Energy supply plan making device and energy supply plan making method

Abstract

To reduce errors related to a formulated energy supply plan. An energy supply plan formulation device includes an acquisition unit that acquires a change in a basic load of an energy device; a determination unit that determines whether or not correction of at least one of a device model and a load model is needed based on the change in the basic load; a correction unit that corrects at least one of the device model and the load model based on a determination result; a demand prediction unit that predicts an energy demand amount; and a supply plan formulation unit that formulates an energy supply plan based on the device model and the energy demand amount.

Description

Technical Field

[0001] The technology disclosed in this application relates to an energy supply planning device and an energy supply planning method. Background Technology

[0002] With the aim of efficient utilization of energy resources and reduction of energy costs, a thermoelectric energy supply system is developed that allows multiple energy devices, such as solar power generation and other power supply equipment, batteries and other power exchange equipment, coolers or heat pump water heaters (Ecocute) and heat exchange equipment, or thermal storage tanks, to work together to supply heat and electricity to buildings and other facilities (for example, see Patent Document 1, Patent Document 2 or Patent Document 3, etc.).

[0003] Existing technical documents

[0004] Patent documents

[0005] Patent Document 1: Japanese Patent Application Publication No. 2009-115386

[0006] Patent Document 2: Japanese Patent Application Publication No. 2018-128995

[0007] Patent Document 3: Japanese Patent Application Publication No. 2016-170715 Summary of the Invention

[0008] For example, in Patent Document 1 and Patent Document 2, a device model is generated that can be used to formulate an energy supply plan based on the operation of energy-powered equipment. However, the function of correcting the device model is not disclosed.

[0009] On the other hand, Patent Document 3 determines whether or not the correction of the device model described above is necessary. However, the determination of whether or not correction is necessary is based on whether the polarity of the coefficient is the same as that set by the user or compared with other models of the same type of device. Therefore, it cannot take into account the device characteristics as the basic load of the energy device, and cannot guarantee the prediction accuracy when there are changes in characteristics that accompany changes in the device structure or performance degradation of the device.

[0010] This leads to errors in load forecasting when formulating energy supply plans, and also in the formulated energy supply plans themselves. Consequently, problems arise where energy resources cannot be used efficiently and energy costs cannot be reduced.

[0011] The technology disclosed in this application was developed in view of the problems described above, and is a technology for reducing errors related to the established energy supply plan.

[0012] The first aspect of the technology disclosed in this application provides an energy supply planning apparatus. An energy supply plan for controlling the operation of at least one energy device that supplies energy to an energy supply object is formulated based on an equipment model corresponding to the energy device and a load model corresponding to the energy supply object. The equipment model is data representing the energy input / output characteristics of the corresponding energy device, and the load model is a physical formula or calculation method for predicting the energy demand of the corresponding energy supply object. The energy supply planning apparatus includes: an acquisition unit for acquiring changes in the basic load of the energy device; a determination unit for determining, based on the changes in the basic load, whether correction of at least one of the equipment model and the load model is needed; a correction unit for correcting at least one of the equipment model and the load model based on the determination result of the determination unit; a demand forecasting unit for predicting the energy demand of the energy supply object; and a supply planning unit for formulating an energy supply plan based on the equipment model and the energy demand.

[0013] The second aspect of the technology disclosed in this application provides a method for formulating an energy supply plan. The method involves formulating an energy supply plan based on a device model corresponding to the energy device and a load model corresponding to the energy supply object, where the device model represents data indicating the energy input / output characteristics of the corresponding energy device, and the load model is a physical formula or calculation method used to predict the energy demand of the corresponding energy supply object. The method obtains changes in the basic load of the energy device, determines whether correction is needed for at least one of the device model and the load model based on the changes in the basic load, corrects at least one of the device model and the load model based on the determination result, predicts the energy demand of the energy supply object, and formulates an energy supply plan based on the device model and the energy demand.

[0014] According to at least the first and second embodiments of the technology disclosed in this application, the model can be corrected based on changes in the basic load of the energy equipment, and then an energy supply plan can be formulated based on the model. Therefore, errors related to the energy supply plan can be reduced.

[0015] Furthermore, the objectives, features, aspects, and advantages related to the technology disclosed in this application become clearer through the detailed description and accompanying drawings shown below. Attached Figure Description

[0016] Figure 1 This is a diagram illustrating, conceptually, an example of the structure of a thermoelectric energy supply system including an energy supply planning device, according to an embodiment.

[0017] Figure 2 This is a diagram illustrating an example of the structure of an energy supply planning device according to an embodiment.

[0018] Figure 3 This is a diagram illustrating an example of a table showing the parameters of an expressive device model for an embodiment.

[0019] Figure 4 This is a diagram illustrating an example of an energy supply plan for an implementation method.

[0020] Figure 5 This is a diagram illustrating an example of the structure of the correction requirement determination unit and the parameter calculation unit in the embodiment.

[0021] Figure 6 This is a flowchart illustrating an example of the processing flow of the correction requirement determination unit in an implementation embodiment.

[0022] Figure 7 This is a diagram illustrating an example of frequency component extraction in an implementation scheme.

[0023] Figure 8 This is a flowchart illustrating an example of the processing flow of the correction requirement determination unit in an implementation embodiment.

[0024] Figure 9 This is a flowchart illustrating an example of the processing flow of the parameter calculation unit in an embodiment.

[0025] Figure 10 This is a flowchart illustrating an example of the processing flow of the parameter calculation unit in an embodiment.

[0026] Figure 11 This is a flowchart illustrating an example of the processing flow of the demand forecasting unit in an implementation method.

[0027] Figure 12 This is a flowchart illustrating an example of the processing flow of the demand forecasting unit in an implementation method.

[0028] Figure 13 This is a flowchart illustrating an example of the processing flow of the supply planning department in an implementation method.

[0029] Figure 14 It is a general example of practical application. Figure 2 as well as Figure 5 A diagram illustrating the hardware structure of the energy supply planning device.

[0030] Figure 15 It is a general example of practical application. Figure 2 as well as Figure 5 A diagram illustrating the hardware structure of the energy supply planning device.

[0031] Figure 16 This is a diagram that conceptually illustrates an example of the structure (functional part) of the energy supply planning device of this embodiment.

[0032] (Symbol Explanation)

[0033] 1: Energy supply planning device; 2: Energy equipment; 3, 31, 32: Sensors; 4: Control network; 11: Receiving device; 12: Transmitting device; 13, 1103: Storage device; 14: Computing device; 21: Power supply equipment; 22: Power exchange equipment; 23: Heat supply equipment; 24: Heat exchange equipment; 131: Equipment characteristic value data; 132: Load model parameters; 133: Actual energy supply data; 134: Actual energy consumption data; 135: Indoor External environment data; 136: Equipment operation performance data; 141: Calibration need determination unit; 141a: Main cause analysis unit; 141b: Characteristic change acquisition unit; 141c: Judgment unit; 142: Parameter calculation unit; 143: Demand forecasting unit; 143a: Load model; 144: Supply planning unit; 144a: Equipment model; 144b: Optimization engine; 145: Control command conversion unit; 1000: Building; 1102A, 1102B: Processing circuit. Detailed Implementation

[0034] Hereinafter, embodiments will be described with reference to the accompanying drawings. In the following embodiments, detailed features are shown for the purpose of illustrating the technology, but these are illustrative and are not all features necessary for carrying out the embodiments.

[0035] Furthermore, the accompanying drawings are schematic, and for ease of explanation, structures may be omitted or simplified as appropriate. Additionally, the size and positional relationships of structures shown in different drawings need not be accurately described and can be appropriately varied. Moreover, in drawings such as top views that are not sectional views, shading may sometimes be added to facilitate understanding of the embodiments.

[0036] Furthermore, in the following descriptions, the same symbols are used to illustrate the same constituent elements, and their names and functions are also set to be the same. Therefore, detailed descriptions of them are sometimes omitted to avoid repetition.

[0037] Furthermore, in the descriptions described below, when a certain constituent element is described as "possessing," "including," or "having," unless otherwise specified, it is not an exclusive expression that excludes the existence of other constituent elements.

[0038] <Conceptual Structure of an Energy Supply Planning Device>

[0039] Figure 16 This is a diagram that conceptually illustrates an example of the structure (functional part) of the energy supply planning device of this embodiment.

[0040] Figure 16 The energy supply planning device shown conceptually is an energy supply planning device that formulates an energy supply plan for controlling the operation of at least one energy device that supplies energy to the energy device based on an equipment model corresponding to the energy device and a load model corresponding to the energy supply object.

[0041] like Figure 16 As illustrated, the energy supply planning device includes an acquisition unit 3001, a judgment unit 3002, a correction unit 3003, a demand forecasting unit 3004, and a supply planning unit 3005.

[0042] Here, the equipment model represents the energy input and output characteristics of the corresponding energy equipment. Additionally, the load model is a physical formula or calculation method used to predict the energy demand of the corresponding energy supply object.

[0043] The acquisition unit 3001 acquires changes in the basic load of the energy equipment. The determination unit 3002 determines, based on the changes in the basic load, whether correction is needed for at least one of the equipment model and the load model.

[0044] The calibration unit 3003 calibrates at least one of the equipment model and the load model based on the judgment result from the judgment unit. The demand forecasting unit 3004 forecasts the energy demand of the energy supply target. The supply planning unit 3005 formulates an energy supply plan based on the equipment model and the energy demand.

[0045] The various structures shown in the accompanying drawings are illustrated in more detail in the following embodiments. Figure 16 Examples of structures are shown.

[0046] <Implementation Method>

[0047] The energy supply planning apparatus and energy supply planning method of this embodiment will be described below.

[0048] <Structure of the Energy Supply Planning Device>

[0049] Figure 1 This is a diagram conceptually illustrating an example of the structure of a thermoelectric power supply system including the energy supply planning device 1 of this embodiment. (See diagram for details.) Figure 1As illustrated, the energy supply planning device 1 is connected to the energy device 2 and the sensor 3 via the control network 4. Here, the energy supply planning device 1 is a device that controls the operation of the energy device 2.

[0050] Energy device 2 is a component of the energy supply system, which is a device for supplying energy to the building 1000, which is the destination of the energy supply, or for storing (i.e., accumulating) energy for supply.

[0051] Energy device 2 includes power supply equipment 21 such as a self-generated generator that supplies electricity to buildings 1000, power exchange equipment 22 such as batteries that store and discharge electricity, heat supply equipment 23 such as a cooler that supplies heat, and heat exchange equipment 24 such as a heat storage tank that stores and dissipates heat. Furthermore, the structure and quantity of energy device 2 are not limited to… Figure 1 The example is shown.

[0052] Sensor 3 is a sensor that measures physical quantities, including one or more sensors (in...) Figure 1 In this system, sensors 31 and 32 are used. Sensor 3, for example, measures temperature, humidity, or solar radiation and acquires corresponding data as indoor / outdoor environmental data. Furthermore, sensor 3 can also be integrated into the energy device 2. Additionally, the data acquired by sensor 3 may include weather forecast data obtained via the Internet, etc.

[0053] Control network 4 is a communication network used to connect energy supply planning device 1, energy equipment 2 and sensor 3 to each other.

[0054] Figure 2 This is a diagram illustrating an example of the structure of the energy supply planning device 1 according to this embodiment. Figure 2 As illustrated, the energy supply planning device 1 includes a receiving device 11, a transmitting device 12, a storage device 13, and a computing device 14. Furthermore, as described above, Figure 2 The illustrated energy supply planning device 1 controls the operation of the energy equipment 2.

[0055] The receiving device 11 acquires data from the energy device 2 and the sensor 3 at predetermined time intervals (e.g., 5-minute intervals). The receiving device 11 then stores the acquired data in the storage device 13. Furthermore, the acquisition intervals for data from the energy device 2 and the data from the sensor 3 can be different.

[0056] The transmitting device 12 sends control commands to the energy device 2. The storage device 13 stores at least the device characteristic value data 131, load model parameters 132, energy supply performance data 133, consumption performance data 134, indoor and outdoor environmental data 135, and equipment operation performance data 136.

[0057] Equipment characteristic value data 131 is data related to the characteristic values ​​(parameters) of the equipment model 144a possessed by the supply planning unit 144. Equipment model 144a is data showing the energy input and output characteristics of energy device 2 using tables or graphs. Equipment model 144a is used as a reference when formulating energy supply plans to determine the energy input and output quantities in the energy device 2. Equipment model 144a includes, for example, the relationship between the hot and cold water outlet temperatures of a cooler (a type of energy device 2) and the external gas temperature, and the coefficient of performance (COP).

[0058] Load model parameter 132 is data related to the parameters of the load model 143a possessed by the demand forecasting unit 143 (described later). The load model 143a is a physical formula or calculation method used to predict the energy demand in the building 1000, and load model parameter 132 is used to predict this energy demand. Load model parameter 132 is, for example, the thermal flux ratio of the wall surface, which serves as information about the structure of the building 1000.

[0059] The energy supply performance data 133 shows the amount of energy supplied by the energy device 2 at each time point earlier than the current time. Furthermore, the length of the backward storage period for the energy supply performance data 133 is not particularly limited. The energy supply performance data 133 can be data on the energy supply directly measured by the sensor 3, or data on the energy supply calculated based on other associated data.

[0060] The consumption data 134 shows the amount of electricity or fuel consumed by the energy device 2 at each time point earlier than the current time. Furthermore, the length of the backward storage period for the consumption data 134 is not specifically limited. The consumption data 134 can be consumption data directly measured by the sensor 3, or consumption data calculated based on other associated data.

[0061] Indoor and outdoor environmental data 135 refers to data such as temperature, humidity, or solar radiation measured by sensor 3 around or inside building 1000. Weather forecast data obtained via the Internet or similar means may also be included in the indoor and outdoor environmental data 135. The length of the backward retention period for the indoor and outdoor environmental data 135 and the length of the forecast period for the next day or later are not particularly limited.

[0062] The equipment operation performance data 136 shows the equipment status of the energy device 2 at each time point, prior to the current time. For example, the equipment operation performance data 136 may include the inlet and outlet temperatures or flow rates of the hot and cold water in a cooler, which is a type of energy device 2. Furthermore, the length of the backward retention period for the equipment operation performance data 136 is not specifically limited.

[0063] The arithmetic unit 14 includes a correction need determination unit 141, a parameter calculation unit 142, a demand forecasting unit 143, a supply planning unit 144, and a control command conversion unit 145.

[0064] The calibration necessity determination unit 141 determines whether it is necessary to use other data to calibrate the equipment characteristic value data 131 and the load model parameters 132. If the energy supply characteristics in the building 1000 change due to equipment deterioration or structural changes, the calibration necessity determination unit 141 determines that calibration of the equipment model 144a or the load model 143a (or both) is required. Furthermore, details regarding the calibration necessity determination unit 141 will be described in detail later.

[0065] The parameter calculation unit 142 performs calculations on the equipment model 144a or load model 143a (or both) that are determined by the correction need determination unit 141 to require correction, and then updates the parameter calculation unit 142 by storing the new parameters as the calculation result in the storage device 13, thereby correcting the equipment model 144a or load model 143a (or both). Further details regarding the parameter calculation unit 142 will be described in detail later.

[0066] The demand forecasting unit 143 uses load model 143a to predict the energy demand in building 1000 based on data obtained from storage device 13. As described above, load model 143a is a physical formula or calculation method for predicting the energy demand in building 1000, and load model parameters 132 are used to predict this energy demand. Further details regarding the demand forecasting unit 143 will be described in detail later.

[0067] The supply planning unit 144 uses equipment model 144a and optimization engine 144b to determine the operation plan of energy equipment 2 based on the energy demand in building 1000 predicted by demand forecasting unit 143 and data obtained from storage device 13, and then formulates an energy supply plan for building 1000. Equipment model 144a uses tables or similar data to display the energy input and output characteristics of energy equipment 2. Equipment characteristic value data 131 is used to determine the input and output energy of energy equipment 2. Further details about the supply planning unit 144 will be described later.

[0068] The control command conversion unit 145 converts the energy supply plan formulated by the supply planning unit 144 into control commands for actually providing commands to the energy equipment 2.

[0069] Figure 3 This is a diagram illustrating an example of a table showing the parameters of the device model 144a according to this embodiment. The parameters of the device model 144a are stored according to one or more variables (in... Figure 3 In the middle, x1…x n ,y1…y n The values ​​are determined by a table. Multiple tables can be stored, depending on conditions for each device that depend on variables other than those stored in the table. Additionally, multiple variables that enable conditional branching can exist.

[0070] Figure 4 This is a diagram illustrating an example of the energy supply plan in this embodiment. Figure 4 In the diagram, the vertical axis represents the amount of energy [kWh], and the horizontal axis represents time.

[0071] exist Figure 4 In the diagram, the energy supply capacity of energy device A is represented by a thick dashed line, the energy supply capacity of energy device B by a thin dashed line, the cumulative value (total value) of the energy supply capacities of energy devices A, B, and C by a thick solid line, and the amount of energy stored in energy device C by a thin solid line. Furthermore, the energy supply capacity of energy device C is the portion remaining after removing the thick and thin dashed lines from the thick solid line.

[0072] like Figure 4 As illustrated, an example of an energy supply plan is an example that shows the energy supply capacity that each energy device should output and its remaining amount (the amount of stored energy) in a time series according to the energy demand.

[0073] The energy supply plan is transmitted to each energy device 2 after being transformed by the control command conversion unit 145 into a form that each energy device 2 can recognize as a control command. Then, the energy device 2 supplies the energy requested according to the energy supply plan.

[0074] Figure 5 This diagram illustrates an example of the structure of the calibration need determination unit 141 and parameter calculation unit 142 in this embodiment. The energy supply planning device 1 includes the calibration need determination unit 141 and parameter calculation unit 142, thereby ensuring the accuracy of the equipment model 144a and load model 143a even in the event of changes in characteristics associated with changes in equipment structure or performance degradation of the equipment, and providing energy supply stably and cost-effectively.

[0075] The calibration necessity determination unit 141 includes a main cause analysis unit 141a, a characteristic change acquisition unit 141b, and a determination unit 141c. As described above, using data acquired from the storage device 13, it determines whether calibration of the device model 144a in the energy device 2 is necessary. The determination result of whether calibration is necessary is output to the parameter calculation unit 142.

[0076] As described above, the parameter calculation unit 142 selects the energy device 2 whose characteristic value or parameter needs to be corrected based on the determination result output from the correction need determination unit 141. For the parameters of the device model 144a or load model 143a (or both) corresponding to the energy device 2, it uses the data obtained from the storage device 13 to calculate new parameters and update them.

[0077] Figure 6 This is a flowchart illustrating an example of the processing flow of the correction need determination unit 141 in this embodiment. Figure 6 The processing from step ST1 to step ST4 is performed by the main cause analysis unit 141a in the correction need determination unit 141. Additionally, Figure 6 The processing from step ST5 to step ST7 is performed by the characteristic change acquisition unit 141b in the correction need determination unit 141. Additionally, Figure 6 The processing from step ST8 to step ST10 is performed by the determination unit 141c in the correction need determination unit 141.

[0078] First, in step ST1, the main cause analysis unit 141a selects one of the multiple energy devices 2 in the energy supply system.

[0079] Next, in step ST2, the main cause analysis unit 141a acquires the energy supply performance data 133 of the energy device 2 selected in step ST1. Here, the energy supply performance data 133 for a certain period of time (e.g., 7 days) is acquired from the storage device 13, but the acquired period is not limited to this.

[0080] Next, in step ST3, the main cause analysis unit 141a evaluates the degree of influence of the energy device 2 selected in step ST1 on the energy supply system. For example, the main cause analysis unit 141a refers to the energy supply performance data 133 and evaluates the degree of influence of the energy device 2 on the energy supply system according to the method that the amount of energy supplied by the energy device 2 is proportional to the amount of energy supplied by the energy supply system as a whole.

[0081] Next, in step ST4, the main cause analysis unit 141a determines whether the evaluation of the impact of all energy devices 2 in the energy supply system has been completed. Then, if the evaluation of the impact of all energy devices 2 in the energy supply system has been completed, that is, if from... Figure 6 In the case of "Yes" in the illustrated step ST4 branch, proceed to... Figure 6 The illustrated step ST5. On the other hand, in the case of energy device 2 where the evaluation of the impact has not been completed, i.e., from... Figure 6 In the case of "No" in the illustrated step ST4 branch, return to Figure 6 The illustrated step ST1.

[0082] In step ST5, the characteristic change acquisition unit 141b compares the influence of the multiple energy devices 2 evaluated in step ST3 and extracts the energy device 2 with the highest influence.

[0083] Next, in step ST6, the characteristic change acquisition unit 141b performs frequency analysis on the energy supply performance data 133 of the energy device 2 extracted in step ST5, thereby acquiring frequency components. Here, the energy supply performance data 133 for a certain period of time (e.g., 7 days) is acquired from the storage device 13, but the acquired period is not limited to this. In the frequency analysis, as shown in the following equation (1), the power spectrum as the relationship between frequency and amplitude is derived from the time series data of the energy supply performance data 133, which is the input data, using Fourier transform.

[0084] [Number 1]

[0085]

[0086] Here, t represents the time of the calculation, x represents the number of times, n represents the total number of data, F(x) represents the value after the Fourier transform at number x, and y(t) represents the output at time t. Based on the transformation result, the frequency is calculated using the following equation (2), and the amplitude is calculated using the following equation (3).

[0087] [Number 2]

[0088]

[0089] Here, f(x) represents the frequency at number x.

[0090] [Number 3]

[0091]

[0092] Here, A(x) represents the amplitude at order x.

[0093] Next, in step ST7, the characteristic change acquisition unit 141b explores the DC component by referring to the frequency component acquired in step ST6, and considers it as the basic load of the energy device. Here, the basic load refers to the amount of energy (electrical power) that is maintained at a minimum during a predetermined period, regardless of the season or time period. Furthermore, the difference in amplitude between the basic load of the energy device and the previous day is calculated based on the current time, and the difference in amplitude between the basic load of the energy device and the period prior is also calculated. In the extraction of the basic load, the amplitude at a frequency of 0Hz acquired in step ST6 is extracted as the DC component.

[0094] Next, in step ST8, the determination unit 141c compares the difference in amplitude of the basic load of the energy device based on the current time calculated in step ST7 with a predetermined threshold. Furthermore, the predetermined threshold is a value that can be appropriately changed and is not limited to a specific value. Then, the determination unit 141c determines whether the difference in amplitude of the basic load of the energy device based on the current time is above the predetermined threshold.

[0095] If the difference in amplitude of the basic load based on the current time is above a predetermined threshold, that is, compared with the value from... Figure 6 In the case of "Yes" in the illustrated ST8 branch, proceed to... Figure 6 The illustrated step ST9. On the other hand, if the difference in amplitude of the basic load based on the current time is less than a predetermined threshold, that is, compared with... Figure 6 In the case of "No" in the illustrated step ST8 branch, proceed to... Figure 6 The illustrated step ST10.

[0096] In step ST9, the difference calculated in step ST7 is above the threshold, so the determination unit 141c determines that the device characteristic value data 131, which is a parameter of the device model 144a, needs to be corrected.

[0097] In step ST10, the difference calculated in step ST7 is less than the threshold, so the determination unit 141c determines that the correction of the device characteristic value data 131, which is a parameter of the device model 144a, is not required.

[0098] Next, in step ST11, the determination unit 141c outputs the determination result of whether or not the correction is needed, as determined in step ST9 or step ST10, to the parameter calculation unit 142.

[0099] Figure 7 This is a diagram illustrating an example of frequency component decimation in this embodiment. Figure 7 In the diagram, the vertical axis represents amplitude, and the horizontal axis represents frequency. Additionally, in... Figure 7 In, it is shown that in Figure 6 Examples of frequency components obtained by performing frequency analysis on the actual energy supply data 133 in steps ST6 and ST7.

[0100] By proceeding through step ST6, export as follows: Figure 7 The power spectrum is shown. Furthermore, the DC component, considered as the basic load of the energy device in step ST7, is, as described above, a portion such as point P1 with a frequency of 0 Hz. The need for correction is determined by referring to the amplitude of this portion.

[0101] Figure 8 This is a flowchart illustrating an example of the processing flow of the correction need determination unit 141 in this embodiment. Figure 6 The processing flow shown is different, in Figure 8 Frequency analysis is not performed in the processing flow shown.

[0102] Figure 8 The processing from step ST101 to step ST104 is performed by the main cause analysis unit 141a in the correction need determination unit 141. Additionally, Figure 8 The processing from step ST105 to step ST110 is performed by the characteristic change acquisition unit 141b in the correction need determination unit 141. Additionally, Figure 8 The processing from step ST111 to step ST113 is performed by the determination unit 141c in the correction need determination unit 141.

[0103] Figure 8 From step ST101 to step ST105 in Figure 6 The steps ST1 through ST5 are the same, so the explanation is omitted.

[0104] Next, in step ST106, the characteristic change acquisition unit 141b acquires the energy supply performance data 133 and consumption performance data 134 of the energy device 2 extracted in step ST105. Here, the energy supply performance data 133 and consumption performance data 134 for a certain period of time (e.g., 7 days) are acquired from the storage device 13, but the acquired period is not limited to this.

[0105] Next, in step ST107, the characteristic change acquisition unit 141b uses the data acquired in step ST106 to calculate the operating time value of the parameter that is the object of correction for each moment. For example, in energy supply equipment such as heat pumps or cogeneration systems that input electricity and output heat through energy conversion, or in energy supply equipment such as solar photovoltaics (PV) that inputs energy other than heat or electricity, the operating time value of the object of correction as the parameter is calculated using the following formula (4).

[0106] [Number 4]

[0107]

[0108] Here, t represents time, and Parameter m (t) represents the operating time value at time t, Q m (t) represents the amount of output energy at time t, P m (t) represents the amount of input energy at time t. In addition, in energy exchange devices such as batteries or heat storage tanks that do not perform energy conversion but accumulate energy in the device to regulate the energy supply, the correction object is set as a coefficient related to energy loss, and the following formula (5) is used to calculate the data.

[0109] [Number 5]

[0110] Q loss_m (t)=Q stock (t-1)-Q stock (t)+Q in (t)-Q out (t)…(5)

[0111] Here, t represents time, Q loss_m (t) represents the operating time value at time t, Q stock Q(t) represents the amount of energy stored at time t. in (t) represents the amount of input energy at time t, Q out (t) represents the amount of output energy at time t.

[0112] Next, in step ST108, the characteristic change acquisition unit 141b acquires indoor and outdoor environmental data 135 and equipment operation performance data 136 extracted in step ST105 from the energy device 2. Here, a certain period of time (e.g., 7 days) of indoor and outdoor environmental data 135 and equipment operation performance data 136 are acquired from the storage device 13, but the period acquired is not limited to this.

[0113] Next, in step ST109, the characteristic change acquisition unit 141b uses the data acquired in step ST108 to calculate the theoretical value of the parameter that is the object of correction for each time moment. The theoretical value is calculated using the associated data in the device characteristic value data 131. For example, in the case of a cooler, which is a type of energy device 2, the COP is calculated by referring to the table in the device characteristic value data 131 using the hot and cold water outlet temperatures and the external gas temperature.

[0114] Next, in step ST110, the characteristic change acquisition unit 141b calculates the root mean square error (RMSE) of the operating time values ​​and theoretical values ​​calculated in steps ST107 and ST109. That is, the characteristic change acquisition unit 141b acquires an index of the change in basic load based on the RMSE of the operating time values ​​and theoretical values. The RMSE is calculated using the following formula (6).

[0115] [Number 6]

[0116]

[0117] Here, n represents the total number of time steps, i represents the time step, and Parameter ci The parameter represents the theoretical value of time step i. mi This represents the running time value of time step i.

[0118] Similarly, the RMSE for the energy exchange device is calculated using the following equation (7).

[0119] [Number 7]

[0120]

[0121] Here, n represents the total number of time steps, i represents the number of time steps, and Q represents the total number of time steps. loss_ci Q represents the theoretical value of time step i. loss_mi This represents the running time value of time step i.

[0122] Next, in step ST111, the determination unit 141c compares the RMSE calculated in step ST110 with a predetermined threshold. Furthermore, the predetermined threshold is a value that can be appropriately changed and is not limited to a specific value. Then, the determination unit 141c determines whether the RMSE is above the predetermined threshold. That is, the determination unit 141c determines whether the change in the basic load is above the threshold based on the RMSE values ​​during operation and the theoretical values.

[0123] When RMSE is above a predetermined threshold, i.e., compared to... Figure 8In the case of "Yes" in the illustrated step ST111 branch, proceed to... Figure 8 The illustrated step ST112. On the other hand, when RMSE is less than a predetermined threshold, i.e., from... Figure 8 In the case of "No" in the illustrated step ST111 branch, proceed to... Figure 8 The illustrated step ST113.

[0124] In step ST112, the RMSE calculated in step ST111 is above the threshold, so the determination unit 141c determines that the device characteristic value data 131, which is a parameter of the device model 144a, needs to be corrected.

[0125] In step ST113, the RMSE calculated in step ST111 is less than the threshold, so the determination unit 141c determines that the correction of the device characteristic value data 131, which is a parameter of the device model 144a, is not required.

[0126] Next, in step ST114, the determination unit 141c outputs the determination result of whether or not the correction is needed, as determined in step ST112 or step ST113, to the parameter calculation unit 142.

[0127] according to Figure 8 The processing flow shown, by using RMSE threshold comparison, is not limited to equipment characteristic value data 131, but can also determine whether the parameters of other models need correction. For example, by using operating values ​​as previously calculated demand forecasts and theoretical values ​​as actual supply energy data 133, the determination unit 141c can determine the magnitude of the prediction error of load model 143a and whether the parameters of load model 143a need correction.

[0128] Figure 9 This is a flowchart illustrating an example of the processing flow of the parameter calculation unit 142 in this embodiment.

[0129] First, in step ST201, one of the multiple energy devices 2 in the energy supply system is selected.

[0130] Next, in step ST202, the calibration need determination unit 141 determines whether calibration of the parameters (i.e., device characteristic value data 131) of the device model 144a of the selected energy device 2 is required, based on the calibration need determination result for the energy device 2 selected in step ST201.

[0131] In cases where the parameters of the selected energy device 2, model 144a, need to be corrected, i.e., from... Figure 9 In the case of "Yes" in the illustrated step ST202 branch, proceed to... Figure 9 The illustrated step ST203. On the other hand, in the case where the parameter correction of the device model 144a of the selected energy device 2 is not required, i.e., from... Figure 9 In the case of "No" in the illustrated step ST202 branch, proceed to... Figure 9 The illustrated step ST211.

[0132] In step ST203, for the random exploration process to be performed in subsequent steps, an RMSE target value and initial parameter values ​​are set. The RMSE target value is set to any value, for example, set to... Figure 8 The predetermined threshold value used for comparison with RMSE in step ST111. Additionally, the initial parameter values ​​are set to arbitrary values, such as the values ​​of the parameters used by the model in real-world situations.

[0133] Next, in step ST204, indoor and outdoor environmental data 135 and energy supply performance data 133 extracted in step ST201 from energy device 2 are acquired. Here, indoor and outdoor environmental data 135 and energy supply performance data 133 for a certain period of time (e.g., 7 days) are acquired from storage device 13, but the period acquired is not limited to this.

[0134] Next, in step ST205, random temporary parameters are generated for random exploration in subsequent steps. The range in which the parameters deviate randomly is not specifically limited.

[0135] Next, in step ST206, load forecasting is performed using the random parameters generated in step ST205, and then the RMSE of the load forecast and the actual energy supply data 133 is calculated. The RMSE is calculated using the following equation (8).

[0136] [Number 8]

[0137]

[0138] Here, n represents the total number of time steps, i represents the number of time steps, and Q represents the total number of time steps. ci Q represents the load forecast value at time step i. mi This represents the actual energy supply value at time step i.

[0139] Next, in step ST207, it is determined whether the RMSE calculated in step ST206 is less than the provisional RMSE. The provisional RMSE is set as the RMSE stored in step ST208 described later.

[0140] When the RMSE calculated in step ST206 is less than the provisional RMSE, that is, when it is from... Figure 9In the case of "Yes" in the illustrated step ST207 branch, proceed to... Figure 9 The illustrated step ST208. On the other hand, when the RMSE calculated in step ST206 is greater than or equal to the provisional RMSE, that is, from... Figure 9 In the case of "No" in the illustrated step ST207 branch, proceed to... Figure 9 The illustrated step ST209. Furthermore, if the provisional RMSE is not stored, proceed to step ST208.

[0141] In step ST208, since the RMSE calculated in step ST206 is less than the provisional RMSE, the random parameter generated in step ST205 is stored as a provisional parameter. Here, if a previously stored provisional parameter exists, the old provisional parameter is updated to a new provisional parameter. Additionally, the RMSE calculated in step ST206 is stored as a provisional RMSE. Here, if a previously stored provisional RMSE exists, the old provisional RMSE is updated to a new provisional RMSE.

[0142] In step ST209, it is determined whether the RMSE calculated in step ST206 is below a predetermined threshold. Here, the predetermined threshold is set as the RMSE target value set in step ST203.

[0143] When the RMSE calculated in step ST206 is below a predetermined threshold, that is, when it is from... Figure 9 In the case of "Yes" in the illustrated step ST209 branch, proceed to... Figure 9 The illustrated step ST210. On the other hand, when the RMSE calculated in step ST206 is greater than a predetermined threshold, i.e., compared with... Figure 9 In the case of "No" in the illustrated step ST209 branch, return to Figure 9 The illustrated step ST205.

[0144] In step ST210, since the RMSE calculated in step ST206 is below a predetermined threshold, the provisional parameter stored in step ST208 is output as the corrected new parameter.

[0145] Next, in step ST211, it is determined whether the evaluation of all energy devices 2 in the energy supply system has been completed. Then, if the evaluation of all energy devices 2 in the energy supply system has been completed, that is, if the evaluation is consistent with the evaluation of all energy devices 2 in the energy supply system, then... Figure 9 If the step ST211 branch corresponds to "Yes", the process ends. On the other hand, if there is an energy device 2 whose evaluation is incomplete, i.e., related to... Figure 9If the "No" condition corresponds to the step ST211 branch shown in the example, return to Figure 9 The illustrated step ST201.

[0146] according to Figure 9 The processing flow shown is not limited to equipment characteristic value data 131, but can also be used to correct parameters of other models. For example, by changing the "equipment operation performance data" in step ST204 to "supply energy performance data", the parameters of load model 143a can be corrected. In addition, in this case, the above-mentioned steps ST201 and ST211 are not required.

[0147] Figure 10 This is a flowchart illustrating an example of the processing flow of the parameter calculation unit 142 in this embodiment. Figure 9 The processing flow shown is different, in Figure 10 The processing flow shown does not involve random exploration.

[0148] First, in step ST301, one of the multiple energy devices 2 in the energy supply system is selected.

[0149] Next, in step ST302, the calibration need determination unit 141 determines whether calibration of the device model 144a of the selected energy device 2 is required, based on the calibration need determination result for the energy device 2 selected in step ST301.

[0150] In cases where the parameters of the selected energy device 2, model 144a, need to be corrected, i.e., from... Figure 10 In the case of "Yes" in the illustrated step ST302 branch, proceed to... Figure 10 The illustrated step ST303. On the other hand, in the case where the parameter correction of the device model 144a of the selected energy device 2 is not required, i.e., from... Figure 10 In the case of "No" in the illustrated step ST302 branch, proceed to... Figure 10 The illustrated step ST306.

[0151] In step ST303, indoor and outdoor environmental data 135, energy supply performance data 133 of the energy device 2 extracted in step ST301, and equipment operation performance data 136 are acquired. Here, a certain period of time (e.g., 7 days) of indoor and outdoor environmental data 135, energy supply performance data 133, and equipment operation performance data 136 are acquired from the storage device 13, but the period acquired is not limited to this.

[0152] Next, in step ST304, a regression formula with energy supply as the target variable is derived through multivariate linear regression analysis. The regression formula is derived as shown in equation (9) below.

[0153] [Number 9]

[0154] Q c =x1α1+x2α2…+β…(9)

[0155] Here, Q c Let α1, α2… represent the amount of energy supplied, α1, α2… represent the regression coefficients, x1, x2… represent the explanatory variables, and β represent the constant term. Furthermore, based on the concept of the least squares method, each regression coefficient is calculated as a minimization problem of the following equation (10).

[0156] [Number 10]

[0157]

[0158] Here, i represents the total number of data points, and y i Let α1, α2, ... represent the target variable for the i-th data point, α1, α2, ... represent the regression coefficients, x1, x2, ... represent the explanatory variables for the i-th data point, and β represent the constant term. The explanatory variables in the regression formula are the data associated with the energy device 2 selected in step ST301.

[0159] Next, in step ST305, the device characteristic value data 131 is updated by referring to the regression coefficients in the regression formula derived in step ST304. The objective function of the regression formula can also be set to the device characteristic value data 131 instead of the energy quantity. For example, in the case of a cooler, which is a type of energy device 2, the values ​​of the parameters stored in the table of device characteristic value data 131 are updated by referring to the regression coefficients of a regression formula that uses the hot and cold water outlet temperature and the external gas temperature as explanatory variables and COP as the objective variable. At this time, COP is set as shown in... Figure 8 The operating time values ​​calculated in step ST107 are used to derive the regression formula as shown in equation (11) below.

[0160] [Number 11]

[0161] Paramgter c =x1α1+x2α2…+β…(11)

[0162] Here, Parameter c The theoretical values ​​of the parameters of the calibration object are represented, α1, α2... represent regression coefficients, x1, x2... represent explanatory variables, and β represents the constant term.

[0163] In step ST306, it is determined whether the evaluation of all energy devices 2 in the energy supply system has been completed. Then, if the evaluation of all energy devices 2 in the energy supply system has been completed, that is, if the evaluation is consistent with the evaluation of all energy devices 2 in the energy supply system, then the evaluation is completed. Figure 10 If the step ST306 branch corresponds to "Yes" in the example, the process ends. On the other hand, if there is an energy device 2 whose evaluation is incomplete, i.e., related to... Figure 10 In the case of "No" in the illustrated step ST306 branch, return to Figure 10 The illustrated step ST301.

[0164] according to Figure 10 The processing flow shown is not limited to the parameters of equipment model 144a, but can also be used to correct the parameters of other models. For example, by changing the "equipment characteristic value data" in step ST305 to "load model parameters", the parameters of load model 143a can be corrected. In addition, in this case, steps ST301 and ST306 are not required.

[0165] Figure 11 This is a flowchart illustrating an example of the processing flow of the demand forecasting unit 143 in this embodiment.

[0166] First, in step ST401, the energy supply performance data 133 of all energy devices 2 in the energy supply system is acquired. Here, the energy supply performance data 133 for a certain period of time (e.g., 7 days) is acquired from the storage device 13, but the period acquired is not limited to this. The energy supply performance data 133 is, for example, the amount of energy supplied by the heat source unit as chilled or hot water for air conditioning loads, or the amount of energy supplied by the hot water supply unit as hot water for hot water supply loads.

[0167] Next, in step ST402, only the data at 0:00 for each day of the energy supply performance data 133 obtained in step ST401 is extracted. This is to perform the processing in step ST403 (described later) for each time point, so that the data extracted at predetermined time intervals varies. Then, whenever the processing of step ST402 is repeated from step ST404 (described later), the time step is advanced by one. This repeated processing is performed during a predetermined period (e.g., from 0:00 to 24:00), but this period is not limited to a specific period such as one day.

[0168] Next, in step ST403, the actual energy supply values ​​extracted in step ST402 are summed to obtain the total actual energy supply value supplied by all energy devices 2 to building 1000. Then, the average value of the total actual energy supply value is calculated. This average value is the demand forecast value, calculated using the following formula (12).

[0169] [Number 12]

[0170]

[0171] Here, Q t Q(d,t) represents the demand forecast at time t, Q(d,t) represents the actual supply energy at time t d days ago, and N represents the number of days of the actual supply energy data 133 obtained in step ST401.

[0172] Next, in step ST404, it is determined whether the calculation up to step ST403 is complete with respect to a predetermined period. If the calculation up to step ST403 is complete with respect to the predetermined period, that is, if the calculation from... Figure 11 In the case of "Yes" in the illustrated step ST404 branch, proceed to... Figure 11 The illustrated step ST405. On the other hand, in the case where the calculation up to step ST403 regarding the predetermined period has not been completed, i.e., from... Figure 11 In the case of "No" in the illustrated step ST404 branch, return to Figure 11 The illustrated step ST402.

[0173] In step ST405, the calculated demand forecast values ​​for all time periods are output as the forecast results.

[0174] Figure 12 This is a flowchart illustrating an example of the processing flow of the demand forecasting unit 143 in this embodiment. Figure 11 The processing flow shown is different, in Figure 12 In the processing flow shown, demand forecasts are calculated using physical formulas, rather than by using average values ​​at each moment. For example, when calculating the amount of energy required by an air conditioner to handle the heat load, physical formulas for calculating heat can be used. Figure 12 The processing flow shown predicts the energy demand supplied to such an air conditioner.

[0175] First, in step ST501, indoor and outdoor environmental data 135, equipment operation performance data 136 related to calculations based on physical formulas from the multiple energy devices 2 in the energy supply system, and load model parameters 132 are acquired. As indoor and outdoor environmental data 135, weather forecast information such as external gas temperature or sunshine duration, as well as the most recently stored indoor temperature before the current time, are acquired from the storage device 13, but the acquisition period is not limited to these. As equipment operation performance data, the most recently stored data before the current time is acquired from the storage device 13.

[0176] Next, in step ST502, only the data for 0:00 of each day of the indoor / outdoor environmental data 135 and equipment operation performance data 136 obtained in step ST501 are extracted. This is to perform the processing in steps ST503 to ST508 (described later) for each time point, so that the data extracted at predetermined time intervals varies. Then, whenever the processing of steps ST502 to ST508 is repeated from step ST509 (described later), the time step is advanced by one. This repeated processing is performed during a predetermined period (e.g., 0:00 to 24:00), but this period is not limited to a specific period such as a day.

[0177] Next, in step ST503, the heat load from outside the building 1000 is calculated for both cooling and heating conditions as follows.

[0178] [Number 13]

[0179] Refrigeration

[0180] heating

[0181] Here, Q o A represents the external heat load from building 1000 at that moment. wi A represents the window area. waout U represents the area of ​​the outer wall. wi U represents the window thermal conductivity. waout R represents the thermal flux ratio of the outer wall, and T represents the solar radiation. o T represents the external gas temperature. set The target room temperature is represented by α, the window shading coefficient by α, the solar absorptivity by β, and the surface heat transfer rate by γ. d Indicates a time interval.

[0182] Next, in step ST504, the internal heat load of building 1000 is calculated for both cooling and heating conditions as follows.

[0183] [Number 14]

[0184]

[0185]

[0186] Here, Q in Q represents the heat load inside a 1000-meter building. human Q represents the human body's heat load. OA Q represents the heat load of the OA equipment. light Indicates the heat load of lighting equipment, x humanIndicates the number of people present, x OA Indicates the number of OA devices in use, x light Q indicates the number of lights on. in_human Q represents the total heat load on the human body. in_OA Q represents the total heat load of OA equipment. in_light This represents the total heat load of the lighting equipment.

[0187] Next, in step ST505, the heat load based on the ventilation device is calculated for both cooling and heating conditions as follows.

[0188] [Number 15]

[0189]

[0190]

[0191] Here, Q v V represents the heat load based on the ventilation system. v T represents the air exchange volume. set ρ represents the target room temperature. a C represents air density. a η represents the specific heat of air. v T represents the temperature exchange efficiency of the ventilation device. o t represents the temperature of the external gas. d Indicates a time interval.

[0192] Next, in step ST506, the heat load caused by the difference between the target room temperature and the indoor temperature is calculated for both cooling and heating conditions as follows.

[0193] [Number 16]

[0194] Refrigeration

[0195] heating

[0196] Here, Q t V represents the heat load caused by the difference between the target room temperature and the indoor temperature. area T represents the volume of this section. in Indicates indoor temperature, T set ρ represents the target room temperature. a C represents air density. a This indicates the specific heat of air.

[0197] Next, in step ST507, the heat load from the adjacent non-air-conditioned space is calculated for both cooling and heating conditions as follows.

[0198] [Number 17]

[0199] Refrigeration

[0200] heating

[0201] Here, Q n N represents the heat load based on the heat flow from the wall. n A represents the number of adjacent spaces. waini U represents the inner wall area. waini T represents the thermal conductivity of the inner wall. n T represents the temperature within adjacent spaces. set t represents the target room temperature. d Indicates a time interval.

[0202] In addition, regarding cooling and heating, the heat load from adjacent non-air-conditioned spaces can be calculated separately in the following ways.

[0203] [Number 18]

[0204] Refrigeration

[0205] heating

[0206] Here, Q p N represents the heat load generated through the air. p V represents the number of adjacent spaces. p T represents the airflow rate. p T represents the temperature within adjacent spaces. set ρ represents the target room temperature. a C represents air density. a t represents the specific heat of air. d Indicates a time interval.

[0207] Next, in step ST508, the heat loads calculated in steps ST503 to ST507 are summed up to obtain the energy demand forecast for supplying building 1000. This demand forecast is calculated using the following equation (13).

[0208] [Number 19]

[0209] Q hvac_area =Q o +Q v +Q t +Q in +Q n +Q p …(13)

[0210] Here, Qhvac_area This is the demand forecast.

[0211] Next, in step ST509, it is determined whether the calculation of the demand forecast for the predetermined period has been completed. If the calculation of the demand forecast for the predetermined period has been completed, that is, if it is consistent with the calculation of the demand forecast from... Figure 12 In the case of "Yes" in the illustrated step ST509 branch, proceed to... Figure 12 The illustrated step ST510. On the other hand, in the case where the calculation of the demand forecast for the predetermined period is not completed, i.e., from... Figure 12 In the case of "No" in the illustrated step ST509 branch, return to Figure 12 The illustrated step ST502.

[0212] In step ST510, the calculated demand forecast values ​​for all time periods are output as the demand forecast results.

[0213] Figure 13 This is a flowchart illustrating an example of the processing flow of the supply planning unit 144 in this embodiment. Figure 13 The processing shown is mainly by Figure 2 The optimization engine 144b is used in the process.

[0214] First, in step ST601, the cost price and control target value for electricity and gas (or one of them) are set. The control target value is the upper limit of the cost price incurred for electricity and gas (or one of them) consumed to supply energy. Furthermore, any value can be set for the control target value.

[0215] Next, in step ST602, the device characteristic value data 131 of all energy devices 2 in the energy supply system and the demand forecast value output by the demand forecasting unit 143 are obtained.

[0216] Next, in step ST603, an optimization problem for electricity costs and fuel costs is constructed. This optimization problem can be represented by the following equation (14).

[0217] [Number 20]

[0218]

[0219] Here, t represents time, p price (t) represents the electricity purchase price per unit at time t, p buy (t) represents the amount of electricity purchased at time t, g price (t) represents the fuel purchase price per unit at time t, g buy (t) represents the amount of fuel purchased at time t.

[0220] Next, in step ST604, an energy supply plan is formulated by setting the amount of energy supplied by all energy devices 2 in the energy supply system at each time. This setting is referenced when the amount of energy supplied is set in step ST607 described later.

[0221] Next, in step ST605, the cost required for energy supply when operating energy device 2 according to the energy supply plan established in step ST604 is calculated. For this calculation, the optimization problem of electricity cost and fuel cost prepared in step ST603 is used (refer to equation (14)).

[0222] Next, in step ST606, it is determined whether the energy supply plan established in step ST604 and the cost required for energy supply calculated in step ST605 respectively meet the constraints. Here, the constraints refer to the control target value set in step ST601 and the demand forecast value output by the demand forecasting unit 143. In addition, "meeting the constraints" means that the cost required for energy supply is below the control target value, or the amount of energy supplied based on the energy supply plan is above the demand forecast value.

[0223] When both the energy supply plan and the cost of energy supply meet the constraints, i.e., with [the energy supply plan]... Figure 13 In the case of "Yes" in the illustrated step ST606 branch, proceed to... Figure 13 The illustrated step ST607. On the other hand, the situation where either the energy supply plan or the cost required for energy supply fails to meet the constraints, i.e., from... Figure 13 In the case of "No" in the illustrated step ST606 branch, proceed to... Figure 13 The illustrated step ST608.

[0224] In step ST607, it is determined whether the cost required for energy supply is minimized. Minimizing the cost required for energy supply can be determined, for example, by repeatedly performing cost calculations, and at a time point when a predetermined upper limit for the number of repeated calculations is reached, exploring the cost that is the minimum among the previously calculated costs; or by setting the calculated cost as the minimum cost at a time point when it is lower than a predetermined cost target value.

[0225] In the case where the cost of energy supply is minimized, i.e., from Figure 13 In the case of "Yes" in the illustrated step ST607 branch, proceed to... Figure 13 The illustrated step ST609. On the other hand, in cases where the cost required for energy supply is not minimized, i.e., compared to... Figure 13 In the case of "No" in the illustrated step ST607 branch, proceed to... Figure 13 The illustrated step ST608.

[0226] In step ST608, the amount of energy supplied by each energy device 2, as set in step ST604, is changed, or the amount of energy supplied by each energy device 2, as changed in the previous step ST608, is changed. Then, the process returns to step ST604. The method for changing the amount of energy is, for example, based on a common quadratic programming method. However, the method for changing the amount of energy is not limited to this method.

[0227] In step ST609, the energy supply plan formulated in step ST604 is output to the control command conversion unit 145.

[0228] <Hardware Structure of Energy Supply Planning Device>

[0229] Figure 14 as well as Figure 15 This is a brief example of practical application. Figure 2 as well as Figure 5 A diagram illustrating the hardware structure of the energy supply planning device.

[0230] also, Figure 14 as well as Figure 15 The illustrated hardware structures sometimes have a number of... Figure 2 as well as Figure 5 The structures shown are inconsistent, but this is because Figure 2 as well as Figure 5 The structures shown are derived from conceptual units.

[0231] Therefore, at least it is possible to conceive of Figure 2 as well as Figure 5 One of the structures illustrated includes Figure 14 as well as Figure 15 The examples of various hardware architectures and Figure 2 as well as Figure 5 The example structure and Figure 14 as well as Figure 15 The illustrated hardware structure corresponds to a portion of the situation, which in turn allows us to conceive of... Figure 2 as well as Figure 5 The illustrated structures are equipped with Figure 14 as well as Figure 15 The example illustrates one hardware architecture.

[0232] exist Figure 14 In, as a means of implementation Figure 2 as well as Figure 5The hardware structure of the processing unit 14 (correction need determination unit 141, parameter calculation unit 142, demand forecasting unit 143, supply planning unit 144, and control command conversion unit 145) and the storage device 13 is shown, including the processing circuit 1102A that performs calculations and the storage device 1103 that can store information.

[0233] exist Figure 15 In, as a means of implementation Figure 2 as well as Figure 5 The hardware structure of the arithmetic unit 14 (the functional units of the arithmetic unit 141, the parameter calculation unit 142, the demand forecasting unit 143, the supply planning unit 144, and the control command conversion unit 145) and the storage unit 13 is shown, and the processing circuit 1102B that performs the calculation is shown.

[0234] Storage device 13 is implemented by storage device 1103 or other storage devices (not shown here).

[0235] Storage device 1103 may be, for example, a hard disk drive (HDD), random access memory (RAM), read-only memory (ROM), flash memory, erasable programmable read-only memory (EPROM), and electrically erasable programmable read-only memory (EEPROM), including volatile or non-volatile semiconductor memory, magnetic disks, flexible optical disks, optical disks, compressed optical disks, mini-optical disks, or DVDs, or any storage medium to be used in the future.

[0236] The processing circuit 1102A can also execute programs stored in the storage device 1103, an external CD-ROM, an external DVD-ROM, or an external flash memory. That is, for example, it can be a central processing unit (CPU), a microprocessor, a microcomputer, or a digital signal processor (DSP).

[0237] When the processing circuit 1102A executes a program stored in the storage device 1103, an external CD-ROM, an external DVD-ROM, or an external flash memory, the arithmetic unit 14 is implemented by the processing circuit 1102A executing software, firmware, or a combination of software and firmware of the program stored in the storage device 1103. Furthermore, the individual functional units of the arithmetic unit 14 (correction need determination unit 141, parameter calculation unit 142, demand forecasting unit 143, supply planning unit 144, and control command conversion unit 145) can, for example, be implemented by the cooperation of multiple processing circuits.

[0238] Software and firmware can also be described as programs and stored in storage device 1103. In this case, processing circuit 1102A reads the program stored in storage device 1103 and executes it to achieve the above-mentioned functions. That is, storage device 1103 can also store programs that achieve the above-mentioned functions by being executed by processing circuit 1102A.

[0239] Alternatively, the processing circuit 1102B can also be dedicated hardware. That is, for example, it can be a single circuit, a composite circuit, a programmable processor, a parallel programmable processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination thereof.

[0240] When the processing circuit 1102B is dedicated hardware, the arithmetic unit 14 is implemented by operating through the processing circuit 1102B. Furthermore, the functions of the arithmetic unit 14 can be implemented either by separate circuits or by a single circuit.

[0241] Furthermore, the functions of the arithmetic unit 14 can also be implemented partly in the processing circuit 1102A that executes the program stored in the storage device 1103, and partly in the processing circuit 1102B that is dedicated hardware.

[0242] <Regarding the effects produced by the implementation methods described above>

[0243] Next, examples of the effects produced by the embodiments described above will be shown. Furthermore, in the following description, the effects are described based on the specific structures illustrated in the embodiments described above; however, other specific structures illustrated in this specification may be substituted to produce the same effects.

[0244] According to the embodiments described above, the energy supply planning device includes an acquisition unit, a determination unit 141c, a correction unit, a demand forecasting unit 143, and a supply planning unit 144. Here, the acquisition unit corresponds, for example, to a characteristic change acquisition unit 141b. The correction unit corresponds, for example, to a parameter calculation unit 142. Here, the equipment model 144a is data representing the energy input and output characteristics of the corresponding energy equipment 2. The load model 143a is a physical formula or calculation method for predicting the energy demand of the corresponding energy supply target. Here, the energy supply target corresponds, for example, to a building 1000. Then, the characteristic change acquisition unit 141b acquires changes in the basic load of the energy equipment 2. Furthermore, the determination unit 141c determines, based on the changes in the basic load, whether correction of at least one of the equipment model 144a and the load model 143a is needed. Furthermore, the parameter calculation unit 142 corrects at least one of the equipment model 144a and the load model 143a based on the determination result in the determination unit 141c. Finally, the demand forecasting unit 143 predicts the energy demand of the building 1000. In addition, the supply planning department 144 formulates an energy supply plan based on the equipment model 144a and the energy demand.

[0245] Furthermore, according to the embodiments described above, the energy supply planning device includes a processing circuit 1102A for executing a program and a storage device 1103 for storing the executed program. The processing circuit 1102A then executes the program to perform the following operations.

[0246] That is, the change in the basic load of energy device 2 is obtained. Then, based on the change in basic load, it is determined whether at least one of device model 144a and load model 143a needs correction. Then, based on the determination result, at least one of device model 144a and load model 143a is corrected. Then, the energy demand of building 1000 is predicted. Then, an energy supply plan is formulated based on device model 144a and energy demand.

[0247] Furthermore, according to the embodiments described above, the energy supply planning device includes a processing circuit 1102B as dedicated hardware. Then, the processing circuit 1102B, as dedicated hardware, performs the following operations.

[0248] That is, the processing circuit 1102B, as dedicated hardware, acquires changes in the basic load of the energy device 2. Then, based on the changes in the basic load, it determines whether correction is needed for at least one of the device model 144a and the load model 143a. Then, based on the determination result, at least one of the device model 144a and the load model 143a is corrected. Then, the energy demand of the building 1000 is predicted. Then, an energy supply plan is formulated based on the device model 144a and the energy demand.

[0249] Based on this structure, the model (at least one of the equipment model 144a and the load model 143a) is corrected according to changes in the basic load of the energy device 2, and an energy supply plan can be formulated based on this model. Therefore, even in the event of characteristic changes associated with changes in equipment structure or performance degradation of the energy device 2, errors related to the energy supply plan can be reduced. Furthermore, by supplying energy according to an energy supply plan formulated with high accuracy, costs related to energy supply in thermoelectric combined supply type energy supply systems can be reduced, and energy can be supplied stably.

[0250] Furthermore, the same effect can be achieved even when other structures illustrated in this application are appropriately added to the above structure, that is, when other structures not mentioned in this application are appropriately added as the above structure.

[0251] Furthermore, according to the embodiment described above, the characteristic change acquisition unit 141b acquires the change in basic load based on the difference between the operating time value and the theoretical value at each moment. The operating time value is calculated based on supply energy performance data 133, which is time-series data representing performance related to the energy supplied to the building 1000, and consumption performance data 134, which is time-series data representing performance related to the energy consumption of the energy device 2. The theoretical value is calculated based on environmental data related to the internal and external environment of the building 1000 and equipment operation performance data, which is time-series data representing performance related to the operating state of the energy device 2. With this structure, it is not limited to the equipment characteristic value data 131, and it is also possible to determine whether the parameters of other models need correction. For example, the determination unit 141c can determine the magnitude of the prediction error of the load model 143a, and it is possible to determine whether the parameters of the load model 143a need correction.

[0252] Furthermore, according to the embodiment described above, the characteristic change acquisition unit 141b performs frequency analysis on the supply energy performance data 133, which is time-series data representing performance related to the energy supplied to the building 1000, extracts the basic load at multiple times, and then obtains the change in the basic load based on the difference between the extracted basic loads. With this structure, it is possible to determine whether the equipment model 144a needs correction based on the change in the basic load.

[0253] Furthermore, according to the embodiments described above, the parameter calculation unit 142 corrects at least one of the equipment model 144a and the load model 143a using a random exploration method or a regression formula. In the random exploration method, based on the actual energy supply data 133, the parameters of the equipment model 144a or the load model 143a are made to have random deviations to predict the energy demand of the building 1000 multiple times, and the parameters that are closest to the actual energy supply data 133 are explored. In the regression formula, the parameters of the equipment model 144a or the load model 143a are used as explanatory variables, and the energy supplied to the building 1000 is used as the target variable. Based on this structure, the model is corrected, thereby enabling the formulation of an energy supply plan based on the corrected model. Therefore, even in the event of changes in characteristics associated with changes in equipment structure or performance degradation of the energy equipment 2, errors related to the energy supply plan can be reduced.

[0254] Furthermore, according to the embodiments described above, the demand forecasting unit 143 forecasts the energy demand of the building 1000 based on the average value of the energy supplied to the building 1000 calculated from the energy supply performance data 133, or the heat load calculated from the load model 143a, the operating data of the energy equipment 2 (equipment operating performance data 136), and the indoor and outdoor environmental data 135 related to the indoor and outdoor environment of the building 1000. With this structure, an energy supply plan that meets the energy demand can be formulated.

[0255] Furthermore, according to the embodiments described above, the supply planning unit 144 formulates an energy supply plan by deriving a solution to an optimization problem that satisfies the energy demand and minimizes the cost required to supply energy to the building 1000. With this structure, an energy supply plan can be formulated that suppresses the cost required to supply energy to the building 1000.

[0256] Furthermore, according to the embodiments described above, the energy device 2 includes at least one of a power supply device 21, a power exchange device 22, a heat supply device 23, and a heat exchange device 24. With this structure, the model can be corrected based on changes in the basic load of the energy device 2, and an energy supply plan can be formulated based on this model.

[0257] Furthermore, according to the embodiments described above, the device model 144a is represented as a graph or table showing the energy input and output characteristics of the corresponding energy device 2. With this structure, the model can be corrected according to changes in the basic load of the energy device 2, and an energy supply plan can be formulated based on this model.

[0258] According to the embodiments described above, in the energy supply planning method, the change in the basic load of the energy equipment 2 is obtained. Then, based on the change in the basic load, it is determined whether correction is needed for at least one of the equipment model 144a and the load model 143a. Then, based on the determination result, at least one of the equipment model 144a and the load model 143a is corrected. Then, the energy demand of the building 1000 is predicted. Then, an energy supply plan is formulated based on the equipment model 144a and the energy demand.

[0259] Based on this structure, the model (at least one of the equipment model 144a and the load model 143a) can be corrected according to changes in the basic load of the energy device 2, and an energy supply plan can be formulated based on this model. Therefore, even in the event of characteristic changes associated with changes in equipment structure or performance degradation of the energy device 2, errors related to the energy supply plan can be reduced.

[0260] Furthermore, the order in which the various processes are performed can be changed without any particular restrictions.

[0261] Furthermore, the same effect is produced even when other structures illustrated in this application are appropriately added to the above structure, that is, when other structures in this application not mentioned as the above structure are appropriately added.

[0262] <Regarding variations of the embodiments described above>

[0263] In the embodiments described above, the dimensions, shapes, relative configurations, or implementation conditions of each constituent element are sometimes described, but these are merely examples and not limitations in all respects.

[0264] Therefore, numerous variations and equivalents not illustrated are contemplated within the scope of the technology disclosed in this application. These include, for example, variations, additions, or omissions of at least one constituent element.

[0265] Furthermore, in the embodiments described above, if the material name is not specifically specified, other additives, such as alloys, are included in the material as long as there is no contradiction.

[0266] Furthermore, as long as there is no contradiction, the constituent element described as having "one" in the above-described embodiments may also have "more than one".

[0267] Furthermore, the descriptions in this application specification are referenced for all purposes related to this technology and are not considered to be prior art.

[0268] Furthermore, the constituent elements described in the embodiments described above can be conceived as software or firmware, or as corresponding hardware. In both of these concepts, each constituent element is referred to as a "unit" or "processing circuit" or the like.

[0269] In addition, the technology disclosed in this application specification can also be a case where the various constituent elements are distributed among multiple devices, that is, in the form of a system as a combination of multiple devices.

[0270] For example, storage device 13 is configured to be in Figure 2 It is shown as a functional unit housed within an energy supply planning device, but it can also be an external functional unit. In this case, other functional units within the energy supply planning device interact with the external functional units, thereby enabling the energy supply planning device to function as a whole.

Claims

1. An energy supply planning device, comprising formulating an energy supply plan for controlling the operation of at least one energy device for inputting and outputting energy to an energy supply object based on a device model corresponding to the energy device and a load model corresponding to the energy supply object, wherein, The device model is data representing the energy input and output characteristics of the corresponding energy device. The load model is a physical formula or calculation method used to predict the energy demand of the corresponding energy supply object. The energy supply planning device includes: The acquisition unit acquires the changes in the basic load of the energy device by performing frequency analysis on the time series data of the at least one energy device. The determination unit determines, based on the change in the base load, whether the parameters of at least one of the equipment model and the load model need to be corrected, so as to ensure the accuracy of the energy supply plan based on the change in the base load. The calibration unit, based on the determination result in the determination unit, corrects the parameters of at least one of the equipment model and the load model, so as to adjust the physical formula or calculation method by changing the basic load; The demand forecasting unit uses at least one of the corrected equipment model and the load model to predict the energy demand of the energy supply object. as well as The supply planning department formulates an energy supply plan based on the equipment model and the energy demand. The acquisition unit performs frequency analysis on the supply energy performance data, which is time-series data representing the performance related to the energy supplied to the energy supply object, thereby extracting the basic load at multiple times, and then obtaining the change of the basic load based on the difference between the extracted basic loads.

2. The energy supply planning device according to claim 1, wherein, The acquisition unit acquires the change in the basic load based on the difference between the operating time value and the theoretical value at each moment. The operating time value is calculated based on supply energy performance data, which is time series data representing the performance related to the energy supplied to the energy supply object, and consumption performance data, which is time series data representing the performance related to the energy consumption of the energy equipment. The theoretical value is calculated based on environmental data related to the internal and external environment of the energy supply object and equipment operation performance data, which is time series data representing the performance related to the operating state of the energy equipment.

3. The energy supply planning device according to claim 2, wherein, The correction unit corrects at least one of the equipment model and the load model using a random exploration method or a regression formula. In the random exploration method, the energy demand of the energy supply object is predicted multiple times by randomly biasing the parameters of the equipment model or the load model based on the actual energy supply data, exploring the parameter that is closest to the actual energy supply data. In the regression formula, the parameters of the equipment model or the load model are used as explanatory variables, and the energy supplied to the energy supply object is used as the target variable.

4. The energy supply planning device according to claim 1, wherein, The correction unit corrects at least one of the equipment model and the load model using a random exploration method or a regression formula. In the random exploration method, the energy demand of the energy supply object is predicted multiple times by randomly biasing the parameters of the equipment model or the load model based on the actual energy supply data, exploring the parameter that is closest to the actual energy supply data. In the regression formula, the parameters of the equipment model or the load model are used as explanatory variables, and the energy supplied to the energy supply object is used as the target variable.

5. The energy supply planning apparatus according to any one of claims 2 to 4, wherein, The demand forecasting unit predicts the energy demand of the energy supply object based on the average value of the energy supplied to the energy supply object calculated from the actual energy supply data, or the heat load calculated from the load model, the operating data of the energy equipment, and environmental data related to the internal and external environment of the energy supply object.

6. The energy supply planning apparatus according to any one of claims 2 to 4, wherein, The supply planning department derives a solution to the optimization problem that satisfies the energy demand and minimizes the cost required to supply the energy to the energy supply object, thereby formulating the energy supply plan.

7. The energy supply planning apparatus according to any one of claims 2 to 4, wherein, The energy equipment includes at least one of the following: power supply equipment, power exchange equipment, heat supply equipment, and heat exchange equipment.

8. The energy supply planning apparatus according to any one of claims 2 to 4, wherein, The device model is represented as a graph or table showing the energy input and output characteristics of the corresponding energy device.

9. A method for formulating an energy supply plan, wherein an energy supply plan for controlling the operation of at least one energy device for inputting and outputting energy to an energy supply object is formulated based on an equipment model corresponding to the energy device and a load model corresponding to the energy supply object, wherein, The device model is data representing the energy input and output characteristics of the corresponding energy device. The load model is a physical formula or calculation method used to predict the energy demand of the corresponding energy supply object. The changes in the basic load of the energy device are obtained by performing frequency analysis on the time series data of the at least one energy device. Based on the changes in the base load, determine whether the parameters of at least one of the equipment model and the load model need correction to ensure the accuracy of the energy supply plan based on the changes in the base load. Based on the determination result, the parameters of at least one of the equipment model and the load model are corrected to adjust the physical formula or calculation method according to the change of the basic load. The energy demand of the energy supply object is predicted using at least one of the corrected equipment model and the load model. An energy supply plan is formulated based on the equipment model and the energy requirements. Frequency analysis is performed on the supply energy performance data, which is time series data representing the performance related to the energy supplied to the energy supply object, to extract the basic load at multiple times, and then the change of the basic load is obtained based on the difference between the extracted basic loads.

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