JP5580518B2 - Optimal control device, optimal control support device, and optimal control method - Google Patents

Description

  The present invention relates to a technique for performing control so that the operation of a device is optimized using a control rule.

  As one of the measures for reducing the amount of CO2 emitted from the home, the spread of a heat pump type water heater that exhibits high environmental performance is expected. In order to widely disseminate heat pump water heaters to homes, high economic efficiency is important in addition to high environmental performance.

  For this reason, a heat pump water heater optimal configuration search tool (hereinafter referred to as optimal configuration search tool) has been developed that proposes an appropriate device configuration that matches the installation conditions with the aim of reducing the cost of introducing and operating a heat pump water heater. Has been. By using this optimum configuration search tool, it is possible to select a combination of a heat pump and a tank that matches the installation conditions of the user. Here, the installation condition represents a user's hot water supply demand and a space that can be used for installing a heat pump type hot water heater.

  In addition, the optimum configuration search tool has a function of optimizing the operating rule parameters, and the operating rule parameters of the heat pump hot water heater can be optimized according to the installation conditions so as to reduce the operating cost. (For example, refer nonpatent literature 1.).

Kenichi Tokoro, Katsumi Hashimoto, Satoshi Shinohara, “Development of optimum configuration search tool for heat pump type hot water heater”, Research Report of Central Research Institute of Electric Power Industry (R06018), 2007

  In the optimum configuration search tool, the operating rule parameters employed in the current heat pump hot water heater are adjusted to optimum values according to the installation conditions. However, the optimum configuration search tool has a problem that the driving rule itself cannot be changed in response to changes in the driving environment. If new operating rules are generated in response to changes in the operating environment, regardless of the current operating rules for equipment, it is expected that the operating costs can be further reduced.

  The present invention has been made to solve the above-described problems caused by the prior art, and is an optimal control device and an optimal control support capable of generating an optimal control rule in response to a change in the operating environment of a hot water heater. An object is to provide an apparatus and an optimal control method.

In order to solve the above-described problems and achieve the object, the present invention collects data related to the operating environment in which the heat pump type hot water heater is operated , detects a change in the operating environment from which the data was collected, and is controlled by a control rule. that the operation of the heat pump water heater and using the collected data to simulate, when detecting the change in the operating environment, the genetic algorithm control rules such as operation of the simulated heat pump water heater is optimized The heat pump type hot water heater is controlled based on the generated control rule.

Further, the present invention is the data collect data relating to the operating environment heat pump water heater is operated to detect the change in the operating environment to collect data were collected operation of the heat pump water heater that is controlled by a control rule When a change in the operating environment is detected, a control rule that optimizes the operation of the simulated heat pump water heater is generated using a genetic algorithm.

  According to the present invention, since a control rule is generated and used for control, there is an effect that optimal control can be performed corresponding to different operating environments.

  Exemplary embodiments of an optimal control device, an optimal control support device, and an optimal control method according to the present invention will be described below in detail with reference to the accompanying drawings.

  First, the configuration of the hot water supply apparatus optimum operation control apparatus according to the present embodiment will be described. FIG. 1 is a functional block diagram illustrating a configuration of the hot water supply apparatus optimum operation control apparatus according to the present embodiment. As shown in the figure, this hot water supply apparatus optimum operation control device 100 includes a calendar information collection unit 101, a calendar information storage unit 102, an operation information collection unit 103, an operation information storage unit 104, and an environment information collection unit 105. The environmental information storage unit 106, the electricity rate receiving unit 107, the electricity rate storage unit 108, the water heater performance information storage unit 109, the environment change detection unit 110, the optimum operation rule generation unit 111, and the simulation unit 112. And an operation rule storage unit 113 and a water heater control unit 114.

  The calendar information collection unit 101 is a processing unit that collects information on the day of the week, date, and time as calendar information at regular time intervals, and stores the collected calendar information in the calendar information storage unit 102. The hot water supply demand varies depending on the season, day of the week, and time zone, as shown in FIG. Therefore, the calendar information collection unit 101 collects calendar information in order to control the water heater in accordance with the hot water supply demand that changes depending on the date and time. The calendar information storage unit 102 is a storage unit that stores the calendar information collected by the calendar information collection unit 101.

  The operation information collection unit 103 is a processing unit that collects information on the amount of hot water supply and the amount of generated hot water from the start of use of the water heater to the present and the amount of hot water in the tank as operation information at regular time intervals. Stored in the operation information storage unit 104. In addition, the operation information collection unit 103 calculates a hot water supply demand at regular intervals from the collected operation information and stores it in the operation information storage unit 104.

  The driving information storage unit 104 is a storage unit that stores the driving information collected by the driving information collection unit 103. In addition, the operation information storage unit 104 stores the hot water supply demand for each fixed time calculated by the operation information collection unit 103.

  The environment information collection unit 105 is a processing unit that collects the current temperature and the incoming water temperature as environment information at regular time intervals, and stores the collected environment information in the environment information storage unit 106. The environment information storage unit 106 is a storage unit that stores the environment information collected by the environment information collection unit 105.

  The electricity rate receiving unit 107 is a processing unit that receives an electricity rate input by a maintenance staff and stores it in the electricity rate storage unit 108 when the electricity rate is revised. The electricity bill storage unit 108 is a storage unit that stores the electricity bill received by the electricity bill accepting unit 107.

  The water heater performance information storage unit 109 is a storage unit that stores hot water supply performance and tank capacity. That is, the water heater performance information storage unit 109 stores data relating to the rated capacity and temperature of the heat pump, the efficiency change due to the difference in the incoming water temperature, the boiling temperature, the capacity of the tank, and the heat dissipation loss.

  The environment change detection unit 110 checks the contents of the calendar information storage unit 102, the operation information storage unit 104, the environment information storage unit 106, and the electricity rate storage unit 108 at regular time intervals, and detects changes in the operating environment of the water heater. When a change in the driving environment is detected, the optimal driving rule generation unit 111 is instructed to update the driving rule.

  Specifically, the environment change detection unit 110 determines that the driving environment has changed when the date stored in the calendar information storage unit 102 reaches a predetermined date. Although the driving environment does not always change even when the predetermined date is reached, the driving environment may change because a certain period of time has passed.

  In addition, the environment change detection unit 110 refers to the operation information storage unit 104, and when the amount of hot water supply per day at a predetermined time exceeds a predetermined threshold value, the environment change detection unit 110 determines that the hot water supply demand has been advanced on that day, and the operating environment is Judge that it has changed.

  In addition, the environment change detection unit 110 refers to the environment information storage unit 106 and determines that the driving environment has changed when the average value of the temperature or the incoming water temperature changes by a predetermined threshold value or more. The environment change detection unit 110 refers to the electricity rate storage unit 108 and determines that the operating environment has changed when the rate system changes.

  The environment change detection unit 110 detects a change in the operating environment of the water heater and instructs the optimum operation rule generation unit 111 to update the operation rule, so that the water heater optimal operation control device 100 responds to a change in the operating environment. Correspondingly, the water heater can be operated by changing the operation rules.

  Optimal operation rule generation unit 111 is a processing unit that generates an operation rule for a water heater based on an instruction from environment change detection unit 110 and updates the operation rule stored in operation rule storage unit 113.

  Here, the operation rule is a set of if-then rules that determine under what conditions the current time, the amount of heat remaining in the tank, and the like are to be turned ON / OFF. Various conditions can be considered for determining ON / OFF of the heat pump, but here, the current date, current time, amount of hot water in the hot water tank (% of remaining calories with respect to the maximum possible heat storage amount), 30 minutes immediately before The hot water supply demand is the condition.

  The reason for the current date as a condition is that there is a seasonal fluctuation in the hot water supply demand, and the reason for the current time as a condition is because there is a fluctuation in the hot water demand due to time zones (Figure 2). The reason for the remaining hot water amount as a condition is that it is considered indispensable to refer to the remaining hot water amount in order to avoid running out of hot water and perform low-cost operation. The reason is that the demand for hot water supply varies over time.

The condition part of the driving rule is represented by a string of eight numbers (FIG. 4). When these numbers are expressed in order by a, b, c, d, e, f, g, and h, the meanings represented by the numbers are as follows.
a: Date b: Modifier specifying whether the current date is before or after a c: Time d: Modifier specifying whether the current time is before or after c e: Remaining hot water amount f: Current remaining hot water amount Modifier specifying whether less than or more than e g: Hot water supply demand for the last 30 minutes h: Modifier specifying whether the hot water supply demand for the last 30 minutes is less than or greater than g

If the interpretation of the conditional sentence meaning a, b, c, d, e, f, g, h is rewritten as a sentence,
“If the current date is earlier than a or later, the current time is earlier than c or later, the remaining hot water amount is e or more and less than or less, and the immediately preceding hot water supply demand is g or more or less or less”
It becomes.

  In addition, a wild card (#: a numerical value that means don't care) can be used for each condition part, so that all four conditions of the current date, current time, amount of remaining hot water, and hot water demand are not satisfied. Action can be applied. This means that, for example, the existence of an if-then rule that focuses only on the amount of remaining hot water, an if-then rule that focuses only on the demand for hot water supply, or the like is allowed.

  In the action part of the driving rule, an action when all the conditions except for the wild card are matched is designated, and the hot water switch ON / OFF is designated. In addition, since the driving rule is composed of a plurality of if-then rules, mutually contradictory if-then rules may be applied simultaneously. Therefore, the action by applying the if-then rule and the hot water switch ON / OFF are associated as follows according to the number of applied ON and OFF.

(1) Number of ON> Number of OFF: Switched to ON (2) Number of OFF> Number of ON: Switched to OFF (3) Number of ON = Number of OFF ≠ 0: Switched to OFF (4) Number of ON = Number of OFF = 0: Switch remains unchanged

  Here, the above-mentioned correspondence is made with emphasis on optimizing the “switching timing” of the switch and generating a driving rule that is turned on only when it is really necessary. This association can also be changed.

For example, instead of (4), (4 ') number of ON = number of OFF = 0: switch to OFF, so that the switch is turned ON or OFF only while a specific condition is satisfied Can do. Further, instead of (3), (3 ′) the number of ONs = the number of OFFs ≠ 0: can be switched to ON.

The optimum operation rule generation unit 111 optimizes the operation rule using a genetic algorithm (GA) so as to reduce the evaluation value = operation cost + number of times of hot water × penalty. Note that the evaluation value may include a CO ₂ emission amount.

  GA is a learning algorithm that imitatively imitates the concept of survival of the right person in Darwin's evolution theory. FIG. 3 shows a conceptual diagram of GA. In GA, a plurality of solution candidates are prepared as chromosomes. Then, an evaluation value by a certain objective function is given to each solution candidate, and the GA operator of crossover and mutation is applied to the chromosome selected as a parent according to the evaluation value, so that it remains in the next generation Generate chromosomes.

  Crossover is an operator that generates a new chromosome by combining fragments of chromosomes selected as parents, and has the effect of improving the evolution rate so that the solution candidate group has a better evaluation in a shorter period of time. A mutation is an operator that changes a part of a chromosome from an original value to another value in a probabilistic manner, and has an effect of preventing a solution candidate group from falling into a local optimum solution. In FIG. 3, a chromosome 123 is generated by the intersection of the chromosome 121 and the chromosome 122, and a chromosome 125 is generated by the mutation of the chromosome 124.

  A solution having a higher evaluation value is generated by repeating a series of procedures of evaluation, selection, crossover, and mutation.

  FIG. 4 is a diagram showing chromosomes used for optimizing driving rules. As shown in the figure, each chromosome is composed of a plurality of if-then rules. In addition, the number of individuals included in one generation was set to 100, and it was ended when 1000 generations were changed. In addition, the number of elite was set to 2 by using an “elite strategy” in which the individual with the best evaluation in each generation was directly passed on to the next generation.

  In addition, the driving rule is composed of 10 types of if-then rules, the crossover probability is 95%, and the mutation rate is 2.4%. Further, the values handled by the numerical values a, c, e, and g in the condition part are 0-24, and 24 is a wild card. As a result, the date information can be handled by dividing the year into 24 periods in which the month is divided into the first half and the second half, and the time information can be handled in units of one hour.

  The penalty was 10,000 yen, two-point crossover was used, and tournament selection was used for individual selection. In addition, if the initial individual is generated completely at random, an operation rule that does not boil hot water even though hot water has run out occurs, resulting in an extreme difference in fitness and initial convergence. It becomes easy to fall into. Therefore, the initial individual includes an if-then rule in which the date, time, amount of remaining hot water, and immediately preceding hot water supply demand are all wild cards and the action part is ON.

  Furthermore, if there is a late-night charge period where the unit price of electricity is cheaper than other periods, the late-night charge period will end in consideration of the fact that the operation cost can be reduced if the boiling is completed by then. Include an if-then rule to turn the switch off at the time. A rule indicating such behavior is automatically obtained in most cases even if calculation is performed without including it in the initial individual, but rule search efficiency can be improved by including it in the initial individual in advance. By introducing the above if-then rule, it is possible to increase the proportion of feasible solutions included in the initial individual, and to stably obtain an operation rule with low cost and low risk of running out of hot water.

  Returning to FIG. 1, the simulation unit 112 simulates the heat pump type hot water heater based on the information and operation rules of the calendar information storage unit 102, the operation information storage unit 104, the environment information storage unit 106, and the electricity rate storage unit 108. It is a processing unit. This simulation unit 112 is used by the optimum operation rule generation unit 111 to evaluate each chromosome (operation rule), and the operation cost and the number of hot water runs when the water heater is operated with each chromosome, and ON / OFF of the switch. A change in the situation and the amount of remaining hot water is calculated and provided to the optimum operation rule generation unit 111.

  The driving rule storage unit 113 is a storage unit that stores the driving rules optimized by the optimal driving rule generation unit 111. The water heater controller 114 controls ON / OFF of the water heater based on the operation rules stored in the operation rule storage unit 113, the current date and time stored in the calendar information storage unit 102, and the hot water supply demand stored in the operation information storage unit 104. It is a control part. The water heater controller 114 controls the water heater based on the operation rules generated by the optimum operation rule generator 111 in response to changes in the operating environment, thereby reducing operating costs.

  Next, the operation rule update process by the hot water supply apparatus optimum operation control apparatus 100 according to the present embodiment will be described. FIG. 5 is a flowchart illustrating the processing procedure of the operation rule update process performed by the hot water supply apparatus optimum operation control apparatus 100 according to the present embodiment.

  As shown in the figure, in this driving rule update process, the environment change detection unit 110 includes a calendar information storage unit 102, a driving information storage unit 104, an environmental information storage unit 106, and an electricity bill storage unit 108 at regular time intervals. The contents are checked, and it is determined whether or not the operating environment of the water heater has changed (step S1). As a result, when there is no change in the operating environment of the water heater, the operation is not updated, so the process ends.

  On the other hand, when there is a change in the operating environment of the water heater, the optimal operation rule generation unit 111 generates an optimal operation rule using the GA (step S2). That is, the optimal operation rule generation unit 111 performs chromosome optimization by repeatedly performing a series of procedures such as chromosome selection, crossover, and mutation while evaluating a plurality of chromosomes using the simulation unit 112, that is, Optimize driving rules.

  Then, the optimum driving rule generation unit 111 sets the optimized driving rule in the driving rule storage unit 113 (step S3). Thus, by detecting a change in the operating environment of the water heater and updating the operation rule, the water heater can be operated at a low cost even when the operating environment changes.

  Next, the evaluation result by simulation of the water heater optimum operation control apparatus 100 according to the present embodiment will be described. Here, the annual operating cost is calculated when the driving rule is optimized only once and the driving rule is optimized and when the parameters are optimized using the optimum configuration search tool of Non-Patent Document 1. Evaluation was made by comparison.

  In the evaluation, the hot water supply demand for one year measured at intervals of 30 minutes in an actual home was used. FIG. 6 shows the average demand for each time in the summer (July to September), winter (January to March, December), and intermediate period (April to June, October to November). Moreover, the data shown in FIG. 7 was used as the electricity bill unit price, and the data shown in FIG. 8 was used as the monthly temperature and the incoming water temperature. In addition, as a device configuration, a heat pump type hot water heater having a rated heating capacity of 4.5 KW and a tank having a capacity of 300 liters were used.

  FIG. 9 is a diagram illustrating the driving rules generated by the optimal driving rule generation unit 111. As shown in the figure, here, three rules for turning on the water heater and seven rules for turning off the water heater are generated.

  FIG. 10 is an example of a simulation result when the operation rule of FIG. 9 is executed and a change in the amount of remaining hot water accompanying the simulation result. FIG. 11 illustrates when the if-then rule of FIG. 9 is applied during one day.

  During the midnight hours, the switch ON that continues for one day according to rule 1 and the switch ON that operates according to rule 10 that works like “boiling hot water before noon” are switched off until 2 o'clock according to rule 9. However, the water is boiled by finely controlling it to meet the demand for hot water supply in the early morning. Thereafter, the switch OFF from 7 o'clock to 24 o'clock according to rule 2 is suppressed to realize hot water storage until 7 o'clock in the morning, thereby satisfying all hot water supply demands in the evening. The switch is turned on again by rule 4 at 22:00, and the switch is turned off by rule 7 at 2:30.

  Rule 4 is a “rule that serves as a fail-safe” to turn on the switch when the amount of remaining hot water decreases after 9:00 pm to avoid running out of hot water, while rule 7 sets the amount of remaining hot water after 8:00 pm. This is an “if-then rule that encourages power saving” in which the switch is turned off if there is room. In addition, in the used hot water supply demand data, almost all of the hot water generated in 30 minutes falls below 9200 kcal, so the conditions for hot water supply for 30 minutes immediately before rule 7 are satisfied in most cases. It works almost the same.

As described above, when various if-then rules operate cooperatively, a highly flexible operation having the following characteristics can be realized.
・ Store hot water as much as possible by 7:00 when the electricity unit price is cheap.
・ Boil the hot water after 7 o'clock only when the hot water stored by 7 o'clock is not enough.
・ Boiling hot water in response to the demand for hot water supply in the winter late at night. In summer when there is little demand for hot water supply, boiling is automatically completed before 7 o'clock.
・ Generate criteria for determining what conditions are "cannot be satisfied" according to hot water supply demand.

  FIG. 12 is a diagram illustrating a start / stop state of the water heater. FIG. 12A illustrates a case where the operation rule is optimized, and FIG. 12B illustrates a conventional case where the parameters are optimized. FIG. 13 is a diagram showing the results of comparing the annual operating cost and the number of hot water runs (numbers in parentheses) between the case where the operation rule is optimized and the conventional case where the parameters are optimized.

  Comparing FIG. 12 (a) and FIG. 12 (b), when the operation rule is optimized, the start before 23:00 when the unit price of electricity is high decreases, and the start / stop pattern changes greatly according to the season. ing. As a result, as shown in FIG. 13, when the operation rule is optimized, it is possible to reduce the operation cost and eliminate hot water shortage. FIG. 13 also shows the simulation results when the tank capacity is 370 liters. Further, in FIG. 13, the high demand case is a case where the hot water supply demand in the demand standard case is multiplied by 1.2.

  As described above, in this embodiment, the optimal operation rule generation unit 111 generates the operation rule using the GA while simulating the hot water heater by the simulation unit 112. Based on this, an optimal driving rule can be generated. Accordingly, it is possible to reduce the annual operating cost and eliminate hot water shortage.

  In this embodiment, the environment change detection unit 110 detects a change in the operating environment of the water heater, and when the operation environment changes, the optimum operation rule generation unit 111 generates the operation rule. The operation of the water heater can be optimized in response to changes.

  Further, in the present embodiment, since hot water is boiled based on an operation rule that takes into account the season and time zone, the hot water heater can be optimally operated in response to changes in hot water supply demand.

  By the way, although the said Example demonstrated the case where the water heater optimal operation control apparatus 100 performed the production | generation of an operation rule, the production | generation of an operation rule was performed with the apparatus different from the apparatus which controls a water heater, and with another apparatus. The generated operation rule can be stored and controlled by the controller of the water heater. Therefore, a case will be described in which generation of operation rules and control of a water heater are performed by different devices.

  FIG. 14 is a functional block diagram showing a configuration of a water heater device 210 that controls a water heater and an external PC 220 that generates a driving rule when generating the operation rule and controlling the water heater using different devices. . Here, for convenience of explanation, functional units that play the same functions as the respective units shown in FIG.

  As shown in FIG. 14, the water heater performance information storage unit 109, the optimum operation rule generation unit 111, and the simulation unit 112 of the water heater optimum operation control device 100 are removed from the water heater device 210. In addition, the water heater apparatus 210 newly includes an environment change report unit 211 and a water heater / PC interface unit 212. The external PC 220 includes a water heater performance information storage unit 109, an optimum operation rule generation unit 111, and a simulation unit 112.

  The environment change reporting unit 211 is a processing unit that displays a change in the operating environment when the environment change detecting unit 110 detects a change in the operating environment of the water heater. The external PC 220 receives calendar information, operation information, environmental information, and electricity charges from the water heater device 210 via the water heater / PC interface unit 212, generates an optimum operation rule, and operates the operation rule storage unit 113 of the water heater device 210. Set to.

  When the change in the operating environment is displayed by the environment change report unit 211, the user contacts the maintenance staff of the water heater, and the maintenance staff uses the external PC 220 to generate a new operation rule corresponding to the change in the operating environment. And set in the driving rule storage unit 113.

  In this way, by generating the operation rule with another device, the cost of the water heater device 210 can be reduced, and the operation rule corresponding to the change in the operating environment can be set in the water heater device 210 as necessary. it can.

  In addition, although the present Example demonstrated the control apparatus of the water heater, this invention is not limited to this, It can apply similarly to the apparatus which controls the driving | operation of another apparatus. For example, the present invention can be similarly applied to a control device such as a district heat supply device that generates and stores heat supplied to a region, or a device that stores power in a battery used in an electric vehicle.

  As described above, the optimum control device, the optimum control support device, and the optimum control method according to the present invention are useful for an apparatus for accumulating and supplying energy such as a water heater.

It is a functional block diagram which shows the structure of the water heater optimal operation control apparatus which concerns on a present Example. It is a figure which shows the hot water supply demand. It is a conceptual diagram of GA. It is a figure which shows the chromosome used in order to optimize a driving rule. It is a flowchart which shows the process sequence of the operation rule update process by the hot water supply apparatus optimal operation control apparatus which concerns on a present Example. It is a figure which shows the average amount of hot water supply of each time of summer, winter, and an intermediate period. It is a figure which shows an electricity bill unit price. It is a figure which shows the temperature and incoming water temperature of each month. It is a figure which shows the driving | running rule produced | generated by the optimal driving | running rule production | generation part. It is a figure which shows the simulation result when the driving | running rule of FIG. 9 is performed, and the change of the amount of remaining hot water accompanying it. It is a figure which shows when the if-then rule of FIG. 9 is applied within one day. It is a figure which shows the starting stop state of a water heater. It is a figure which shows an annual operating cost and the frequency | count of hot water run-out. It is a functional block diagram which shows the structure of a water heater apparatus and external PC.

Explanation of symbols

DESCRIPTION OF SYMBOLS 100 Water heater optimal operation control apparatus 101 Calendar information collection part 102 Calendar information storage part 103 Operation information collection part 104 Operation information storage part 105 Environmental information collection part 106 Environmental information storage part 107 Electricity charge reception part 108 Electricity charge storage part 109 Water heater Performance information storage unit 110 Environmental change detection unit 111 Optimal operation rule generation unit 112 Simulation unit 113 Operation rule storage unit 114 Water heater control units 121 to 125 Chromosome (operation rule)
210 Water heater device 211 Environmental change report unit 212 Water heater / PC interface unit 220 External PC

Claims (6)

An optimal control device for controlling a heat pump type water heater so that the operation is optimal using a control rule,
Operating environment data collection means for collecting data on the operating environment in which the heat pump water heater is operated;
Driving environment change detecting means for detecting changes in the driving environment for which data has been collected by the driving environment data collecting means;
Simulation means for simulating the operation of the heat pump water heater controlled by the control rule using data collected by the operating environment data collection means;
Control that uses a genetic algorithm to generate a control rule that optimizes the operation of the heat pump water heater simulated by the simulation means when a change in the operating environment is detected by the operating environment change detection means Rule generation means;
And an equipment control means for controlling the heat pump type hot water heater based on the control rule generated by the control rule generation means. The optimal control apparatus according to claim 1 , wherein the control rule enables optimal control of equipment corresponding to seasonal demand changes. The optimal control according to claim 1 , wherein the control rule is a collection of if-then rules, and the if part is a combination of a date, a time zone, a remaining amount of hot water in a tank, and a condition of a demand for hot water supply immediately before. apparatus. The control rule is a collection of if-then rules, and the action specified in the then part is activation or deactivation of the heat pump. When a plurality of competing if-then rules are established, the number of established rules is large. The optimal control device according to claim 1 , wherein an action is selected. An optimal control method by an optimal control device that controls a heat pump type water heater so that the operation is optimized using a control rule,
An operating environment data collection step for collecting data on the operating environment in which the heat pump water heater is operated;
A driving environment change detection step for detecting a change in the driving environment in which data is collected by the driving environment data collection step;
A simulation step of simulating the operation of the heat pump water heater controlled by the control rule using the data collected by the operating environment data collection step;
Control that uses a genetic algorithm to generate a control rule that optimizes the operation of the heat pump water heater simulated by the simulation step when a change in the operating environment is detected by the operating environment change detection step A rule generation step;
And an equipment control step of controlling the heat pump type hot water heater based on the control rule generated by the control rule generation step. An optimal control support device that supports optimization of a control device that controls a heat pump type water heater using a control rule,
Operating environment data collection means for collecting data on the operating environment in which the heat pump water heater is operated;
Driving environment change detecting means for detecting changes in the driving environment for which data has been collected by the driving environment data collecting means;
Simulation means for simulating the operation of the heat pump water heater controlled by the control rule using data collected by the operating environment data collection means ;
Control that uses a genetic algorithm to generate a control rule that optimizes the operation of the heat pump water heater simulated by the simulation means when a change in the operating environment is detected by the operating environment change detection means An optimum control support device comprising a rule generation means.

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