Control device, control system, control method, and program

Abstract

The system includes an optimization unit (13) that calculates control data including a stage target value by optimization calculation based on a simulation result obtained by causing a simulator unit (21) to execute a simulation, an optimal control calculation unit (14) that starts up a controlled device (4), collects sensor data related to a controlled amount measured after the start-up of the controlled device (4), and calculates a control amount for the controlled device (4) based on the collected sensor data so that the controlled amount follows the stage target value calculated by the optimization unit (13), and a control unit (15) that controls the controlled device (4) with the control amount calculated by the optimal control calculation unit (14).

Description

[Technical field]

[0001] The present disclosure relates to a control device, a control system, and a control method that control a device to be controlled (hereinafter referred to as a "controlled device") so that a controlled amount, which is a measurement value aiming for a target value, reaches a target value at a target time (hereinafter referred to as a "target time"). [Background technology]

[0002] Conventionally, in systems such as air conditioning systems, a technique is known in which a control amount of a controlled device is calculated using a modern control method represented by LQR (Linear-Quadratic Regulator) so that the controlled amount reaches a target value at a target time. The modern control method tracks the behavior of the controlled device and calculates the control amount of the controlled device based on the difference between the target value and the current value aiming for the target value, and is widely used to achieve stable control. Modern control is an effective method for calculating a control amount that allows the controlled amount to reach the target value quickly while minimizing the energy consumption of the controlled device. Incidentally, as a method for calculating an optimal control input in system control while reducing the power consumption in the entire system, for example, an optimization method represented by a genetic algorithm is known, which uses the power consumption of a device installed in the system as an objective function, searches for decision variables that minimize the objective function by optimization calculation, and obtains the optimal setting value of the device (for example, Patent Document 1). The optimization method is a method for finding an optimal solution by adjusting variables under constraint conditions, and is an effective method for achieving a goal and optimizing efficiency at the same time. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-89089 Summary of the Invention [Problem to be solved by the invention]

[0004] In some cases, it may be possible to operate controlled equipment more efficiently by reducing its capacity to a certain extent rather than operating it at full power. The relationship between the capacity and power consumption of a controlled device varies depending on the controlled device or the operating environment of the controlled device. Therefore, it is effective to use modern control methods to calculate a highly efficient control amount that reduces the overall amount of energy consumption. However, modern control is based on the concept that it is sufficient if the controlled quantity reaches the target value by the target time. Therefore, in conventional technology that uses the modern control method to control the controlled device so that the controlled quantity reaches the target value at the target time, there is a problem that the controlled quantity may reach the target value too early, resulting in energy loss. In addition, the optimization method can calculate an optimal operation schedule for achieving a purpose, including the start time or various set values ​​of the controlled equipment, but the optimization method has difficulty in calculating a fine control amount, i.e., continuous optimization. Therefore, the control with the control amount calculated by the optimization method is a control with a somewhat discrete time width. In this way, in the technology for controlling the controlled equipment so that the controlled amount reaches the target value by the target time, for example, even if the modern control method is directly replaced with the optimization method, another problem occurs.

[0005] The present disclosure has been made to solve the above-mentioned problems, and aims to provide a control device that can perform targeted control at a target time and prevent energy loss caused by the controlled quantity reaching a target value earlier than the target time. [Means for solving the problem]

[0006] The control device according to the present disclosure is a control device that controls a controlled device to be controlled so that a controlled quantity, which is a measurement value aiming for a target value, reaches the target value at a target time, and includes an optimization unit that calculates control data including a stage target value, which is a staged target value until the controlled quantity reaches the target value, by optimization calculation based on a simulation result indicating an error between the controlled quantity and the target value and power consumption obtained by having a simulator unit that executes a simulation to simulate the operation or behavior of the controlled device execute a simulation, an optimal control calculation unit that starts the controlled device, collects sensor data related to the controlled quantity measured after the controlled device is started, and calculates a control quantity for the controlled device based on the collected sensor data so that the controlled quantity follows the stage target value calculated by the optimization unit, and a control unit that controls the controlled device with the control quantity calculated by the optimal control calculation unit. Effect of the Invention

[0007] According to the present disclosure, the control device can perform targeted control at a target time and prevent energy loss caused by the controlled amount reaching a target value earlier than the target time. [Brief description of the drawings]

[0008] [Figure 1] These are diagrams for explaining the advantages and disadvantages of modern control and optimization methods. Figure 1A is a diagram for explaining the advantages and disadvantages of using a control engineering approach, in other words, a modern control method, to control the fan airflow so that the room temperature in a room reaches the temperature set as the target value at the target time. Figure 1B is a diagram for explaining the advantages and disadvantages of using an optimization approach, in other words, an optimization method, to control the fan airflow so that the room temperature in a room reaches the temperature set as the target value at the target time. [Diagram 2] 1 is a diagram illustrating a configuration example of a control system including a control device according to a first embodiment. [Diagram 3]FIG. 3A is a diagram for explaining an example of a stage target value of the room temperature at a stage time calculated by an optimization calculation in the "pre-optimization processing" in the control device in embodiment 1, and an image of a control quantity calculated by modern control in the "modern control processing" to follow the stage target value calculated by the optimization calculation. FIG. 3A is a diagram for explaining an example of a stage target value of the room temperature at a stage time calculated in the "pre-optimization processing", and FIG. 3B is a diagram for explaining an example of a control quantity calculated to follow the stage target value calculated in the "modern control processing". [Figure 4] 4 is a flowchart for explaining the operation of the control system according to the first embodiment. [Diagram 5] 4 is a flowchart for explaining the operation of the control device according to the first embodiment. [Figure 6] 6A and 6B are diagrams illustrating an example of a hardware configuration of the control device according to the first embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. Embodiment 1 As mentioned above, modern control and optimization techniques are different approaches, each with their own advantages and disadvantages. Here, the advantages and disadvantages of modern control and optimization techniques are explained with reference to figures.

[0010] FIG. 1 is a diagram used to explain the advantages and disadvantages of modern control and optimization methods. Here, as an example, we will explain the advantages and disadvantages of modern control and optimization methods using an air conditioning system that controls the room temperature in a room to a target temperature set at a target time. The air conditioning system here is assumed to be, for example, a chiller system that conditions air in air-conditioned rooms in a building. The chiller system is assumed to be a general chiller system, and a configuration example thereof will be briefly described.

[0011] The chiller system includes air conditioning-related equipment, such as an air-cooled chiller, a cushion tank, an outdoor air conditioning unit, and an AHU (air handling unit). The air-cooled chiller and the outdoor air-conditioning unit are connected via a chilled water circuit. An air-cooled chiller is a cooling device that uses outside air to dissipate heat. The outdoor air conditioner controls the temperature of the chilled water in the chiller system. The outdoor air conditioner includes a condenser and a fan. An AHU is an air processing device consisting of filters, dampers, fans, humidifiers, coolers, etc. It adjusts the air for cooling or heating using cold water whose temperature is controlled by an outdoor air conditioning unit, and supplies the air to living rooms. The cushion tank absorbs water pressure fluctuations in the chilled water circuit within the chiller system. The chilled water circuit is equipped with a pump to circulate the cooling water. The chilled water circuit is also equipped with a bypass valve and a valve to control the flow of cooling water in the chiller system. These pumps, bypass valves, and valves are also included in the air conditioning-related equipment. In addition, the chiller system is equipped with various sensors, such as a sensor that measures the water supply temperature at each point in the chilled water circuit, a sensor that measures the water supply differential pressure, a sensor that measures the flow rate of each bypass valve, a sensor that measures the room temperature of the living room, a sensor that measures the outside air temperature, and a sensor that measures the power consumption in the chiller system. The air conditioning-related devices and various sensors are connected to each other via a control network.

[0012] Here, the aim is to have the room temperature reach a target value at a target time by controlling the fan airflow in the AHU. In an air conditioning system, air conditioning-related equipment is the equipment that is subject to control (hereinafter referred to as "controlled equipment"). Hereinafter, the fan airflow in the AHU will simply be referred to as the fan airflow. As described above, in this example, the fan airflow is the controlled variable. Also, the room temperature is controlled to approach a target value by controlling the fan airflow. The room temperature can also be said to be a controlled variable that is controlled by controlling the controlled variable (here, the fan airflow). In the following description, when referring to a "controlled variable," the "controlled variable" refers to any measured value that aims to reach a target value at a target time.

[0013] 1A is a diagram for explaining advantages and disadvantages of controlling the fan airflow rate so that the room temperature in a room reaches a temperature set as a target value at a target time using a control engineering approach, in other words, a modern control method. An example of a modern control method is LQR (Linear-Quadratic Regulator). Since LQR is a well-known method, detailed description will be omitted. 1B is a diagram for explaining advantages and disadvantages of controlling the fan airflow rate so that the room temperature in a room reaches a temperature set as a target value at a target time by using an optimization approach, in other words, an optimization method. Examples of the optimization method include a genetic algorithm and Bayesian optimization. Since the genetic algorithm and Bayesian optimization are well-known methods, detailed explanations are omitted.

[0014] As shown in Figure 1A, for example, when using a control engineering approach to control the fan airflow so that the room temperature in a room reaches a temperature set as a target value at a target time, it is possible to calculate the fan airflow that will allow the room temperature to reach the target value as quickly as possible while minimizing the amount of energy consumed by the controlled device (here, the fan). However, the control engineering approach is based on the concept that it is sufficient if some measured value aiming at a target value, i.e., the controlled quantity, reaches the target value at the target time, and in the control engineering approach, the initial fan airflow volume after the controlled device in the air conditioning system starts to increase so that the target value is reached quickly. As a result, the room temperature may reach the target value earlier than the target time, resulting in energy loss for that amount, that is, energy loss of energy consumed by the fan or other controlled devices, and energy loss of energy required to maintain the room temperature until the target time when the target value is reached earlier than the target time. With the control engineering approach, it is difficult to perform control that also includes optimizing the start-up time of the controlled device. With the control engineering approach, when it is necessary to obtain other setting values ​​related to the controlled amount in the controlled device, there is also the problem that it is difficult to calculate the optimal setting value at the same time as the controlled amount. In this example, with the control engineering approach, it is difficult to calculate the setting value related to the fan airflow, such as the bypass valve opening or water temperature, at the same time as the fan airflow. Furthermore, the control engineering approach has the problem that it is difficult to calculate the control amount that minimizes the amount of energy consumed when controlling a complex system.

[0015] On the other hand, as shown in Figure 1B, when the optimization approach controls the fan volume so that the room temperature reaches the temperature set as the target value at the target time, it is possible to calculate the optimal operation schedule, including the start-up times of the controlled devices and the fan volume, for the room temperature to reach the target value at the target time. With the optimization approach, it is possible to calculate the optimal control amount and other setting values ​​even for complex systems. However, the optimization approach has the problem that continuous optimization is difficult, and control is performed over a somewhat discrete time span. For example, as shown in Figure 1B, in the optimization approach, the fan airflow is controlled for each section separated by a certain interval. As a result, the room temperature changes stepwise at a certain interval. Thus, with the optimization approach, it is difficult to continuously optimize the fan airflow, in other words, to precisely calculate the fan airflow. In other words, with the optimization approach, it is difficult to precisely control the room temperature. This is because, when trying to continuously find the optimal solution, the search space becomes larger and the amount of calculations increases, so it takes time to find the optimal solution. Such control over a somewhat discrete time span is not desirable for controlling the room temperature. However, as shown in Figure 1A, with the control engineering approach, precise control of the room temperature is possible. Furthermore, in the optimization approach, when an optimal operation schedule is calculated using a simulator, there is a problem that the accuracy of the calculated operation schedule may deteriorate if there is a difference between the simulator and the actual equipment to be controlled.

[0016] Thus, although modern control allows for precise control of room temperature, which is desirable, there is a problem in that the room temperature may reach the target value earlier than the target time, resulting in energy loss. In contrast, the optimization method makes it possible to perform control including optimizing the startup time or various setting values ​​of the controlled equipment, but does not allow fine adjustment of the room temperature.

[0017] The present disclosure focuses on the advantages and disadvantages of modern control and optimization methods as described above, and by combining modern control with optimization methods, it enables optimization of the startup of controlled equipment, including optimization of the startup time and various setting values ​​of the controlled equipment, which was difficult to achieve using modern control alone. This enables targeted control (more specifically, detailed control) to be performed at the target time, while preventing energy loss caused by the controlled quantity reaching the target value earlier than expected.

[0018] FIG. 2 is a diagram showing an example of a configuration of a control system 100 including the control device 1 according to the first embodiment. In the following first embodiment, as an example, the control system 100 is assumed to be an air conditioning system as described above with reference to Fig. 1. Note that air conditioning-related devices in the air conditioning system are collectively referred to as control target devices 4. The air conditioning-related devices are targets of control by the control device 1. In the following first embodiment, as an example, the controlled amount is the fan air volume, and the controlled amount is the room temperature. In the following embodiment 1, as an example, the control device 1 controls the fan air volume so that the room temperature in the room reaches the target value at the target time. More specifically, the control device 1 calculates the fan air volume so that the room temperature in the room reaches the target value at the target time, and controls the fan in the AHU to the calculated fan air volume. Note that the room is not shown in FIG. 2. The target time and the target value aiming to reach the target time are determined in advance by an administrator, etc. In the first embodiment, the target value aiming to reach the target time is also referred to as a "final target value."

[0019] As shown in FIG. 2, the control system 100 includes, for example, a control device 1, a simulator 2, a model creating device 3, and a control target device 4. For example, the model creating device 3 may be provided in a system other than the control system 100, and the control system 100 may not include the model creating device 3.

[0020] The control device 1 calculates a control quantity (here, the fan airflow) for the controlled device 4 so that a certain measurement value (i.e., a controlled quantity, here, the room temperature) measured in the control system 100 reaches a target value at a target time, and controls the controlled device 4 to achieve the calculated control quantity. The control device 1 is assumed to be mounted on, for example, a server (not shown).

[0021] The control device 1 includes a simulation execution instruction unit 11, a simulation result collection unit 12, an optimization unit 13, an optimal control calculation unit 14, a control unit 15, and a data collection unit 16.

[0022] The control device 1 causes the simulator 2 to execute a simulation, and based on the results of the simulation, calculates, through optimization calculations, the target value (hereinafter referred to as the "step target value") of the controlled quantity (here, room temperature) at each step time (hereinafter referred to as the "step time") up to the target time, the start-up time of the controlled device 4, and the setting value (e.g., water supply temperature, etc.).

[0023] In embodiment 1, the process performed by the control device 1 to have the simulator 2 execute a simulation and calculate the stage target values ​​of the controlled quantities at stage times up to the target time, the start-up time of the controlled device 4, and the setting values ​​through optimization calculations is referred to as the "pre-optimization process."

[0024] Furthermore, the control device 1 starts the controlled device 4 at the start time of the controlled device 4 calculated by performing the "pre-optimization process" with a set value (e.g., water supply temperature, etc.) calculated by performing the "pre-optimization process". The control device 1 receives feedback from the controlled device 4, and more specifically, collects data (hereinafter referred to as "sensor data") related to the controlled amount (here, room temperature) measured after the start of the controlled device 4 from a sensor (not shown), and calculates a controlled amount (here, FAN air volume) so that the controlled amount follows the stage target value calculated by performing the "pre-optimization process". Then, the control device 1 controls the controlled device 4, which is controlled with the controlled amount, with the calculated controlled amount. Here, the control device 1 controls the FAN in the AHU so that the calculated FAN air volume is achieved. The control device 1 calculates the above control amount using modern control.

[0025] In embodiment 1, the process performed by the control device 1 to start up the controlled device 4 based on the start-up time and setting values ​​of the controlled device 4 calculated by performing the "pre-optimization process", calculate a control amount so as to follow the stage target value of the controlled amount at the stage time up to the target time calculated by performing the "pre-optimization process" while receiving feedback from the controlled device 4, and control the controlled device 4 with the calculated control amount is referred to as the "modern control process".

[0026] In the above-mentioned "pre-optimization process", a simulation execution instruction unit 11, a simulation result collection unit 12, and an optimization unit 13 function. In the above-mentioned "modern control process", an optimum control calculation unit 14, a control unit 15, and a data collection unit 16 function. Below, a detailed description of the above-mentioned "pre-optimization process" and "modern control process" will be given, while an example of the configuration of the control device 1 will be explained.

[0027] The simulation execution command unit 11 inputs operating condition parameters to the simulator 2 and causes the simulator 2 to execute a simulation. The operating condition parameters input by the simulation execution instruction unit 11 to the simulator 2 include the start-up time of the controlled device 4, the stage target value of the controlled variable (here, room temperature) at the stage time up to the target time, and the setting value of the controlled device 4.

[0028] Here, the simulator 2 will be described. The simulator 2 is a simulator that uses a general simulation technique. The simulator 2 includes a simulator unit 21 and a data storage unit 22 . The simulator unit 21 simulates the operation or behavior of the controlled device 4 based on the read operating condition parameters, and performs a simulation of changes in the indoor environment or the energy consumption of the controlled device 4, or the like. In the first embodiment, the simulator unit 21 performs the above simulation using PID control (Proportional-Integral-Derivative control), which is a known control technique. By performing the simulation using PID control, the simulator unit 21, more specifically, the control device 1 that causes the simulator unit 21 to perform the simulation, does not need to collect data in advance to perform the simulation. The operating condition parameters read by the simulator unit 21 are the operating condition parameters input by the simulation execution instruction unit 11 described above.

[0029] When the simulator unit 21 performs the simulation, it calculates the error between the target value of the room temperature and the current room temperature in the simulation environment, and the power consumption consumed by the controlled device 4. The simulator unit 21 outputs data indicating the calculated error and power consumption (hereinafter referred to as the “simulation result”) to the control device 1. Moreover, the simulator unit 21 stores various data collected as a result of performing the simulation as simulation data in the data storage unit 22. The simulation data includes operation data (data indicating the operation status, such as the control amount of the control target device 4), sensor data (measured values ​​measured by various sensors, such as water temperature, valve opening, power consumption, room temperature, fan air volume, etc.), the simulation result calculated by the simulator unit 21, and operation condition parameters read by the simulator unit 21. In this embodiment, the simulator 2 is provided with the data storage unit 22, but this is merely an example. The data storage unit 22 may be provided in a location outside the simulator 2 that can be referenced by the simulator 2 and the model creation device 3.

[0030] Returning to the explanation of the control device 1, The simulation result collection unit 12 collects the simulation results from the simulator unit 21 .

[0031] The optimization unit 13 uses a known optimization algorithm based on the simulation results collected by the simulation result collection unit 12 to calculate the optimal start-up time of the controlled device 4, the optimal stage target value of the controlled quantity (here, room temperature) at the optimal stage time until the target time, and the optimal setting value of the controlled device 4. The known optimization algorithm is an algorithm such as a genetic algorithm or Bayesian optimization. The optimization unit 13, for example, calculates an optimal start-up time for the controlled device 4, an optimal stage target value of the controlled quantity at an optimal stage time until the target time, and an optimal setting value for the controlled device 4 by selecting a combination of the start-up time of the controlled device 4 that consumes the least amount of power, the stage target value of the controlled quantity at the stage time until the target time, and the setting value of the controlled device 4.

[0032] The optimization unit 13 changes the operating condition parameters until an optimal solution is found, inputs the changed operating condition parameters to the simulator 2 via the simulation execution instruction unit 11, collects the simulation results via the simulation result collection unit 12, and repeats the optimization calculations as described above to search for the optimal solution. The optimization unit 13 may appropriately set the start time of the controlled device 4, a set of a stage time and a stage target value at the stage time, and the setting value of the controlled device 4 for the operating condition parameters when first issuing an instruction to execute a simulation to the simulator unit 21. For example, it is assumed that initial values ​​are set in advance, and the optimization unit 13 may set the initial values ​​to the start time of the controlled device 4, the set of a stage time and a stage target value, and the setting value of the controlled device 4, or it is assumed that setting conditions are defined in advance, and the optimization unit 13 may calculate the start time of the controlled device 4, the set of a stage time and a stage target value, and the setting value of the controlled device 4 in accordance with the setting conditions.

[0033] The functions of the simulation execution instructing unit 11 and the simulation result collecting unit 12 may be included in the optimization unit 13. In this case, the control device 1 may be configured not to include the simulation execution instructing unit 11 and the simulation result collecting unit 12.

[0034] When the optimization unit 13 finds an optimal solution, it outputs data (hereinafter referred to as "control data") including the optimal start-up time of the controlled device 4, the optimal step target value of the controlled quantity at the optimal step time up to the target time, and the optimal setting value of the controlled device 4 to the optimal control calculation unit 14.

[0035] For example, the optimization unit 13 calculates the start-up time of the controlled device 4, step target values ​​of the controlled variables at step times up to the target time, and the set values ​​of the controlled device 4 as follows. Note that in the following, the set values ​​are, as an example, the bypass valve opening and the supply water temperature. {Start time}={6:50} {Setting value} = [{Bypass valve opening, 50%}, {Water supply temperature, 40℃}] {time, target value}=[{7:00,22℃},{7:15,23℃}] In this way, the optimization unit 13 calculates the stage target values ​​of the controlled quantities at the stage times up to the target time, in pairs of corresponding stage times and stage target values. The optimization unit 13 can calculate a plurality of pairs of stage times and stage target values. It is sufficient that the optimization unit 13 is configured to calculate one or more pairs of stage times and stage target values.

[0036] The optimum control calculation unit 14 starts up the controlled device 4 with the optimum setting value calculated by the optimization unit 13 at the optimum start time calculated by the optimization unit 13 based on the control data output from the optimization unit 13. Note that the controlled device 4 started up by the optimum control calculation unit 14 here is a real device. The optimal control calculation unit 14 starts up the controlled device 4, and when the controlled device 4 starts up, the data collection unit 16 collects sensor data related to the controlled quantity (here, room temperature). The data collection unit 16 collects the sensor data at a preset cycle while the controlled device 4 is in operation.

[0037] The optimal control calculation unit 14 uses a known modern control algorithm based on the sensor data collected by the data collection unit 16 to calculate a control amount (here, the fan air volume) so as to follow the optimal stage target value at the optimal stage time calculated by the optimization unit 13. The known modern control algorithm is an algorithm such as LQR. The optimal control calculation unit 14 calculates the control amount at a preset cycle. The optimal control calculation unit 14 calculates the controlled variable using a model (hereinafter referred to as a "system identified model") in which the relationship between the controlled variable and the power consumption and the relationship between the controlled variable and the controlled variable are modeled. Modern control is a method that allows for efficient control that takes into account energy consumption, but it requires system identification of power consumption, which means that the above system identified model must be prepared. The system identified model is created by the model creation device 3 before the "modern control process" is executed by the control device 1. The model creating device 3 will be described in detail later.

[0038] The optimal control calculation unit 14 determines which stage target value should be followed, among the stage target values ​​set in the control data, so as to follow the stage target value corresponding to the current time, based on the sensor data collected by the data collection unit 16. The optimal control calculation unit 14 calculates a control amount using a system identified model so as to follow the stage target value determined to be followed. The optimal control calculation unit 14 can determine the stage target value to be followed by comparing the current time with the stage time associated with the stage target value in the control data. The optimum control calculation unit 14 receives feedback from the controlled device 4 and calculates the control amount so as to follow the optimum stage target value.

[0039] The function of the data collecting unit 16 may be included in the optimal control calculation unit 14. In this case, the control device 1 may be configured without including the data collecting unit 16.

[0040] The control unit 15 operates the control target device 4 (here, the AHU fan) that performs control with the control amount (here, the fan air volume) calculated by the optimal control calculation unit 14.

[0041] Here, we will use drawings to explain the image of the stage target value of the room temperature at a stage time calculated by optimization calculation in the "pre-optimization process" in the control device 1, and the control amount calculated by modern control in the "modern control process" to follow the stage target value calculated by optimization calculation. FIG. 3 is a diagram for explaining an image of the stage target value of the room temperature at a stage time calculated by optimization calculation in the "pre-optimization process" in the control device 1 in embodiment 1, and the control amount calculated by modern control in the "modern control process" so as to follow the stage target value calculated by the optimization calculation. FIG. 3A is a diagram for explaining an example of a stage target value of room temperature at a stage time calculated by the "pre-optimization process," and FIG. 3B is a diagram for explaining an example of a control amount that is set to follow the stage target value calculated by the "modern control process." 3, it is assumed that the target time is set to "7:30" and the final target value is set to "24° C." Furthermore, it is assumed that the control device 1 calculates the control amount every minute.

[0042] For example, in the control device 1, the optimization unit 13 sets the optimal start-up time of the controlled device 4 to "6:50" and calculates two optimal pairs of stage times and stage target values: a pair of stage time "7:00" and stage target value "22°C", and a pair of stage time "7:15" and stage target value "23°C" (see Figure 3A). In this case, the optimal control calculation unit 14 starts the controlled device 4 at "6:50", calculates the control amount, here the fan airflow, every minute from "6:50" to "7:00" so as to follow the stage target value of "22°C", calculates the fan airflow every minute from "7:00" to "7:15" so as to follow the stage target value of "23°C", and calculates the fan airflow every minute from "7:15" to "7:30" so as to follow the final target value of "24°C" (see Figure 3B).

[0043] In this way, in the control device 1, the optimization unit 13 uses an optimization algorithm to calculate a pair of the start-up time of the controlled device 4 and the stage target value at the optimal stage time until the controlled quantity reaches the final target value at the target time. Then, the optimal control calculation unit 14 uses a modern control algorithm to calculate the controlled variable at each step up to each stage time calculated by the optimization unit 13 so that the controlled variable reaches the corresponding stage target value and achieves optimal energy (low power consumption). This allows the control device 1 to realize energy-saving control including the start-up time. In other words, the control device 1 calculates the control amount stepwise following the step target value set stepwise up to the final target value, thereby preventing the control amount from becoming too large when the controlled device 4 is started up, and preventing the controlled amount from reaching the final target value prematurely. As a result, the control device 1 can prevent energy loss caused by the controlled amount reaching the final target value prematurely.

[0044] The model creation device 3 creates a system identified model that is used by the optimal control calculation unit 14 of the control device 1 in the "modern control process." The model creating device 3 is assumed to be mounted on, for example, a server (not shown). The model creating device 3 includes a model creating unit 31 and a model storage unit 32 . The model creation unit 31 creates a system identified model by using the simulation data stored in the data storage unit 22 and output when the simulator unit 21 executes a simulation in the "pre-optimization process." The model creation unit 31 stores the created system identified model in the model storage unit 32 . In this embodiment, the model creating device 3 is provided with the model storage unit 32, but this is merely an example. The model storage unit 32 may be provided in a location outside the model creating device 3 that can be referenced by the model creating device 3 and the control device 1. In the first embodiment, the process performed by the model creation device 3 to create a system-identified model using the simulation data output when the "pre-optimization process" is executed by the control device 1 is referred to as "system identification process".

[0045] The system identified model created by the model creation unit 31 is, for example, the following model. TIFF0007446546000001.tif12166 x: state variable y: Output (controlled amount) u: Input (control amount) Here, the state variables are the bypass valve opening, the water temperature, the power consumption, etc. The output is the room temperature. The input is the fan airflow.

[0046] As described above, in the control device 1, the optimization unit 13 causes the simulator unit 21 of the simulator 2 to execute a simulation during the optimization calculation process. The simulator unit 21 performs PID control. Therefore, various data in which the controlled variable is changed during the optimization calculation process is output as simulation data and stored in the data storage unit 22. The model creation unit 31 creates a system identified model using simulation data, thereby making it possible to shorten the time required to collect data for creating the system identified model. The control device 1 utilizes the simulation data obtained in the optimization process for modern control, thereby shortening the data collection period required for modern control.

[0047] The operation of the control system 100 according to the first embodiment will be described. FIG. 4 is a flowchart for explaining the operation of the control system 100 according to the first embodiment. In FIG. 4, the process of step ST10 and the process of step ST30 are performed by the control device 1, and the process of step ST20 is performed by the model creating device 3. The control system 100 starts the operation as shown in the flowchart of Fig. 4 when, for example, the control device 1 receives an instruction to perform control such that the controlled amount reaches a target value (final target value) by a target time. For example, an administrator or the like inputs an instruction to perform the above control from an administration terminal such as a PC (Personal Computer). At this time, the administrator or the like may also input the target time and the final target value. The administration terminal is connected to the control system 100 via a network. The control system 100 performs, for example, the process of step ST10 and the process of step ST20 once after starting the operation. The control system 100 repeats the process of step ST30, for example, until the control device 1 receives a control end instruction. For example, an administrator or the like inputs the control end instruction from a management terminal. In the flowchart of FIG. 4, for the sake of convenience, the processes from step ST10 to step ST30 are shown as a continuous flow.

[0048] The control device 1 performs a "pre-optimization process" (step ST10). Specifically, the control device 1 causes the simulator 2 to execute a simulation and calculates, through optimization calculations, the stage target value of the controlled quantity (here, room temperature) at the stage time up to the target time, the start-up time of the controlled device 4, and the set value (e.g., water supply temperature, etc.).

[0049] The model creating device 3 performs a "system identification process" (step ST20). Specifically, the model creation device 3 creates a system-identified model by using simulation data output when the control device 1 executes the “pre-optimization process” in step ST10 and the simulator unit 21 executes a simulation. Then, the model creation device 3 stores the created system-identified model in the model storage unit 32.

[0050] The control device 1 performs a "modern control process" (step ST30). Specifically, the control device 1 starts up the controlled device 4 based on the start-up time of the controlled device 4 calculated by performing the "pre-optimization processing" in step ST10 and the setting value, and while receiving feedback from the controlled device 4, calculates a control amount (here, the fan airflow) so as to follow the stage target value of the controlled amount at the stage time up to the target time calculated by performing the "pre-optimization processing", and controls the controlled device 4 (here, the AHU fan) with the calculated control amount.

[0051] In this way, the control system 100 calculates, by optimization calculation, control data including stepwise target values ​​(step target values) until the controlled quantity reaches the target value (final target value), more specifically, the start-up time, setting value, and step-up target value at the step-up time of the controlled device 4, based on the simulation result obtained by having the simulator 2 execute a simulation. Then, the control system 100 starts up the controlled device 4 at the start-up time and setting value calculated by the optimization calculation, calculates the control quantity by a modern control method so that the controlled quantity follows the step-up target value, and controls the controlled device 4. By calculating the step target value using an optimization method, the control system 100 can perform control so that the controlled quantity reaches the target value (final target value) by the target time, and can prevent energy loss caused by the controlled quantity reaching the target value (final target value) earlier than expected. In addition, the control system 100 makes it possible to optimize the startup of the controlled device 4, including the startup time and setting value of the controlled device 4, which was difficult to achieve with modern control alone. In modern control, if the hyperparameters (for example, the weight matrix of the evaluation function in modern control) are inappropriate, there is a possibility that overshoot or hunting will occur. In response to this, the control system 100 can reduce the possibility of the overshoot or hunting occurring by calculating the controlled variable so as to reach the target value (step target value) in stages.

[0052] As mentioned above, modern control is a method that allows for efficient control that takes into account energy consumption, but it requires system identification of power consumption. However, in reality, accurate system identification can be difficult when the system is complex and involves multiple devices. For example, an air conditioning system can be said to be a complex system involving multiple devices. In response to this, the control system 100 calculates a set of a target time (step time) and a target value (step target value) that will achieve a certain degree of energy saving through optimization calculations that do not require modeling by system identification in the control device 1. Then, the control system 100 calculates a control amount so as to follow the step target value. Therefore, the control system 100 can achieve energy-saving control even when accurate system identification is difficult to perform. Furthermore, in the control system 100, more specifically, the model creation device 3 creates a system identified model used to calculate a control amount with reduced power consumption in modern control, using simulation data obtained in the optimization process in the control device 1. This allows the control system 100 to utilize the data obtained in the optimization process (simulation data) for modern control. The control system 100 can contribute to shortening the collection period of data required for modern control, more specifically, for creating a system identified model required for modern control.

[0053] The operation of the control device 1 according to the first embodiment will be described. FIG. 5 is a flowchart for explaining the operation of the control device 1 according to the first embodiment. The control device 1 starts the operation shown in the flowchart of FIG. 5, for example, when receiving an instruction to perform control such that the controlled amount reaches a target value (final target value) by a target time. The control device 1 performs the processes of steps ST101 to ST103 once after starting the operation, for example. The control device 1 repeats the processes of steps ST302 to ST303 until the control device 1 receives a control end instruction, for example. In the flowchart of FIG. 5, for the sake of convenience, the processes from step ST101 to step ST303 are shown as a continuous flow. In the process shown in the flowchart of FIG. 5, operations from step ST101 to step ST103 are operations executed in the "pre-optimization process", and operations from step ST301 to step ST303 are operations executed in the "modern control process".

[0054] The simulation execution instruction unit 11 inputs operating condition parameters to the simulator 2 and causes the simulator 2 to execute a simulation (step ST101). In the simulator 2, the simulator unit 21 simulates the operation or behavior of the controlled device 4 based on the read operating condition parameters by PID control, and simulates changes in the indoor environment or the energy consumption of the controlled device 4, etc. The simulator unit 21 outputs the simulation result to the control device 1. Moreover, the simulator unit 21 stores the simulation data in the data storage unit 22 .

[0055] The simulation result collection unit 12 collects the simulation results from the simulator unit 21 (step ST102).

[0056] Based on the simulation results collected by the simulation result collection unit 12 in step ST102, the optimization unit 13 uses a known optimization algorithm to calculate the optimal start-up time of the controlled device 4, the optimal stage target value of the controlled quantity (here, room temperature) at the optimal stage time until the target time, and the optimal setting value of the controlled device 4 (step ST103). When the optimization unit 13 finds an optimal solution, it outputs control data to an optimal control calculation unit 14 .

[0057] The optimization unit 13 changes the operating condition parameters until an optimal solution is found, inputs the changed operating condition parameters to the simulator 2 via the simulation execution instruction unit 11, collects the simulation results via the simulation result collection unit 12, and repeats the optimization calculation as described above to search for an optimal solution. That is, the control device 1 changes the operating condition parameters and repeats the processes of steps ST101 to ST103 until the optimization unit 13 finds the optimal solution.

[0058] The optimum control calculation unit 14 starts up the controlled device 4 with the optimum setting value at the optimum start time calculated by the optimization unit 13 in step ST103 (step ST301). Note that the controlled device 4 started up by the optimum control calculation unit 14 here is a real device.

[0059] The data collection unit 16 collects sensor data relating to a controlled variable (here, room temperature). Then, the optimal control calculation unit 14 uses the system identified model based on the sensor data collected by the data collection unit 16 and a known modern control algorithm to calculate a control amount (here, the fan air volume) so as to follow the optimal stage target value at the optimal stage time calculated by the optimization unit 13 in step ST103 (step ST302).

[0060] The control unit 15 operates the control target device 4 (here, the AHU fan) that is controlled with the control amount calculated by the optimal control calculation unit 14 in step ST302 (step ST303).

[0061] The control device 1 repeats the processing of steps ST302 to ST303, determines the stage target value to be followed by comparing the current time with the stage time corresponding to the stage target value, and calculates a control amount so as to follow the determined stage target value.

[0062] In this way, the control device 1 calculates control data including step target values, which are step-by-step target values ​​until the controlled amount reaches the target value (final target value), by optimization calculation based on the simulation result obtained by making the simulator unit 21 that executes the simulation execute the simulation and indicating the error between the controlled amount and the target value (final target value) and the power consumption. After performing the optimization calculation, the control device 1 starts the controlled device 4, collects sensor data related to the controlled amount measured after the control target device 4 is started, and calculates the control amount of the controlled device 4 based on the collected sensor data so that the controlled amount follows the calculated step target value. Then, the control device 1 controls the controlled device with the calculated control amount. By calculating the step target value using an optimization method, the control device 1 can control the controlled quantity to reach the target value (final target value) by the target time, and can prevent energy loss caused by the controlled quantity reaching the target value (final target value) earlier than expected. In addition, the control device 1 can optimize the startup of the controlled device 4, including the startup time and setting value of the controlled device 4, which was difficult with modern control alone. In modern control, if the hyperparameters are inappropriate, overshooting or hunting may occur. In response to this, the control device 1 calculates the controlled variable so that it reaches a target value (step target value) in stages, thereby reducing the possibility of the overshooting or hunting occurring.

[0063] As described above, modern control is a method that allows efficient control that takes into account energy consumption, but requires system identification of power consumption. However, in reality, when a system is complicated involving multiple devices, accurate system identification may be difficult. For example, an air conditioning system can be said to be a complicated system involving multiple devices. In response to this, the control device 1 calculates a set of a target time (step time) and a target value (step target value) that will save energy to a certain extent by an optimization calculation that does not require modeling by system identification. Then, the control device 1 calculates a control amount to follow the step target value. Therefore, the control device 1, and therefore the control system 100, can realize energy-saving control even when accurate system identification is difficult. Furthermore, the control device 1 uses the system identified model created using the simulation data obtained in the optimization process to calculate the control amount that reduces power consumption in modern control. The control device 1 can utilize the data (simulation data) obtained in the optimization process for modern control. The control device 1 can contribute to shortening the data collection period required for modern control, more specifically, for creating the system identified model required for modern control.

[0064] 6A and 6B are diagrams illustrating an example of a hardware configuration of the control device 1 according to the first embodiment. In the first embodiment, the functions of the simulation execution instruction unit 11, the simulation result collection unit 12, the optimization unit 13, the optimal control calculation unit 14, the control unit 15, and the data collection unit 16 are realized by a processing circuit 1001. That is, the control device 1 includes the processing circuit 1001 for calculating a control amount by a modern control method and controlling the controlled device 4 so that the controlled amount follows a step-by-step target value calculated by an optimization method. The processing circuit 1001 may be dedicated hardware as shown in FIG. 6A, or may be a processor 1004 executing a program stored in a memory as shown in FIG. 6B.

[0065] When the processing circuit 1001 is dedicated hardware, the processing circuit 1001 may be, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a combination thereof.

[0066] 0 When the processing circuit is the processor 1004, the functions of the simulation execution instruction unit 11, the simulation result collection unit 12, the optimization unit 13, the optimal control calculation unit 14, the control unit 15, and the data collection unit 16 are realized by software, firmware, or a combination of software and firmware. The software or firmware is described as a program and stored in the memory 1005. The processor 1004 executes the functions of the simulation execution instruction unit 11, the simulation result collection unit 12, the optimization unit 13, the optimal control calculation unit 14, the control unit 15, and the data collection unit 16 by reading and executing the program stored in the memory 1005. That is, the control device 1 includes a memory 1005 for storing a program that, when executed by the processor 1004, results in the execution of steps ST101 to ST303 in FIG. 5 described above. It can also be said that the program stored in the memory 1005 causes the computer to execute the procedures or methods of the processing of the simulation execution instruction unit 11, the simulation result collection unit 12, the optimization unit 13, the optimal control calculation unit 14, the control unit 15, and the data collection unit 16. Here, the memory 1005 corresponds to, for example, a non-volatile or volatile semiconductor memory such as a random access memory (RAM), a read only memory (ROM), a flash memory, an erasable programmable read only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic disk, a flexible disk, an optical disk, a compact disk, a mini disk, a digital versatile disk (DVD), and the like.

[0067] The functions of the simulation execution instruction unit 11, the simulation result collection unit 12, the optimization unit 13, the optimal control calculation unit 14, the control unit 15, and the data collection unit 16 may be partially realized by dedicated hardware and partially realized by software or firmware. For example, the control unit 15 may be realized by a processing circuit 1001 as dedicated hardware, and the simulation execution instruction unit 11, the simulation result collection unit 12, the optimization unit 13, the optimal control calculation unit 14, and the data collection unit 16 may be realized by a processor 1004 reading and executing a program stored in a memory 1005. The control device 1 also includes devices such as a simulator 2 and a model creation device 3, as well as an input interface device 1002 and an output interface device 1003 that perform wired or wireless communication.

[0068] An example of the hardware configuration of the model creating device 3 according to the first embodiment is also configured as shown in FIG. 6A and FIG. 6B. In the first embodiment, the function of the model creation unit 31 is realized by a processing circuit 1001. That is, the model creation device 3 includes the processing circuit 1001 for performing control to create a system identified model by using simulation data output when the control device 1 executes the "pre-optimization process." The processing circuit 1001 may be dedicated hardware as shown in FIG. 6A, or may be a processor 1004 executing a program stored in a memory as shown in FIG. 6B.

[0069] When the processing circuit is the processor 1004, the function of the model creation unit 31 is realized by software, firmware, or a combination of software and firmware. The software or firmware is written as a program and stored in the memory 1005. The processor 1004 executes the function of the model creation unit 31 by reading and executing the program stored in the memory 1005. That is, the model creation device 3 includes the memory 1005 for storing a program that, when executed by the processor 1004, results in the execution of step ST20 in FIG. 4 described above. It can also be said that the program stored in the memory 1005 causes a computer to execute the procedure or method of the processing of the model creation unit 31.

[0070] The model storage unit 32 is configured with a memory 1005, a RAM, a ROM, or the like. The model creation device 3 also includes devices such as the control device 1 and simulator 2, as well as an input interface device 1002 and an output interface device 1003 that perform wired or wireless communication.

[0071] In the above embodiment 1, the optimization unit 13 of the control device 1 may be capable of selecting multi-objective optimization in the optimization calculation, which allows a trade-off between comfort, energy saving, or power consumption, in which the measured value and the target value (final target value) match. For example, the user can trade off comfort, i.e., reaching the target value (final target value) at the target time, energy saving, and the demand value, i.e., the maximum power consumption, on the Pareto frontier. visualization The selection can be made by: This enables the control system 100 to be a system capable of setting the target value of the controlled quantity by prioritizing energy saving or demand value, even if it sacrifices some comfort, i.e., even if the target value (final target value) is not reached exactly at the target time. In addition, the control device 1 can be a device that is capable of setting the target value of the controlled quantity by prioritizing energy saving or demand value, even if it sacrifices some comfort, i.e., even if the target value (final target value) is not reached exactly at the target time.

[0072] In addition, in the above embodiment 1, the controlled amount is the room temperature and the controlled amount is the fan air volume, but this is merely an example. For example, the controlled amount may be the supply air temperature of the AHU. Any measured value measured in the air conditioning system can be the controlled amount. Also, for example, the controlled amount may be the set value of the supply air temperature of the fan in the AHU, or the supply air temperature of the AHU. In the air conditioning system, the controlled amount can be a value that can be controlled to control some target set value (controlled amount).

[0073] In the above first embodiment, the control system 100 is assumed to be an air conditioning system, or more specifically, a chiller system. However, this is merely one example. When the control system 100 is an air conditioning system, the air conditioning system may be a VRF (Variable Refrigerant Flow) system. The VRF system performs air conditioning for rooms in a building, for example. In a VRF system, air conditioning-related devices that are the control target devices 4 include, for example, a compressor and an indoor unit. The indoor unit includes a fan, a heat exchanger, and the like. The compressor compresses the refrigerant to high pressure and uses it as a fluid. The refrigerant is sent from the compressor to the indoor unit, where the fan controls the flow of the refrigerant to blow air. The air is heated or cooled by the rotation of the fan, adjusting the temperature in the room. For example, in a VRF system, the control device 1 can control the compressor with the room temperature as the controlled variable and the compressor frequency as the controlled variable so that the room temperature reaches the target value (final target value) at the target time. The control device 1 executes a simulation and calculates the optimal start time of the controlled device 4, the optimal set value, and the optimal stage target value of the room temperature at the optimal stage time by an optimization method. The control device 1 then starts the controlled device 4 at the optimal start time and the optimal set value calculated by the optimization method, calculates the compressor frequency by a modern control method so that the room temperature follows the optimal stage target value, and controls the controlled device 4 (here, the compressor). In this case, the set value is, for example, the fan air volume of the fan in the indoor unit.

[0074] Furthermore, the control system 100 is not limited to an air conditioning system, but may be a system other than an air conditioning system. For example, the control system 100 may be a plant system such as a manufacturing plant, a power plant, a petrochemical plant, or a water treatment plant. For example, in a plant system, the control device 1 can control the cutting speed so that the cutting amount reaches a target value (final target value) at a target time by setting the equipment or devices constituting the plant system as the controlled device 4. For example, the control device 1 executes a simulation and calculates the optimal start time, optimal setting value, and stage target value of the optimal cutting amount at the optimal stage time of the controlled device 4 by an optimization method. Then, the control device 1 starts the controlled device 4 at the optimal start time and optimal setting value calculated by the optimization method, calculates the cutting speed by a modern control method so that the cutting amount follows the optimal stage target value, and controls the controlled device 4 (here, for example, a cutting machine). In this case, the setting value is, for example, the feed speed, cutting depth, cutting angle, etc. of the cutting machine.

[0075] Also, for example, the control system 100 may be a robot system equipped with a transport robot (for example, a drone). For example, in a robot system, the control device 1 can control devices related to the transport of luggage by the transport robot, including the transport robot, as the control target devices 4, so that the movement amount of the transport robot reaches a target value (final target value) at a target time. For example, the control device 1 executes a simulation and calculates an optimal start time of the control target device 4, an optimal setting value, and an optimal stage target value of the movement amount of the transport robot at the optimal stage time by an optimization method. Then, the control device 1 starts the control target device 4 at the optimal start time and optimal setting value calculated by the optimization method, calculates the movement speed of the transport robot by a modern control method so that the movement amount of the transport robot follows the optimal stage target value, and controls the control target device 4 (here, for example, a motor). In this case, the setting value is, for example, the number of revolutions of a propeller provided on the transport robot, the amount of power supplied to the transport robot, etc.

[0076] In addition, in the above-mentioned first embodiment, the simulator 2 is a device separate from the control device 1, but this is merely an example. For example, the simulator 2 may be provided in the control device 1.

[0077] In the above first embodiment, the model creating device 3 is a device separate from the control device 1, but this is merely an example. For example, the model creating device 3 may be provided in the control device 1.

[0078] For example, both the simulator 2 and the model creating device 3 may be provided in the control device 1.

[0079] In addition, in the above-mentioned first embodiment, the control device 1 and the model creating device 3 are mounted on a server, but this is merely an example. For example, the control device 1 and the model creating device 3 may be mounted on the controlled device 4. Also, for example, in the control device 1, some of the simulation execution instruction unit 11, the simulation result collection unit 12, the optimization unit 13, the optimal control calculation unit 14, the control unit 15, and the data collection unit 16 may be provided in the server, and the rest may be provided in the controlled device 4.

[0080] In the above-described first embodiment, the optimization unit 13 in the control device 1 calculates the optimal start-up time of the controlled device 4 and the optimal setting value of the controlled device 4 using a known optimization algorithm. However, this is merely an example, and the optimization unit 13 may calculate the optimal start-up time of the controlled device 4 and the optimal setting value of the controlled device 4 using other methods. For example, the optimization unit 13 may calculate the optimal start-up time of the controlled device 4 and the optimal setting value of the controlled device 4 using reinforcement learning, which is one of the known machine learning methods that models the relationship between the setting value and the control system and calculates the optimal setting value. However, as a method for calculating the optimal start-up time of the controlled device 4 and the optimal setting value of the controlled device 4, optimization calculation is recommended as described above.

[0081] In the above-described first embodiment, in the control device 1, the optimal control calculation unit 14 calculates the control amount using a known modern control algorithm based on the sensor data collected by the data collection unit 16 so as to follow the optimal stage target value at the optimal stage time calculated by the optimization unit 13. However, this is merely an example, and the optimal control calculation unit 14 may calculate the control amount using, for example, other control algorithms. For example, the optimal control calculation unit 14 may calculate the control amount using a known classical control algorithm. The known classical control algorithm is, for example, a PID control algorithm. In the classical control algorithm, it is possible to control so as to follow a target value, but energy minimization cannot be considered. Therefore, for example, it is also useful in terms of energy minimization to have the optimal control calculation unit 14 perform PID control so as to follow the target value after calculating a stepwise target value that minimizes energy in the optimization calculation.

[0082] As described above, according to the first embodiment, the control device 1 is configured to include an optimization unit 13 that calculates, by optimization calculation, control data including a stage target value, which is a staged target value until the controlled quantity reaches the target value, based on the simulation result indicating the error between the controlled quantity and the target value and the power consumption obtained by causing the simulator unit 21, which executes a simulation to simulately reproduce the operation or behavior of the controlled device 4, an optimal control calculation unit 14 that starts the controlled device 4, collects sensor data on the controlled quantity measured after the control target device 4 is started, and calculates a control quantity for the controlled device 4 based on the collected sensor data so that the controlled quantity follows the stage target value calculated by the optimization unit 13, and a control unit 15 that controls the controlled device 4 with the control quantity calculated by the optimal control calculation unit 14. Therefore, the control device 1 can perform targeted control at the target time and prevent energy loss caused by the controlled amount reaching the target value earlier than the target time. By calculating the stage target value using an optimization technique, the control device 1 can perform control so that the controlled quantity reaches the target value by the target time, and can prevent energy loss caused by the controlled quantity reaching the target value earlier than expected.

[0083] The control data includes the start-up time of the controlled device 4 and the setting value of the controlled device 4, and the optimum control calculation unit 14 starts up the controlled device with the setting value at the start-up time based on the control data. The control device 1 is capable of optimizing the startup of the controlled device 4, including the startup time and setting values ​​of the controlled device 4, which is difficult to achieve with modern control alone.

[0084] In addition, in the control device 1, the optimal control calculation unit 14 calculates the control amount using a system identified model in which the relationship between the control amount and the power consumption and the relationship between the control amount and the controlled amount are modeled, and the system identified model is created using simulation data collected when the simulator unit 21 executes the simulation. The control device 1 can utilize data (simulation data) obtained in the optimization process for modern control. The control device 1 can contribute to shortening the data collection period required for modern control, more specifically, for creating a system identified model required for modern control.

[0085] Furthermore, according to embodiment 1, the control system 100 is configured to include an optimization unit 13 that calculates, by optimization calculation, control data including stage target values, which are staged target values ​​until the controlled quantity reaches the target value, based on simulation results obtained by having a simulator unit 21 that executes a simulation to simulately reproduce the operation or behavior of the controlled device 4 execute a simulation and indicate the error between the controlled quantity and the target value and the power consumption; an optimal control calculation unit 14 that starts up the controlled device 4, collects sensor data on the controlled quantity measured after the control target device 4 is started up, and calculates a control quantity for the controlled device 4 based on the collected sensor data so that the controlled quantity follows the stage target value calculated by the optimization unit 13; a control unit 15 that controls the controlled device 4 with the control quantity calculated by the optimal control calculation unit 14; and the controlled device 4. Therefore, the control system 100 can perform targeted control at the target time and prevent energy loss caused by the controlled amount reaching the target value earlier than the target time. By calculating the stage target value using an optimization technique, the control system 100 can perform control so that the controlled quantity reaches the target value by the target time, and can prevent energy loss caused by the controlled quantity reaching the target value earlier than expected.

[0086] The control data includes the start-up time of the controlled device 4 and the setting value of the controlled device 4, and the optimum control calculation unit 14 starts up the controlled device with the setting value at the start-up time based on the control data. The control system 100 is capable of optimizing the startup of the controlled device 4, including the startup time and setting values ​​of the controlled device 4, which is difficult to achieve with modern control alone.

[0087] Furthermore, in the control system 100, the control device 1 calculates the control amount using a system identified model in which the relationship between the control amount and the power consumption and the relationship between the control amount and the controlled amount are modeled, and the system identified model is created using simulation data collected when the simulator unit 21 executes the simulation. The control system 100 can utilize data (simulation data) obtained in the optimization process for modern control. The control system 100 can contribute to shortening the data collection period required for modern control, more specifically, for creating a system identified model required for modern control.

[0088] Any of the components of the embodiments may be modified or omitted. [Industrial Applicability]

[0089] The control device according to the present disclosure can perform targeted control at a target time and prevent energy loss caused by the controlled amount reaching the target value earlier than the target time. [Explanation of symbols]

[0090] 1 control device, 11 simulation execution instruction unit, 12 simulation result collection unit, 13 optimization unit, 14 optimal control calculation unit, 15 control unit, 16 data collection unit, 2 simulator, 21 simulator unit, 22 data storage unit, 3 model creation device, 31 model creation unit, 32 model storage unit, 4 controlled device, 100 control system, 1001 processing circuit, 1002 input interface device, 1003 output interface device, 1004 processor, 1005 memory.

Claims

1. A control device that controls a control target device to be controlled such that a controlled quantity, which is a measured value aiming at a target value, reaches the target value at a target time, Based on a simulation result indicating an error between the controlled quantity and the target value and power consumption, obtained by causing a simulator unit that executes a simulation that simulates the operation or behavior of the control target device to execute the simulation, an optimization unit that calculates control data including a step target value that is the stepwise target value until the controlled quantity reaches the target value by optimization calculation; An optimal control calculation unit that starts the control target device, collects sensor data related to the controlled quantity measured after the start of the control target device, and calculates a control quantity of the control target device so that the controlled quantity follows the step target value calculated by the optimization unit based on the collected sensor data; A control unit that controls the control target device with the control quantity calculated by the optimal control calculation unit A control device comprising:

2. The control data includes a start time of the control target device and a set value of the control target device, The optimal control calculation unit starts the control target device with the set value at the start time based on the control data The control device according to claim 1, characterized in that:

3. The optimal control calculation unit calculates the control quantity using modern control The control device according to claim 1 or claim 2, characterized in that:

4. The optimal control calculation unit calculates the control quantity using a system identification model in which the relationship between the control quantity and the power consumption and the relationship between the control quantity and the controlled quantity are modeled, The system identification model is created using simulation data collected when the simulator unit executes the simulation The control device according to claim 1, characterized in that:

5. The optimization unit can select multi-objective optimization in which the trade-off between comfort, energy saving, or the power consumption when the measured value and the target value match can be selected in the optimization calculation The control device according to claim 1, characterized in that:

6. A simulator unit that executes the simulation and outputs the simulation result The control device according to claim 1, comprising:

7. A control system comprising the control device according to claim 1 and the control target device Comprising:

8. The control device according to claim 1, The simulator unit that executes the simulation and outputs the simulation result And a control system comprising The device to be controlled .

9. The control system according to claim 7 or claim 8, characterized in that it is an air conditioning system .

10. A control method for controlling a control target device to be controlled such that a controlled variable, which is a measured value aiming at a target value, reaches the target value at a target time, A step of calculating, by an optimization calculation, control data including a step target value, which is a stepwise target value until the controlled variable reaches the target value, based on a simulation result indicating an error between the controlled variable and the target value and power consumption obtained by causing a simulator unit, which executes a simulation that simulates the operation or behavior of the control target device, to execute the simulation; A step of starting the control target device, collecting sensor data regarding the controlled variable measured after the start of the control target device, and calculating a control amount of the control target device such that the controlled variable follows the step target value calculated by the optimization unit based on the collected sensor data; A step of controlling the control target device with the control amount calculated by the optimal control calculation unit And a control method comprising

11. A program for causing a control device that controls a control target device to be controlled such that a controlled variable, which is a measured value aiming at a target value, reaches the target value at a target time, to execute In the control device, Based on a simulation result indicating an error between the controlled variable and the target value and power consumption obtained by causing a simulator unit, which executes a simulation that simulates the operation or behavior of the control target device, to execute the simulation, a process of calculating, by an optimization calculation, control data including a step target value, which is a stepwise target value until the controlled variable reaches the target value; A process of starting the control target device, collecting sensor data regarding the controlled variable measured after the start of the control target device, and calculating a control amount of the control target device such that the controlled variable follows the calculated step target value based on the collected sensor data; A process of controlling the control target device with the calculated control amount And a program for causing the above to be executed.

12. A control device that controls a control target device to be controlled so that a controlled variable reaches a target value at a target time, comprising: a processor that executes a program stored in a memory; The processor: a process of calculating a step target value that is a stepwise value until the controlled variable reaches the target value; a process of collecting measurement data regarding the controlled variable measured after activation of the control target device; a process of calculating a control amount of the control target device so that the controlled variable follows the calculated step target value based on the collected measurement data; a process of controlling the control target device with the calculated control amount A control device characterized by executing the above.

13. The control amount is calculated based on modern control The control device according to claim 12, characterized by the above.

14. The control amount is calculated based on the measurement data to minimize the energy consumption of the control target device and to cause the controlled variable to reach the step target value The control device according to claim 12, characterized by the above.

15. The step target value is calculated by an optimization calculation The control device according to any one of claims 12 to 14, characterized by the above.

16. A control method for controlling a control target device to be controlled so that a controlled variable reaches a target value at a target time, comprising: a step of calculating a step target value that is a stepwise value until the controlled variable reaches the target value; a step of collecting measurement data regarding the controlled variable measured after activation of the control target device; a step of calculating a control amount of the control target device so that the controlled variable follows the calculated step target value based on the collected measurement data; a step of controlling the control target device with the calculated control amount A control method comprising the above.

17. A program for causing a control device that controls a control target device to be controlled so that a controlled variable reaches a target value at a target time to execute, comprising: causing the control device to a process of calculating a step target value that is a stepwise value until the controlled variable reaches the target value; a process of collecting measurement data regarding the controlled variable measured after activation of the control target device; a process of calculating a control amount of the control target device so that the controlled variable follows the calculated step target value based on the collected measurement data; a process of controlling the control target device with the calculated control amount A program for causing [something] to be executed.

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