Energy amount calculation method and energy amount calculation program

Abstract

This energy amount calculation method comprises: acquiring information on an operation period in which a control device for executing control to achieve a first target value operates, and information on an installation area of the control device; acquiring weather information corresponding to the operation period and the installation area; receiving settings for a control strategy of the control and weather conditions under which the control device operates and executing a control simulation for the control device to achieve the first target value with respect to respective scenarios based on the weather conditions and the control strategy of the control; and, by using the control simulation, estimating and outputting an energy amount required for achieving the first target value.

Description

Energy amount calculation method and energy amount calculation program

[0001] The present disclosure relates to an energy amount calculation method and an energy amount calculation program.

[0002] Patent Document 1 discloses a control device that controls a controlled device as a control target such that a controlled variable, which is a measured value aiming for a target value, reaches the target value at a target time. The control device causes a simulator unit that executes a simulation for reproducing the operation or behavior of the controlled device in a simulated manner to execute the simulation, and based on a simulation result indicating an error between the controlled variable and the target value and power consumption obtained thereby, an optimization unit that calculates control data including step target values, which are stepwise target values until the controlled variable reaches the target value, through optimization calculation; an optimal control calculation unit that activates the controlled device, collects sensor data relating to the controlled variable measured after activation of the controlled device, and calculates a control amount for the controlled device such that the controlled variable follows the step target value calculated by the optimization unit based on the collected sensor data; and a control unit that controls the controlled device with the control amount calculated by the optimal control calculation unit.

[0003] Japanese Patent No. 7446546 Publication

[0004] Conventionally, in formulating business plans, there has been a demand for a technology that estimates the amount of energy required for medium- to long-term air conditioning control and estimates air conditioning costs more accurately. However, in air conditioning control, the temperature that is the object of air conditioning changes depending on actual weather conditions (i.e., air temperature). Therefore, with conventional control devices, it has been difficult to estimate with high accuracy the control and energy amount required for adjusting the temperature that people find comfortable (the target value).

[0005] The present disclosure has been devised in view of the above-described conventional circumstances, and an object thereof is to provide an energy amount calculation method and an energy amount calculation program that estimate, with higher accuracy, the amount of energy required for a device to achieve a target value in a control plan for the device.

[0006] This disclosure provides a method for calculating the amount of energy performed by a computer to estimate the amount of energy required for control to achieve a first target value by a control device, the method comprising: acquiring information on the operating period during which the control device is in operation and information on the installation area of ​​the control device; acquiring weather information corresponding to the operating period and the installation area; accepting settings for the weather conditions under which the control device is in operation and the control strategy of the control device; executing a control simulation for the control device to achieve the first target value for each scenario based on the weather conditions and the control strategy of the control device; and estimating and outputting the amount of energy based on the control simulation.

[0007] Furthermore, this disclosure provides an energy calculation program that is executed by at least one processor, and which includes the steps of: acquiring information on the operating period and location of a control device that performs control to achieve a first target value; acquiring weather information corresponding to the operating period and location; receiving settings for weather conditions under which the control device operates and a control strategy for the control device, and for each scenario based on the weather conditions and the control strategy for the control device, executing a control simulation for the control device to achieve the first target value; and estimating and outputting the amount of energy required to achieve the first target value based on the control simulation.

[0008] According to this disclosure, the amount of energy required for the device to achieve a target value can be estimated with greater accuracy in the control plan of the device.

[0009] Block diagram showing an example of the internal configuration of the simulation device according to the embodiment. Flowchart showing an example of the simulation procedure of the simulation device according to the embodiment. Diagram showing an example of a target space that is subject to air conditioning control. Diagram showing an example of a temperature profile. Diagram showing an example of a control strategy. Diagram showing an example of the comfort distribution of a target space that is subject to air conditioning control. Flowchart showing an example of the air conditioning control simulation procedure for each day of the simulation device according to the embodiment. Diagram explaining an example of determining air conditioning control conditions (control points) by one-stage control. Diagram explaining an example of determining air conditioning control conditions (control points) by two-stage control. Diagram showing a comparative example of the air conditioning control process.

[0010] The following describes in detail each embodiment that specifically discloses the energy calculation method and the configuration and operation of the energy calculation program related to this disclosure, with reference to the drawings as appropriate. However, unnecessarily detailed explanations may be omitted. For example, detailed explanations of already well-known matters and redundant explanations of substantially identical configurations may be omitted. This is to avoid the following explanation becoming unnecessarily verbose and to facilitate understanding by those skilled in the art. The attached drawings and the following explanation are provided to enable those skilled in the art to fully understand this disclosure and are not intended to limit the subject matter described in the claims.

[0011] Referring to Figure 1, an example of the internal configuration of the simulation device P1 will be described. Figure 1 is a block diagram showing an example of the internal configuration of the simulation device P1 according to an embodiment.

[0012] The simulation device P1 operates an air conditioner for a predetermined period to adjust the temperature of a space to a predetermined temperature and performs an air conditioning control simulation to estimate the amount of energy required to adjust the temperature of the space for that period. The simulation device P1 can accept operations from a user requesting the estimation of energy quantity and may be implemented by, for example, a Personal Computer (hereinafter referred to as "PC"), a notebook PC, etc. Alternatively, the simulation device P1 may be implemented by an on-premise server or cloud server that is connected to an external device (not shown) capable of accepting user input operations and is capable of data communication.

[0013] The simulation device P1 includes a communication unit 10, a processor 11, a memory 12, an input unit 13, and a monitor 14. Note that the simulation device P1 shown in Figure 1 is an example and is not limited thereto. For example, if the simulation device P1 is implemented by an on-premise server or a cloud server, the input unit 13 and the monitor 14 may be omitted.

[0014] The communication unit 10 is connected to a network (not shown) via wireless or wired communication, and acquires various information (e.g., temperature profile) necessary for air conditioning control simulation from an external device (not shown). The communication unit 10 outputs the acquired information necessary for air conditioning control simulation to the processor 11.

[0015] The wireless communication referred to herein includes, for example, short-range wireless communication such as Bluetooth® and NFC®, or communication via wireless Local Area Network (LAN) such as Wi-Fi®.

[0016] The processor 11 is configured using, for example, a Central Processing Unit (CPU), a Field Programmable Gate Array (FPGA), or a Graphics Processing Unit (GPU), and works in cooperation with the memory 12 to perform various processes and controls. Specifically, the processor 11 refers to the programs and data held in the memory 12 and executes those programs to realize the functions of the simulation device P1.

[0017] Memory 12 includes, for example, Random Access Memory (RAM) as work memory used when executing each process of the processor 11, and Read Only Memory (ROM) which stores programs and data that define the operation of the processor 11. Data or information generated or acquired by the processor 11 is temporarily stored in RAM. Programs that define the operation of the processor 11 are written in ROM.

[0018] The input unit 13 is a user interface configured using, for example, a touch panel, keyboard, or mouse. The input unit 13 converts the received user operations into electrical signals (control commands) and outputs them to the processor 11. The input unit 13 may also be a touch panel integrated with the monitor 14.

[0019] The monitor 14 is configured using a display such as a Liquid Crystal Display (LCD) or an Organic Electroluminescence (EL). The monitor 14 displays information regarding the estimated amount of energy required for air conditioning control output from the processor 11.

[0020] <Simulation Procedure Example> Next, an example of the simulation procedure will be described with reference to Figures 2 to 6. Figure 2 is a flowchart showing an example of the simulation procedure of the simulation device P1 according to the embodiment. Figure 3 is a diagram showing an example of a target space MP that is subject to air conditioning control. Figure 4 is a diagram showing an example of temperature profiles Dt11, Dt12, and Dt13. Figure 5 is a diagram showing an example of a control strategy. Figure 6 is a diagram showing an example of the comfort distribution Cd of the target space MP that is subject to air conditioning control. Note that the temperature profiles Dt11 to Dt13 shown in Figure 4 and the control strategy shown in Figure 5 are just examples and are not limited thereto.

[0021] The processor 11 accepts and sets input information from the user regarding the target space MP, which is the subject of the air conditioning control simulation, and information regarding the period for which the air conditioning control simulation will be performed (St11). Specifically, the processor 11 accepts input information regarding the target space MP, such as data showing the three-dimensional shape of the target space MP, for example, CAD data or a 3D model, and input information regarding the location of the target space MP or the region or district to which the target space MP corresponds. Furthermore, the processor 11 may also accept input information such as the installation location, number of units, or model number of the air conditioners that control the temperature of the target space MP.

[0022] The processor 11 collects reference data corresponding to the set target period (St12). The reference data here refers to data that may affect the energy consumption required for air conditioning control or the business plan, such as weather data, weather (temperature) prediction probability data, or social background data related to energy.

[0023] The weather data referred to here is data showing daily temperature changes collected in the region corresponding to the target space MP and during the same period as the target period. For example, if the location (region) where the target space MP exists is "Osaka" and the target period is "January 1st to January 31st", the processor 11 will collect 25 years' worth of past weather data collected in the region "Osaka" during the period "January 1st to January 31st", from 2000 to 2024.

[0024] The predicted probability data is predicted data that predicts the temperature trend or the probability of a trend for a location (region) corresponding to the target space MP. The temperature trend may be information that shows a trend based on comparisons such as being similar to a certain year, normal, above average, or below average, or it may be information that shows a general trend such as a heatwave or cool summer, a warm winter, or a severe cold. For example, the processor 11 obtains information that the temperature trend for "Osaka" from "January 1st to January 31st" in the future is that there is a 60% probability that it will be normal, a 30% probability that it will be above average, and a 10% probability that it will be below average.

[0025] Furthermore, social background data is data that can influence the estimation of the amount of energy required for air conditioning, or the formulation of business plans based on the estimated amount of energy, and includes, for example, data on carbon neutrality policies, the status of electricity supply, or electricity rates. It should be noted that social background data is not limited to the examples mentioned above and may be data selected or chosen by the user. For example, processor 11 may acquire information predicting power shortages during the target period, notices of electricity rate increases, or information on changes to electricity rate plans.

[0026] The processor 11 generates one or more temperature profiles based on temperature data for locations corresponding to the target spatial MP during the target period, based on the collected historical weather data. Examples of temperature profiles Dt11, Dt12, and Dt13 will be described below with reference to Figure 4.

[0027] For example, temperature profile Dt11 is a historically based temperature profile that shows the hourly changes in the average temperature, minimum temperature, or maximum temperature for each day of the target period, based on temperature data collected in the past.

[0028] For example, temperature profile Dt12 is a historically based temperature profile that shows the hourly change in the average temperature (μ), average temperature (μ) + (standard deviation (+σ) × coefficient (α)), or average temperature (μ) + (standard deviation (-σ) × coefficient (α)) for each day of the target period, based on temperature data collected in the past. Here, 68% of the data (actual temperature values) are distributed within the range from the average temperature (μ) to the standard deviations (+σ) and (-σ). The coefficient (α) can be any value between 0 (zero) and 3.0, for example. The temperature at each time point shown in temperature profile Dt12 is a Gaussian distribution generated using temperature data for the target period or the number of years for which data has been collected.

[0029] Furthermore, for example, the temperature profile Dt13 is a prediction-based temperature profile that shows the predicted temperature change for each day of the target period every hour, based on past temperature data collected during the same period as the target period and prediction data that predicts the probability of each trend (average, above average, or below average, etc.) for the temperature of the location (region) corresponding to the target space MP. The processor 11 predicts the temperature for the target period based on past temperature data collected during the same period as the target period and the probability of each temperature trend (average, above average, or below average, etc.) occurring. Based on the distribution of the predicted temperature (data) occurrence probabilities, the processor 11 generates a temperature profile Dt13 that shows the change in mean temperature (μ), mean temperature (μ) + standard deviation (+σ), or mean temperature (μ) + standard deviation (-σ) every hour. In addition, the temperature profile Dt13 shown in Figure 4 is associated with information that there is a 60% probability that the temperature during the target period will be average, a 30% probability that it will be above average, and a 10% probability that it will be below average.

[0030] The processor 11 accepts a selection operation from the one or more generated temperature profiles to be used in the execution scenario of the air conditioning control simulation, or to select one or more temperature graphs included in the temperature profile (St13).

[0031] For example, the processor 11 may accept a selection operation between temperature profile Dt11 from temperature profiles Dt11 to Dt13 and the temperature graph of temperature profile Dt13 for cases where the temperature is above average. By using temperature profile Dt11, the processor 11 can obtain air conditioning control simulation results for when the temperature during the target period changes from the historical average temperature, maximum temperature, and minimum temperature. Furthermore, by using the temperature graph for cases where the temperature is above average, the processor 11 can obtain air conditioning control simulation results for when the temperature during the target period is above average.

[0032] Furthermore, the processor 11 accepts setting operations regarding the responsiveness and tracking ability of the air conditioning control as at least one control strategy for the air conditioning control based on user operation (St13). The setting operation regarding the responsiveness of the air conditioning control may be, for example, an operation to select words indicating the degree of responsiveness such as comfortable, fast, energy-saving, or slow, or an input operation for the target arrival time Tm0, which will be described later. Similarly, the setting operation regarding the tracking ability of the air conditioning control may be an operation to select words indicating the degree of tracking ability such as comfortable, high, energy-saving, or low, or an input operation for the target error ΔTm, which will be described later.

[0033] For example, the target graph Gp0 is a graph that shows the ideal temperature change at the timing requested by the user, where the sensor temperature Tp1 before air conditioning switches to the target sensor temperature Tp0 after air conditioning.

[0034] Control strategy graph Gp02 shows the temperature change when a control strategy with improved responsiveness and tracking is adopted. Conversely, control strategy graph Gp01 shows the temperature change when a control strategy with reduced responsiveness and tracking is adopted. In other words, control strategy graph Gp02 is a control strategy that consumes more energy than control strategy graph Gp01.

[0035] Here, responsiveness refers to the length of the target arrival time Tm0, which is the time it takes to raise the sensor temperature Tp1 to the target sensor temperature Tp0. The target arrival time Tm0 decreases as responsiveness increases and increases as responsiveness decreases. Furthermore, tracking performance refers to the target error ΔTm between the temperature during air conditioning control (e.g., sensor temperature Tp2) and the target sensor temperature Tp0, and is the allowable error. The target error ΔTm decreases as tracking performance increases and increases as responsiveness decreases.

[0036] The processor 11 generates a scenario for running at least one air conditioning control simulation based on one or more temperature profiles and one or more control strategies selected by the user. The processor 11 generates a temperature graph for each scenario (St14) based on the temperature profile corresponding to the generated scenario. For example, if temperature profile Dt11 is selected, the processor 11 generates a temperature graph for the maximum temperature shown by temperature profile Dt11, a temperature graph for the average temperature shown by temperature profile Dt11, and a temperature graph for the minimum temperature shown by temperature profile Dt11.

[0037] The processor 11 selects one of the generated temperature graphs and, if the temperature change indicated by the selected temperature graph occurs, executes an air conditioning control simulation to adjust the temperature of the target space MP to the target temperature, for each day included in the target period (St15). The procedure for executing the air conditioning control simulation in step St15 will be explained in detail in the explanation of Figure 7.

[0038] The processor 11 calculates several indicators on a daily basis as simulation results of the air conditioning control simulation, including the comfort distribution of the target space MP, the amount of energy consumption required for air conditioning, and Pulse Width Modulation (hereinafter referred to as "PWM") (St16).

[0039] The comfort distribution Cd referred to here is data that maps the degree of comfort a person feels in the target space MP to each location. For example, the comfort distribution Cd shows the comfort level at a height of 1.4m for a person. The processor 11 generates the comfort distribution Cd based on the temperature difference between the target sensor temperature and the temperature shown by the temperature distribution of the target space MP obtained by the air conditioning control simulation. In the comfort distribution Cd, the smaller the absolute difference between the target sensor temperature Tp0 and the temperature shown by the temperature distribution, the better (higher) the comfort level, and the larger the absolute difference, the worse (lower) the comfort level. For example, the comfort distribution Cd shown in Figure 6 visualizes that the comfort level is highest at location Ep1 and lowest at locations Ep2 and Ep3.

[0040] The processor 11 determines whether or not the air conditioning control simulation for the target period has been completed (St17).

[0041] If the processor 11 determines that the air conditioning control simulation for the target period is complete (St17, YES), it sums up the multiple indicators calculated for each day for each scenario of the target period and outputs the total value of each of the multiple indicators for each scenario during the target period (St18).

[0042] On the other hand, if the processor 11 determines that the air conditioning control simulation for the target period is not yet complete (St17, NO), it returns to step St14.

[0043] As described above, the simulation device P1 in the embodiment can calculate with greater accuracy the amount of energy consumption required to adjust the target space to a comfortable temperature (target sensor temperature) during the target period, based on a temperature profile and control strategy derived from actual weather data (temperature data).

[0044] Furthermore, the simulation apparatus P1 according to the embodiment can set at least one scenario in which an air conditioning control simulation is executed. The simulation apparatus P1 can output the energy consumption required to adjust the target space to a comfortable temperature (target sensor temperature) during the target period for each set scenario. This enables the user to formulate a business plan that considers various scenarios based on the energy consumption output for each scenario.

[0045] <Air Conditioning Control Simulation Method> Next, an example of a control simulation method performed by the simulation apparatus P1 will be described with reference to FIG. 7. FIG. 7 is a flowchart illustrating an example of a daily air conditioning control simulation procedure of the simulation apparatus P1 according to the embodiment.

[0046] In the following description of the air conditioning control simulation, as an example, it is assumed that an interval in which air conditioning control is performed under predetermined air conditioning control conditions (hereinafter referred to as a "control interval") is 5 minutes, and an example of determining air conditioning control conditions for each control interval will be described. Note that the duration of one control interval is not limited to 5 minutes, and may be 10 minutes or 15 minutes. Furthermore, in the description of the air conditioning control simulation, in order to determine the air conditioning control conditions to be executed in one control interval, an example will be described in which setting of air conditioning control conditions for each of four control intervals (=20 minutes) including this one control interval, and execution of an air conditioning control simulation for the four control intervals based on the set air conditioning conditions are performed, but the present invention is not limited to this. The number of control intervals to be simulated for determining the air conditioning control conditions for one control interval only needs to be three or more.

[0047] The processor 11 divides the operating time zone in which the air conditioner operates in one day into intervals each having a predetermined duration (for example, 5 minutes, 10 minutes, or the like), and sets each control interval (St101).

[0048] The processor 11 sets a plurality of positions, which are specified when setting the target space MP and at which people stay within the target space MP, as evaluation points where temperature or comfort adjusted by air conditioning control is evaluated. The processor 11 sets a target sensor temperature Ts4 as a target temperature to be achieved for each of the plurality of set evaluation points (St102). The sensor temperature referred to herein is an indoor temperature measured by a temperature sensor provided in an air conditioner.

[0049] Note that the evaluation point in the present disclosure is described as an example of a position where a person stays, but the present disclosure is not limited thereto. The evaluation point may be a position where a user desires to adjust the temperature in the target space. In addition, the target sensor temperature Ts4 set herein is a sensor temperature as an achievement target for control sections 1 to 4, and is a temperature different from a final target temperature (e.g., the target sensor temperature Tp0 shown in FIG. 5). The target sensor temperature Ts4 may be set based on a control strategy, that is, responsiveness and followability.

[0050] The processor 11 selects four control sections 1 to 4 for which air conditioning control conditions have not been determined among the set control sections in chronological order. Based on the set target sensor temperature Ts4, the processor 11 sets an air conditioning control condition 4 for the last control section 4 among the four selected control sections 1 to 4 (St103). Specifically, using an air conditioning control condition calculation model, when the control section 4 among the control sections 1 to 4 operates in a steady state, the processor 11 calculates the air conditioning control condition 4 (that is, the control point Pt3 shown in FIG. 10) for the control section 4 at which the sensor temperature measured by the temperature sensor of the air conditioner becomes the target sensor temperature Ts4 (St103). The air conditioning control condition calculation model referred to herein is a trained model capable of calculating air conditioning control conditions for bringing each evaluation point to the target sensor temperature Ts4 based on the target sensor temperature Ts4. In addition, the air conditioning control conditions are, for example, the temperature and air volume of air blown from an outlet of the air conditioner.

[0051] Based on the target sensor temperature Ts4 and the air conditioning control conditions 4 for the control section 4, the processor 11 sets the target total energy amount ES required to bring the temperature of each of the multiple evaluation points to the target sensor temperature Ts4 by controlling the air conditioning in the control sections 1 to 4 (St104).

[0052] The processor 11 sets the air conditioning control conditions 1 to 3 for each of the control sections 1 to 3. The air conditioning control conditions 1 to 3 here correspond, for example, to the air conditioning control conditions 1a, 2a, and 3a shown in Figure 10. The processor 11 designs an air conditioning control process (St105) for executing (simulating) the air conditioning control for each of the set air conditioning control conditions 1 to 4 for each of the control sections 1 to 4.

[0053] The processor 11 obtains the sensor temperature Ts0 of the target space MP based on the temperature graph of the currently simulated scenario and the temperature graph of the date and time corresponding to the time period of control intervals 1 to 4 (St 106). The sensor temperature Ts0 of the target space MP after shifting the control interval may be the temperature distribution simulated based on the air conditioning control conditions determined in step St 113 and the temperature graph of the date and time corresponding to the latest time period of control intervals 1 to 4.

[0054] The processor 11 repeatedly executes the processes of steps St108 to St110 (St107) until the air conditioning control process designed, that is, the air conditioning control simulation over control intervals 1 to 4 under each of the air conditioning control conditions 1 to 4, is completed.

[0055] The processor 11 performs an air conditioning control simulation with air conditioning control conditions i corresponding to the control interval i (i: an integer greater than or equal to 0, i = 0 to 4 in this disclosure) (St 108). Specifically, the processor 11 obtains the temperature distribution Ti and sensor temperature Tsi of the target space MP at the end of the control interval i, based on the temperature distribution estimation model, the sensor temperature Ts(i-1) at the start timing of the control interval i (i.e., the present), and the air conditioning control conditions i for the control interval i (St 108). The temperature distribution estimation model is a machine learning model trained to estimate the temperature distribution after a predetermined time (5 minutes in this disclosure) based on the current sensor temperature, and predictions are made quickly (less than 1 second per prediction), allowing for faster execution of massive calculations. i = 0 (zero) is the start timing of control interval 1.

[0056] The processor 11 calculates the amount of energy Ei consumed in this control interval i based on the air conditioning control condition i and the energy function func (St109). The energy function func is calculated using the temperature difference, airflow rate, or COP, etc.

[0057] The temperature difference referred to here is the temperature difference between the sensor temperature Tsi at the air conditioner's outlet and the target sensor temperature Ts4, which is the ambient temperature outside the air conditioner. The air conditioning control process is the airflow rate set in the air conditioning control condition i of control section i. COP is an abbreviation for Coefficient of Performance, and represents the cooling / heating capacity (kW) per 1 kW of power consumption of the air conditioner.

[0058] After performing the processes in steps St108 and St109, the processor 11 increments the current value i (i+1) (St110).

[0059] The processor 11 determines whether the sensor temperature Ts3 after executing the air conditioning control corresponding to the control section 3 is close to the target sensor temperature Ts4 (i.e., sensor temperature Ts3 ≈ target sensor temperature Ts4) (St111). Specifically, the processor 11 may determine whether the sensor temperature Ts3 is within the allowable temperature range based on the tracking capability set by the control strategy, using the target sensor temperature Ts4 as a reference.

[0060] If the processor 11 determines that the sensor temperature Ts3 is close to the target sensor temperature Ts4 (St111, YES), it calculates the total amount of energy EA consumed by the air conditioning control 1 to 4 during control intervals 1 to 4 (St112).

[0061] On the other hand, if the processor 11 determines that the sensor temperature Ts3 is not close to the target sensor temperature Ts4 (St111, NO), it returns to the process of step St105.

[0062] The processor 11 determines whether the total amount of energy consumed during control intervals 1 to 4 is less than the target total amount of energy ES (St113).

[0063] If the processor 11 determines that the total amount of energy consumed between control intervals 1 to 4 EA < target total energy amount ES (St113, YES), it determines the air conditioning control condition 1 to be executed in the time period corresponding to control interval 1 among the air conditioning control processes set in step St105 (St114).

[0064] The processor 11 shifts the current control intervals 1 to 4 by one control interval (5 minutes in this disclosure) (St115). The processor 11 sets the shifted control intervals 2 to 5 as the next control intervals 1 to 4 (St101).

[0065] On the other hand, if the processor 11 determines that the total amount of energy consumed between control sections 1 to 4 is not less than the target total amount of energy ES (St113, NO), it returns to the process of step St105 and sets the air conditioning control conditions 1 to 4 for control sections 1 to 4 again. For example, if control sections 1 to 3 are air conditioning control conditions 1a, 2a, 3a (see Figure 10) and the total amount of energy EA is greater than or equal to the target total amount of energy ES, the processor 11 returns to the process of step St105 to calculate the air conditioning control conditions 1b, 2b, 3b shown in Figure 10 for control sections 1 to 3 and redesigns the air conditioning control process. After redesigning the air conditioning control process, the processor 11 executes the processes of steps St106 to St112.

[0066] The processor 11 repeatedly executes the processes in steps St101 to St115 until the amount of energy used by the air conditioner for one day (operating time) is calculated. The processor 11 also performs the calculation of the amount of energy used for one day for each temperature graph generated for each scenario. This allows the processor 11 to perform the process of running an air conditioning control simulation based on the temperature graphs for each day shown in Figure 2 (step St15) and the process of calculating daily indicators (e.g., temperature distribution, energy consumption, and comfort distribution) based on the results of the air conditioning control simulation (step St16) with higher accuracy.

[0067] <Method for Determining Air Conditioning Control Conditions> Next, an example of how to determine air conditioning control conditions using the simulation device P1 will be explained with reference to Figures 8 and 9, respectively. Figure 8 is a diagram illustrating an example of determining air conditioning control conditions (control points) using one-stage control. Figure 9 is a diagram illustrating an example of determining air conditioning control conditions (control points) using two-stage control.

[0068] The control points described below represent the temperature and airflow rate of the air blown from the outlet, which are the air conditioning control conditions (parameters) necessary to achieve the target temperature. In other words, the processor 11 determines the air conditioning control conditions for each control section by determining the control points.

[0069] <Method for determining control points by one-stage control> The processor 11 inputs the current sensor temperature Ts0 of the target space MP based on the temperature graph Gp11 and the target sensor temperature Ts4 to the air conditioning control condition calculation model. The air conditioning condition calculation model selects a control point Pt11 that has the lowest energy consumption and whose temperature after air conditioning control falls between the upper and lower limits by comparing the five-stage airflow parameter Q of the air conditioner and the five-stage temperature parameter T of the air conditioner outlet. In this disclosure, an example is described in which five different parameters can be set for the airflow parameter Q for heating and cooling and the temperature parameter T for the outlet, but it goes without saying that the invention is not limited to this.

[0070] The air conditioning condition calculation model searches for each of the 25 control points Pt12, Pt121 to Pt125 by brute-force matching the sensor temperature Ts0 obtained after running an air conditioning control simulation corresponding to control point Pt11 with five levels of airflow parameter Q and five levels of outlet temperature parameter T. Note that in Figure 8, only six control points Pt12, Pt121 to Pt125 are shown for the sake of clarity in the explanation of the figure.

[0071] The processor 11 selects control point Pt12 from among these 25 control points Pt12, Pt121 to Pt125 as the next control point after control point Pt11, as it has the lowest energy consumption and the temperature after control falls between the upper and lower limits.

[0072] The processor 11 determines the subsequent control points Pt13, Pt14, Pt15, Pt16, Pt17, and Pt18 in the same manner as the control point Pt12 described above.

[0073] <Method for determining control points using two-stage control> The processor 11 inputs the current sensor temperature Ts0 of the target space MP based on the temperature graph Gp11 and the target sensor temperature Ts4 to the air conditioning control condition calculation model. The air conditioning condition calculation model selects a control point Pt11 that has the lowest energy consumption and whose temperature after air conditioning control falls between the upper and lower limits by exhausting all possible combinations of the five-stage airflow parameter Q of the air conditioner and the five-stage temperature parameter T of the outlet. In this disclosure, an example is described in which five different parameters can be set for the five-stage airflow parameter Q and the outlet temperature parameter T, but it goes without saying that the invention is not limited to this.

[0074] The air conditioning condition calculation model searches for the temperature of each of the 25 control points Pt12 in the first stage by brute-force testing the temperature after performing air conditioning control corresponding to control point Pt11, using five levels of airflow parameter Q and five levels of outlet temperature parameter T. Note that in Figure 9, only six control points are shown for the sake of clarity, and the assignment of signs to control points other than control point Pt12 has been omitted.

[0075] The processor 11 searches for each of the 25 control points Pt13, Pt131, Pt132, Pt133, and Pt134 for each of the 25 control points Pt12 in the first stage, by using a brute-force method with the five airflow parameters Q and the five outlet temperature parameters T, based on the temperature after performing the air conditioning control corresponding to each of the 25 control points Pt12 in the first stage. In other words, the processor 11 searches for 625 control points (= 25 x 25). Note that in Figure 9, for the sake of clarity, only one control point from the second stage is shown for each control point from the first stage.

[0076] The processor 11 selects a combination of the first-stage control point Pt12 and the second-stage control point Pt13 from among the 625 control points explored, which have lower energy consumption and whose temperature after control falls between the upper and lower limits, as the control point following control point Pt11.

[0077] The processor 11 determines subsequent control points Pt14 to Pt18 in the same manner as the control points Pt12 and Pt13 described above.

[0078] Based on the above, the simulation device P1 can determine the air conditioning control conditions 1 to 4 for each control section 1 to 4 to adjust the sensor temperature of the target space MP to the sensor target temperature when a temperature change occurs as shown in the temperature graph of each scenario, based on the current temperature shown in the temperature distribution and the target temperature.

[0079] Next, a comparative example of the air conditioning control process will be described with reference to Figure 10. Figure 10 is a diagram showing a comparative example of the air conditioning control process. Note that the air conditioning control process shown in Figure 10 is just one example and is not limited thereto.

[0080] The simulation device P1 sets a series of air conditioning control processes across control sections 1 to 4 based on air conditioning control conditions 1 to 4. The simulation device P1 determines the air conditioning control condition 1 for control section 1 based on a comparison of the total energy amount EA obtained by executing the set series of air conditioning control processes with the target total energy amount ES.

[0081] The control sections 1 to 4 shown in Figure 10 are time periods in which air conditioning control is performed under a single air conditioning control condition, each divided into time intervals Δt (for example, 5 minutes). When determining the air conditioning control condition 1 for control section 1, the simulation device P1 designs air conditioning conditions 1 to 4 (air conditioning control processes) to achieve the target sensor temperature Ts4 that should be reached by the air conditioning control in the four control sections 1 to 4. Control section 4 is the section in which the air conditioner is operated in a steady state. Therefore, the simulation device P1 calculates air conditioning control conditions 1 to 4 that bring the temperature of each evaluation point to the target sensor temperature Ts4 in the three control sections 1 to 3, and maintain the target sensor temperature Ts4 by performing steady-state operation in control section 4.

[0082] Temperature distribution T0 shows the temperature distribution at time t0, which is the start timing of control section 1. Temperature distribution T1 shows the temperature distribution at time t1, which is the start timing of control section 2. Temperature distribution T2 shows the temperature distribution at time t2, which is the start timing of control section 3. Temperature distribution T3 shows the temperature distribution at time t3, which is the start timing of control section 4. Temperature distribution T4 shows the temperature distribution at time t4 within control section 4.

[0083] Sensor temperatures Ts0, Ts1, Ts2, and Ts3 are temperatures measured at locations in the target space where a comfortable temperature is desired. Sensor temperature Ts0 is the temperature measured at time t0, which is the start timing of control section 1. Sensor temperature Ts1 is the temperature measured at time t1, which is the start timing of control section 2. Sensor temperature Ts2 is the temperature measured at time t2, which is the start timing of control section 3. Sensor temperature Ts3 is the temperature measured at time t3, which is the start timing of control section 4.

[0084] The target sensor temperature Ts4 is the sensor temperature that is the target to be achieved by the air conditioning control during control intervals 1 to 4. The target sensor temperature Ts4 is the temperature measured at time t4 during control interval 4.

[0085] Energy consumption E1 is the amount of energy consumed by the execution of air conditioning control according to the air conditioning control conditions in control section 1. Energy consumption E2 is the amount of energy consumed by the execution of air conditioning control according to the air conditioning control conditions in control section 2. Energy consumption E3 is the amount of energy consumed by the execution of air conditioning control according to the air conditioning control conditions in control section 3. Energy consumption E4 is the amount of energy consumed by the execution of air conditioning control (steady state) according to the air conditioning control conditions in control section 4.

[0086] Control point Pt0 is the air conditioning control condition at the start of control section 1. At the start of control section 1, the target space MP has a temperature distribution T0 and a sensor temperature Ts0. The simulation device P1 starts from control point Pt0, which is the temperature distribution T0 and sensor temperature Ts0, and adjusts to the target sensor temperature Ts4 using the air conditioning control conditions corresponding to the control points in each control section 1 to 3.

[0087] The air conditioning control process 1 performs the following steps: in control section 1, it executes air conditioning control condition 1a to adjust the temperature to the temperature corresponding to control point Pt1A; in control section 2, it executes air conditioning control condition 2a to adjust the temperature to the temperature corresponding to control point Pt1B; in control section 3, it executes air conditioning control condition 3a to adjust the temperature to the temperature corresponding to control point Pt3; and then in control section 4, it performs steady-state air conditioning control according to air conditioning control condition 4a.

[0088] The air conditioning control process 2 performs the following steps: in control section 1, it executes air conditioning control condition 1b to adjust the temperature to the temperature corresponding to control point Pt2A; in control section 2, it executes air conditioning control condition 2b to adjust the temperature to the temperature corresponding to control point Pt2B; in control section 3, it executes air conditioning control condition 3b to adjust the temperature to the temperature corresponding to control point Pt3; and then in control section 4, it performs steady-state air conditioning control according to air conditioning control condition 4b.

[0089] The air conditioning control process 3 performs the following steps: in control section 1, it executes air conditioning control condition 1c to adjust the temperature to the temperature corresponding to control point Pt3A; in control section 2, it executes air conditioning control condition 2c to adjust the temperature to the temperature corresponding to control point Pt3B; in control section 3, it executes air conditioning control condition 3c to adjust the temperature to the temperature corresponding to control point Pt3; and then in control section 4, it performs steady-state air conditioning control according to air conditioning control condition 4c.

[0090] As described above, when the simulation device P1 executes an air conditioning control process with different air conditioning control conditions in control sections 1 to 4, the amount of energy consumed during the execution of the air conditioning control process, as well as the temperature responsiveness and tracking performance, change accordingly. Therefore, the simulation device P1 according to this embodiment can determine the air conditioning control condition 1 for control section 1 that reduces the total energy amount EA and brings the sensor temperature Ts3 closer to the target sensor temperature Ts4, based on the difference between the sensor temperature Ts3 and the target sensor temperature Ts4 at the end of control section 3, and a comparison between the total energy amount EA consumed during the execution of the air conditioning control process and the target total energy amount ES.

[0091] (Note) The following technologies are disclosed based on the descriptions of each embodiment above.

[0092] (Technology 1) A method for calculating the amount of energy performed by a computer (simulation device P1) that estimates the amount of energy required for control to achieve a first target value (sensor temperature Ts0) by a control device, comprising: acquiring information on the operating period (target period) in which the control device operates and information on the installation area of ​​the control device; acquiring weather information (past temperature data, etc.) corresponding to the operating period (target period) and the installation area; accepting settings for the weather conditions (temperature profile) in which the control device operates and the control strategy of the control device; executing a control simulation for the control device to achieve the first target value (sensor temperature Ts0) for each scenario based on the weather conditions (temperature profile) and the control strategy of the control device; and estimating and outputting the amount of energy based on the control simulation.

[0093] (Technology 2) A method for calculating the amount of energy as described in (Technology 1), comprising generating a temperature graph for each scenario showing the change in temperature during the operating period (target period) in which the control device operates, based on the weather conditions (temperature profile), and executing the control simulation based on the temperature graph. As a result, the simulation device P1 can calculate with greater accuracy the amount of energy required to achieve the sensor temperature Ts0 (target value) on each day of the target period, based on the weather conditions and the control strategy.

[0094] (Technology 3) A method for calculating the amount of energy as described in (Technology 1) or (Technology 2), wherein control conditions (air conditioning control conditions) for the control device are set for each control section in which the control device is operated under one control condition, the control simulation is performed based on the control conditions of the control device set for each control section, the amount of energy consumed by the control device in a day is estimated by the control simulation, and the amount of energy consumed during the operating period (target period) is estimated and output. As a result, the simulation device P1 can calculate the amount of energy required to achieve the sensor temperature Ts0 (target value) on each day of the target period with higher accuracy by performing the setting of air conditioning control conditions and the simulation for each control section.

[0095] (Technical 4) The method for calculating the amount of energy described in (Technical 3), wherein the control conditions are set based on the weather conditions (temperature profile) corresponding to the date on which the control simulation is performed and the control strategy of the control. As a result, the simulation device P1 can calculate with greater accuracy the amount of energy required to achieve the sensor temperature Ts0 (target value) on each day of the target period by setting the air conditioning control conditions and performing the simulation for each control section.

[0096] (Technical 5) A method for calculating the amount of energy as described in (Technical 3), wherein a second target value (target sensor temperature Ts4) is set to be achieved by control over N (N: an integer of 3 or more) consecutive control intervals in a time series, control conditions are set for each of the N control intervals to realize the second target value (target sensor temperature Ts4), and the control simulation is performed for each of the N control intervals. As a result, the simulation device P1 can calculate the amount of energy required to achieve the sensor temperature Ts0 (target value) on each day of the target period with greater accuracy by setting the target sensor temperature Ts4 for each of the N control intervals and performing the air conditioning control simulation.

[0097] (Technical 6) The method for calculating the amount of energy described in (Technical 5), wherein the control condition executed in the Nth control interval in a time series is the condition for achieving the second target value (target sensor temperature Ts4) in steady-state operation. As a result, the simulation device P1 can set air conditioning control conditions to achieve the target sensor temperature Ts4 with higher accuracy in N control intervals.

[0098] (Technical 7) A method for calculating the amount of energy as described in (Technical 5), further comprising setting a target amount of energy to be consumed in the N control intervals, estimating the amount of energy consumed in the N control intervals based on the control conditions set for each control interval, and determining the control conditions for the first control interval in the time series among the N control intervals based on a comparison of the amount of energy consumed and the target amount of energy. As a result, the simulation device P1 can calculate the amount of energy with higher accuracy by efficiently suppressing the setting of air conditioning control conditions in which the calculation result of the air conditioning control simulation of energy amount significantly exceeds the target amount of energy.

[0099] (Technical 8) The method for calculating the amount of energy described in any one of (Technical 1) to (Technical 7), wherein the weather information is the operating period (target period) and past temperature data in the installation area. As a result, the simulation device P1 can calculate with greater accuracy the amount of energy required to bring the target space MP to the sensor temperature Ts0 (target value) during the target period, based on past temperature data of the area where the air conditioner is installed.

[0100] (Technical 9) The method for calculating the amount of energy as described in (Technical 8), wherein the weather conditions (temperature profile) are the average temperature, minimum temperature, or maximum temperature in the operating period (target period) and the installation area, calculated based on the past temperature data. As a result, the simulation device P1 can calculate with greater accuracy the amount of energy required to bring the target space MP to the sensor temperature Ts0 (target value) during the target period, based on the past average temperature, minimum temperature, or maximum temperature of the area where the air conditioner is installed.

[0101] (Technical 10) The method for calculating the amount of energy as described in (Technical 8), wherein the weather conditions (temperature profile) are the temperature based on the operating period (target period) and the probability of temperature occurrence in the installation area, calculated based on the past temperature data. As a result, the simulation device P1 can calculate with greater accuracy the amount of energy required to achieve the sensor temperature Ts0 (target value) during the target period, based on the past average temperature, minimum temperature, or maximum temperature of the area where the air conditioner is installed.

[0102] (Technical 11) The method for calculating the amount of energy as described in (Technical 8), wherein the weather conditions (temperature profile) are predicted values ​​of the temperature during the operating period (target period) and in the installation area, based on past temperature data in the operating period (target period) and the installation area, and the probability of temperature occurrence based on the temperature data. As a result, the simulation device P1 can calculate with greater accuracy the amount of energy required to achieve the sensor temperature Ts0 (target value) during the target period, based on the past average temperature, minimum temperature, or maximum temperature of the area where the air conditioner is installed.

[0103] (Technical 12) The control strategy is the responsiveness and tracking ability of the control device in the control, as described in any one of (Technical 1) to (Technical 11), a method for calculating the amount of energy. As a result, the simulation device P1 can calculate the amount of energy required to achieve the sensor temperature Ts0 (target value) with greater accuracy based on the responsiveness and tracking ability of the air conditioner control.

[0104] (Technical 13) An energy calculation program executed by at least one processor 11, comprising the steps of: acquiring information on the operating period (target period) during which a control device (air conditioner) that performs control to achieve a first target value (sensor temperature Ts0) is in operation and information on the installation area of ​​the control device; acquiring weather information (past temperature data, etc.) corresponding to the operating period (target period) and the installation area; receiving settings for weather conditions (temperature profile) under which the control device operates and a control strategy for the control device, and executing a control simulation for the control device to achieve the first target value (sensor temperature Ts0) for each scenario based on the weather conditions (temperature profile) and the control strategy of the control device; and estimating and outputting the amount of energy required to achieve the first target value (sensor temperature Ts0) based on the control simulation.

[0105] Although various embodiments have been described above with reference to the attached drawings, this disclosure is not limited to such examples. It will be clear to those skilled in the art that various modifications, alterations, substitutions, additions, deletions, and equivalents can be conceived within the scope of the claims, and these will also be understood to fall within the technical scope of this disclosure. Furthermore, the components of the various embodiments described above can be combined arbitrarily without departing from the spirit of the invention.

[0106] This application is based on the Japanese Patent Application No. 2025-056364 filed on March 28, 2025, the contents of which are incorporated by reference within this application.

[0107] This disclosure is useful in providing a method for calculating energy quantities and a program for calculating energy quantities that can more accurately estimate the amount of energy required for a device to achieve a target value in a device control plan.

[0108] 10 Communication unit 11 Processor 12 Memory 13 Input unit 14 Monitor Cd Comfort distribution Dt11, Dt12, Dt13 Temperature profile MP Target space P1 Simulation device

Claims

1. A method for calculating the amount of energy performed by a computer to estimate the amount of energy required for control to achieve a first target value by a control device, comprising: acquiring information on the operating period during which the control device is in operation and information on the installation area of ​​the control device; acquiring weather information corresponding to the operating period and the installation area; accepting settings for the weather conditions under which the control device is in operation and the control strategy of the control device; executing a control simulation for the control device to achieve a first target value for each scenario based on the weather conditions and the control strategy of the control device; and estimating and outputting the amount of energy based on the control simulation.

2. A method for calculating the amount of energy according to claim 1, comprising generating a temperature graph for each scenario showing the change in temperature during the operating period in which the control device is in operation, based on the weather conditions, and executing the control simulation based on the temperature graph.

3. A method for calculating energy according to claim 1, comprising: setting control conditions for the control device for each control interval in which the control device is operated under one control condition; executing the control simulation based on the control conditions for the control device set for each control interval; estimating the amount of energy consumed by the control device per day based on the control simulation; and estimating and outputting the amount of energy consumed during the operating period.

4. The method for calculating the amount of energy according to claim 3, wherein the control conditions are set based on the weather conditions corresponding to the date on which the control simulation is performed and the control strategy of the control.

5. A method for calculating energy according to claim 3, comprising setting a second target value to be achieved by control over N (N: an integer of 3 or more) consecutive control intervals in a time series, setting control conditions for realizing the second target value in each of the N control intervals, and executing the control simulation in each of the N control intervals.

6. The method for calculating the amount of energy according to claim 5, wherein the control condition executed in the Nth control interval in a time series is the condition for achieving the second target value in steady-state operation.

7. A method for calculating energy according to claim 5, further comprising setting a target amount of energy to be consumed in the N control intervals, estimating the amount of energy consumed in the N control intervals based on the control conditions set for each control interval, and determining the control conditions for the first control interval in the time series among the N control intervals based on a comparison of the amount of energy consumed and the target amount of energy.

8. The method for calculating the amount of energy according to claim 1, wherein the weather information is past temperature data for the operating period and the installation area.

9. The method for calculating the amount of energy according to claim 8, wherein the weather conditions are the average temperature, minimum temperature, or maximum temperature in the operating period and the installation area calculated based on the past temperature data.

10. The method for calculating the amount of energy according to claim 8, wherein the weather conditions are temperatures based on the probability of occurrence of the operating period and the installation area, calculated based on the past temperature data.

11. The method for calculating the amount of energy according to claim 8, wherein the weather conditions are predicted values ​​of the temperature during the operating period and in the installation area, based on past temperature data in the operating period and the installation area, and the probability of temperature occurrence based on the temperature data.

12. The method for calculating the amount of energy according to claim 1, wherein the control strategy is the responsiveness and tracking ability of the control device in the control.

13. An energy calculation program executed by at least one processor, comprising the steps of: acquiring information on the operating period and location of a control device that performs control to achieve a first target value; acquiring weather information corresponding to the operating period and location; receiving settings for weather conditions under which the control device operates and a control strategy for the control device, and executing a control simulation for the control device to achieve the first target value for each scenario based on the weather conditions and the control strategy for the control device; and estimating and outputting the amount of energy required to achieve the first target value based on the control simulation.

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