the setting conditions are explanatory variables and the heat load due to heat generation by the heating elements 2 is a target variable, and selecting, from the candidate setting conditions, a candidate setting condition for which a predicted value that satisfies an environmental condition that specifies a reference value or reference range for the heat load has been obtained, and controlling the air conditioning equipment 3 based on the selected candidate setting conditions. [Explanation of symbols]
[0083] 1. Air conditioning system 2 Heating element 3 Humidifier 10 Control device 11 Processors 111 Acquisition Department 112 Prediction Department 113 Control Unit 114 Learning Department 12. Memory 13. Storage 14 Communication Interface 20 Fans 20 Heating element 21 Intake air temperature sensor 22 Exhaust temperature sensor 30 Air conditioner 31 Intake temperature sensor 32 Exhaust temperature sensor 35 Humidifier 51 Room temperature sensor 51 Temperature Sensor 51 Humidity Sensor 52 Humidity Sensor CA Cold Aisle HA Hot Aisle M Models
Claims
1. A control device for an air conditioning system including an air conditioner installed in a facility in which a plurality of heating elements are arranged, an acquisition unit that acquires measurement values including temperatures measured at predetermined positions in the facility and setting conditions of the air conditioning equipment including setting temperatures of the air conditioners; a prediction unit that predicts a future heat load corresponding to each of a plurality of candidate setting conditions by using a model in which the measurement value and the setting conditions are explanatory variables and the heat load due to the heat generated by the heating element is a response variable; and a control unit that selects a setting condition candidate from the setting condition candidates that has obtained a predicted value that satisfies an environmental condition that defines a reference value or a reference range of the heat load, and controls the air conditioning equipment based on the selected setting condition candidate; A control device for air conditioning equipment comprising:
2. A learning unit is further provided which identifies an actual value of the thermal load at the evaluation time from the measurement value acquired at the evaluation time and the setting condition, and learns and updates the model based on an error between the identified actual value of the thermal load and a predicted value of the thermal load at the evaluation time predicted in the past. The control device for an air conditioning system according to claim 1.
3. The temperature measured at a predetermined position in the facility is at least one of a room temperature of the facility, an intake temperature of the air conditioner, an exhaust temperature of the air conditioner, an intake temperature of the heating element, an exhaust temperature of the heating element, and a temperature of the heating element. The control device for an air conditioning system according to claim 1.
4. The set conditions further include at least one of the number of operating air conditioners and the air volume of the air conditioners. The control device for an air conditioning system according to claim 1.
5. The air conditioning equipment includes a fan provided in the heating element, The set conditions further include an air volume or a rotation speed of the fan. The control device for an air conditioning system according to claim 1.
6. the thermal load includes at least one of a room temperature of the facility, an intake temperature of the air conditioner, an exhaust temperature of the air conditioner, an intake temperature of the heating element, an exhaust temperature of the heating element, and a temperature of the heating element, and a power usage efficiency of the facility; the control unit selects the setting condition candidate such that a predicted value of the thermal load other than the power usage efficiency satisfies the environmental condition and the predicted value of the power usage efficiency is minimized. The control device for an air conditioning system according to claim 1.
7. the air conditioning unit further comprises a plurality of humidifiers; The measurements and the heat load further include humidity of the facility; The set conditions further include the number of humidifiers in operation. The control device for an air conditioning system according to any one of claims 1 to 6.
8. A method for controlling an air conditioning system including an air conditioner installed in a facility in which a plurality of heating elements are arranged, comprising the steps of: acquiring measurement values including a temperature measured at a predetermined position in the facility and setting conditions of the air conditioning equipment including a set temperature of the air conditioner; predicting a future heat load corresponding to each of a plurality of candidate setting conditions by using a model in which the measured values and the setting conditions are explanatory variables and the heat load due to the heat generated by the heating element is a response variable; selecting a setting condition candidate from the setting condition candidates that has obtained a predicted value that satisfies an environmental condition that defines a reference value or a reference range of the heat load, and controlling the air conditioning equipment based on the selected setting condition candidate; A method for controlling an air conditioning system comprising the steps of:
9. A control device for an air conditioning system including an air conditioner installed in a facility in which a plurality of heating elements are arranged, acquiring measurement values including a temperature measured at a predetermined position in the facility and setting conditions of the air conditioning equipment including a set temperature of the air conditioner; predicting a future heat load corresponding to each of a plurality of candidate setting conditions by using a model in which the measured values and the setting conditions are explanatory variables and the heat load due to the heat generated by the heating element is a response variable; selecting a setting condition candidate from the setting condition candidates that has obtained a predicted value that satisfies an environmental condition that defines a reference value or a reference range of the heat load, and controlling the air conditioning equipment based on the selected setting condition candidate; A program that executes the following.
Citation Information
Patent Citations
Polypropylene synthetic resin expanded molding
JP1983001732A
Management program, management device and management method
JP2023026104A