JP2012242067A - Consumed energy calculator, consumed energy calculating method, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To reduce parameters required for estimating an amount of the power consumption of a cold device while precisely estimating the amount of the power consumption of the cold device.SOLUTION: A consumed energy calculator 1 is a device for calculating an amount of the power consumption of the cold device which controls a temperature of a space defined in a building. A first heat load analysis part 12 uses a physical model in which building information 51, heating element information 52, environment information 53, and operation information 54 are used as input information 5 to find a heat load of the space. The building information 51 includes parameters on the building, and the heating element information 52 includes parameters on a heating element existing in the building. The environment information 53 includes parameters on an environment of an installation location of the building, and the operation information 54 includes parameters on an operation of the cold device. A first power consumption amount estimation part 14 estimates an amount of power consumption corresponding to a heat load found by the first heat load analysis part 12 from cold device characteristics 131 which correlate the heat load with the amount of power consumption.

Description

  The present invention relates to an energy consumption calculation device that calculates power consumption of a cooling device that controls the temperature of a predetermined space in a building, a method for calculating energy consumption, and means for calculating the power consumption of the cooling device. The present invention relates to a program for causing a computer to function.

  In recent years, social demands for energy saving have increased with global warming. The current energy conservation law requires the civilian sector not only to improve the performance of a single device, but also to save energy in terms of management using, for example, BEMS (Building Energy Management System). The target of such a management system is expanding from large buildings to small and medium-sized buildings that do not have specialized managers, and a system design that can effectively implement energy-saving management and tools to support it Equipment is required. In addition, various demonstration experiments have been conducted as energy saving practice methods for small and medium buildings. The knowledge gained from the results of these demonstration experiments is that it is an effective way to save energy because it is effective for energy saving and does not require a large initial investment. It can be said that there is. However, in order to practice such management, it is not enough to improve the efficiency of the equipment, but it is indispensable to promote it with at least some cooperation from the user (administrator, etc.) side.

  Patent Document 1 discloses a system that estimates a predetermined state quantity of an air conditioning system based on a physical quantity of a working fluid for operating the air conditioning system.

JP 2008-232507 A

  However, the persuasive material for obtaining cooperation from the user (administrator, etc.) is the energy saving effect as described above. Conventionally, a simple simulation tool for estimating the energy saving effect has been sufficiently prepared. It wasn't.

  That is, in order to persuade the user when introducing the energy saving system, it is necessary to present the energy saving effect by the introduction, but the influence due to the building characteristics is not considered in the conventional simulation tool. For this reason, the estimation results were the same for all buildings and the persuasive power was low.

  In view of these circumstances, there is a need for a tool that does not require high-level input but can expect an energy-saving effect in accordance with actual building conditions (for example, operation data and past power consumption data).

  However, most of the simulation software that considers building characteristics has a function to calculate the energy-saving effect, but it is built to determine the building design conditions and specializes in estimating the energy-saving effect. It has not been. For this reason, the simulation software requires the user to input a large amount of parameters that are not important from the viewpoint of operational improvement, and is not suitable as operational improvement software. In particular, the simulation related to the air conditioner requires a very large number of input parameters because of its complicated configuration, and further, expert knowledge is required for input work.

  On the other hand, for example, a statistical model such as multiple regression analysis has been known for a long time as a method for reducing the input variables in accordance with the actual usage of energy. For example, the air conditioner is one system, the output condition is the power consumption of the air conditioner, the input condition is the weather condition and the operation condition, the relationship between the input data and the output data is quantitatively evaluated, and the fluctuation of the output data There is a method of selecting input conditions that greatly contribute to. However, since the above method does not describe any process up to the estimation of power consumption, it has not been possible to simulate the change of input conditions (for example, change of set temperature or change of operation time).

  The present invention has been made in view of the above points, and an object of the present invention is to accurately estimate the power consumption of the cooling device while reducing the parameters necessary for estimating the power consumption of the cooling device. The present invention provides a consumption energy calculation device, a consumption energy calculation method, and a program.

  The energy consumption calculation device of the present invention is a energy consumption calculation device for calculating the amount of power consumption of a cooling device for controlling the temperature of a predetermined space in a building, wherein the building information includes parameters related to the building, A physical model using as input information heating element information including parameters relating to heating elements existing in the building, environmental information including parameters relating to the environment of the installation location of the building, and operation information including parameters relating to operation of the cooling and heating apparatus. The thermal load obtained by the first thermal load analysis unit from the first thermal load analysis unit that obtains the thermal load of the space and the cooling device characteristics that associates the thermal load and the power consumption. And a first power consumption estimation unit that estimates the power consumption corresponding to the load.

  In this energy consumption calculation device, a parameter changing unit that changes at least one parameter in the input information within a predetermined range, and an optimum power consumption amount when the parameter is changed by the parameter changing unit An optimization processing unit for obtaining a value, and each time the parameter is changed by the parameter changing unit, the first heat load analyzing unit uses the input information in which the parameter has been changed. The first power consumption estimation unit obtains the power consumption corresponding to the thermal load obtained by the first thermal load analysis unit for each of the input information in which the parameter has been changed. The optimization processing unit estimates a plurality of previous values estimated by the first power consumption estimation unit for each input information in which the parameter is changed. It is preferable to extract the minimum value of the power consumption amount as the optimum value.

  In this energy consumption calculation apparatus, a second heat load data group is obtained by using the building information, the actual data group of the heating element information, the actual data group of the environmental information, and the actual data group of the operation information. Using a statistical model that statistically correlates the past heat load data group obtained by the second heat load analysis unit and the past power consumption result data group obtained by the second heat load analysis unit. It is preferable to include a statistical analysis unit that obtains the characteristics of the cooling / heating device.

  In this energy consumption calculation device, a second power consumption estimation unit that estimates the power consumption of another device different from the cooling device, and the other device estimated by the second power consumption estimation unit It is preferable to provide a total power amount calculation unit that obtains the sum of the power consumption amount and the power consumption amount of the cooling / heating device as a total power amount.

  In this energy consumption calculation device, the statistical analysis unit calculates a total power amount that is a sum of a past power consumption amount of the cooling device and a past power consumption amount of another device different from the cooling device. It is preferable to create the characteristics of the cooling / heating device as a data group with power consumption.

  The method for calculating energy consumption of the present invention is a method for calculating energy consumption for calculating power consumption of a cooling device that controls the temperature of a predetermined space in a building, wherein the building information includes parameters related to the building, A physical model using as input information heating element information including parameters relating to heating elements existing in the building, environmental information including parameters relating to the environment of the installation location of the building, and operation information including parameters relating to operation of the cooling and heating apparatus. The power consumption corresponding to the thermal load determined in the first step from the first step of determining the thermal load of the space using the refrigeration apparatus characteristics in which the thermal load and the power consumption are associated with each other And a second step of estimating the quantity.

  The program according to the present invention includes a computer, building information including parameters related to the building, and a computer in order to calculate power consumption of a cooling device that controls the temperature of a predetermined space in the building. Using a physical model that uses as input information heating element information including parameters relating to the heating element, environmental information including parameters relating to the environment of the installation location of the building, and operation information including parameters relating to operation of the cooling and heating apparatus, The power consumption corresponding to the thermal load obtained by the thermal load analysis means is estimated from the thermal load analysis means for obtaining the thermal load of the space and the cooling device characteristics in which the thermal load is associated with the power consumption. It is a program for functioning as a power consumption amount estimation means.

  In the present invention, when estimating the power consumption of the refrigeration system, the analysis up to the analysis of the thermal load in the space is made an analysis by a physical model, and the process after the analysis of the thermal load is associated with the heat load and the power consumption. Estimate the amount of power consumed by the characteristics of the cooling and heating equipment. Thereby, in this invention, the power consumption of a cooling device can be estimated accurately, reducing the parameter required in order to estimate the power consumption of a cooling device.

It is a block diagram which shows the structure of the consumption energy calculation apparatus which concerns on Embodiment 1. FIG. It is a block diagram of a cooling device. It is a figure explaining space heat load. It is a figure which shows the cooling-heat apparatus characteristic used with the consumption energy calculation apparatus which concerns on Embodiment 1. FIG. 4 is a flowchart illustrating an operation of calculating a cooling / heating device characteristic by the energy consumption calculating device according to the first embodiment. 4 is a flowchart illustrating an operation of optimization processing by the energy consumption calculation apparatus according to the first embodiment. It is a figure which shows the cooling-heat apparatus characteristic used with the consumption energy calculation apparatus of the modification of Embodiment 1. FIG. It is a block diagram which shows the structure of the consumption energy calculation apparatus which concerns on Embodiment 2. FIG. It is a block diagram which shows the structure of the consumption energy calculation apparatus which concerns on Embodiment 3. FIG.

  In the following first to third embodiments, an energy consumption calculation device that calculates the amount of power consumption of a cooling device that controls the temperature of a predetermined space in a building will be described.

(Embodiment 1)
As illustrated in FIG. 1, the energy consumption calculation apparatus 1 according to the first embodiment includes a first input unit 11, a first thermal load analysis unit 12, a storage unit 13, and a first power consumption amount estimation unit. 14, a second input unit 15, a second thermal load analysis unit 16, and a statistical analysis unit 17.

  The process for estimating the power consumption of the cooling device 2 (see FIG. 2) includes the process of estimating the heat load amount (hereinafter referred to as “space heat load”) 4 of the space 3 (see FIG. 2), and the space heat load 4 And a process for estimating power consumption required for processing. The space heat load 4 is constituted by the amount of heat generated by various factors such as the indoor / outdoor environment and the operating conditions of the cooling / heating device 2.

  The energy consumption calculation device 1 according to the present embodiment describes the process of estimating the power consumption of the cooling / heating device 2 by describing the process part related to the parameters used for operational improvement with a physical model and using the statistical model for other processes. A (hybrid) model is being built.

  The cooling device 2 is, for example, an air conditioner or a refrigeration device. In this embodiment, the air conditioner whose phenomenon is more complicated than that of the refrigeration apparatus is targeted.

  As shown in FIG. 3, the space heat load 4 includes, for example, through heat 41 that flows through a wall, solar heat 42 that passes through a window surface, internal heat generation 43 of a heating element in the space 3, and outside air due to intake outside air. The load 44 is a heat load amount to be processed by the cooling / heating device 2 (air conditioner). As shown in FIG. 2, the space heat load 4 includes air in the space 3 taken into the cooling device 2 by the fans 21 and 22, processed by a coil 23 through which cold water / hot water flows, and a heat source (air cooling in FIG. 2). HP) 24 and heat exchanger 25. Finally, heat is radiated to the atmosphere in the cooling tower 26. In this process, power is consumed by the fans 21, 22, the heat source 24, the cooling tower 26, and the pumps 27, 28, and 29 to convey air and water and to compress / expand the air with the heat source 24. The

  The first input unit 11 shown in FIG. 1 is used when the user inputs the input information 5 used in the first thermal load analysis unit 12. Specifically, the first input unit 11 includes building information 51, heating element information 52, and the like. Environmental information 53 and operational information 54 are input.

  The first heat load analysis unit 12 uses the physical model in which the building information 51, the heating element information 52, the environmental information 53, and the operation information 54 are input information 5, and the spatial heat load 4 (space 3 (see FIG. 2)). Heat load).

  The building information 51 is information including parameters related to the building, and is information relating to, for example, the amount of outside air, the heat insulating property / area of walls, the heat insulating property / area of windows, or a combination thereof. The spatial heat load 4 is greatly influenced by building frame information such as the floor plan of the building, the height of the ceiling, the wall structure, and the opening structure (window area, window heat insulation). In the present embodiment, a building structure such as an actual food supermarket or office is based on a patterned building structure on the assumption that a similar pattern is constructed within the same industry. The difference in the thermal load characteristics of each building is expressed by at least one parameter of, for example, the area of the building space 3 and the heat insulation level / opening degree of the wall structure (wall / window). As described above, by using the building information 51 based on the patterned building structure in the same industry, the input parameters can be narrowed down to at least one of the area of the building space 3 and the wall structure, for example. Thereby, the input operation for estimating the space heat load 4 can be facilitated.

The heating element information 52 is information including parameters regarding a plurality of heating elements existing in the building. Each heating element is, for example, an OA (Office Automation) device, a lighting device, or a human body. The plurality of heating elements may be a combination of different types (for example, at least two of OA equipment, lighting equipment, and human body), or all of the same type (for example, either OA equipment, lighting equipment, or human body). Also good. Since the heating element directly radiates heat into the space 3, it greatly affects the space heat load 4. By the way, the heating element information 52 can be patterned within the same industry. For example, the basic unit (heat load per unit area arranged for each application, for example, 12 W / m ^{2 in} the case of office lighting). ) And the product of the operation pattern and floor area for each application. The heating element information 52 may be information including a parameter related to one heating element existing in the building, instead of a plurality of heating elements existing in the building.

  The environment information 53 is information including parameters related to the environment of the installation location of the building. The environmental information 53 includes weather conditions (outside air temperature, humidity and amount of solar radiation) and indoor environment conditions (indoor temperature and humidity). As for the environmental information 53, for example, standard weather obtained by statistically collecting and standardizing data measured by sensors installed inside and outside the building, past weather data of the area where the building is located, or weather data for several years in the area. It can be obtained from data.

  The operation information 54 is information including a plurality of parameters related to the operation of the cooling / heating device 2. The plurality of parameters related to the operation of the cooling / heating device 2 include, for example, store operating hours / equipment operating time (schedule) associated with office hours, the set temperature of the cooling / heating device 2, and the operating state of the cooling / heating device 2 (cooling operation state / heating operation state) ) Etc. are used. The operation information 54 can be obtained by reading from the waveform characteristics of past power data or by hearing the user. The operation information 54 may be information including one parameter regarding the operation of the cooling / heating device 2 instead of a plurality of parameters regarding the operation of the cooling / heating device 2.

  There are three calculation methods for obtaining the spatial heat load 4 in the first heat load analysis unit 12. As a first calculation method, there is a method for estimating all heat loads to be processed by the cooling / heating device 2 by a simulation model based on a calculation principle of heat transfer calculation. This method is a calculation method similar to that of commercially available simulation software. As a theoretical calculation method, for example, a response factor method and a dynamic air conditioning heat load calculation method (Yama Matsuo) are generally disclosed.

  As a second calculation method, a load component that is decomposed into load components constituting the space heat load 4 and is generated for each item related to operation (for example, schedule information or set temperature) is generated. There is a method of describing and expressing the above in a physical model (see Dynamic Air Conditioning Thermal Load Calculation Method). By describing the estimation procedure of load components including items necessary for operational improvement in accordance with physical principles, it is possible to evaluate the increase or decrease in thermal load necessary for operational improvement.

For example, the solar radiation load Q1, the heat load Q2 due to room temperature fluctuation, and the heat load Q3 due to ventilation / throughflow heat are expressed as follows.
Solar radiation load Q1: Q1 = I * SC * A (1)
I: Solar radiation acquisition rate of standard 3 mm glass, SC: solar radiation shielding coefficient, A: glass area Thermal load due to room temperature fluctuation Q2: Q2 = ΣWZj * t (−j) (2)
WZ: Response factor with respect to room temperature displacement, t (-j): Temperature change in the past j hours Heat load due to ventilation / throughflow heat Q3: Q3 = ΣGj * φ (−j) (3)
φ: Response factor for ventilation and once-through heat, G (-j): Ventilation and once-through heat for the past j hours

  As a third calculation method, the standard heat load is estimated by the product of the basic unit (heat load per area) of the spatial heat load 4 patterned for each industry and the operation pattern and floor area for each application. There is a way. About the difference in building characteristics, for example, the spatial heat load 4 can be calculated by correcting with a physical model such as Equations (1) to (3).

  The memory | storage part 13 has memorize | stored the cooling-heat apparatus characteristic 131 which matched thermal load and power consumption. The cooling / heating device characteristic 131 of the present embodiment is a characteristic obtained by the statistical analysis unit 17. A method for obtaining the cooling / heating device characteristics 131 will be described later.

  The first power consumption amount estimation unit 14 estimates the power consumption amount corresponding to the spatial heat load 4 obtained by the first heat load analysis unit 12 from the cooling device characteristics 131 stored in the storage unit 13. .

  Next, how to determine the cooling / heating device characteristics 131 by the energy consumption calculation device 1 of the present embodiment will be described with reference to FIGS.

  The second input unit 15 is used when the user inputs information used in the second thermal load analysis unit 16 or the statistical analysis unit 17. Specifically, the second input unit 15 includes the building information 51, the actual data group 61 of the heating element information, the actual data group 62 of the environmental information (weather information), the actual data group 63 of the operation information, and the power consumption. The record data group 64 is input. The record data group is a set of data measured so far.

  The second thermal load analysis unit 16 obtains the load component using the building information 51, the actual data group 61 of the heating element information, the actual data group 62 of the environmental information, and the actual data group 63 of the operation information (see FIG. 5 S1), a past spatial heat load data group is obtained (S2).

  The statistical analysis unit 17 uses the statistical model that statistically associates the past spatial heat load data group obtained by the second heat load analysis unit 16 with the actual power consumption data group 64 to use the cooling device characteristics. 131 is obtained (S3, S4). There are a single regression analysis method and a multiple regression analysis method as a method of obtaining the cooling / heating device characteristic 131.

  The simple regression analysis method uses the past spatial heat load estimated by the second heat load analysis unit 16 and the actual data group 64 of the power consumption of the refrigeration apparatus 2 to simply calculate the heat load and the power consumption. It is a method of modeling by regression analysis. The inclination is expressed as the efficiency (cooling device characteristics) of the cooling device 2 (air conditioning device). FIG. 4 shows the result of applying a single regression analysis to a building. The horizontal axis represents the heat load (calculated value) for each month, and the vertical axis represents the power consumption (measured value). Note that in a building in which neither an air conditioner nor a refrigeration unit is used, the slope obtained from the relationship between the power consumption of the entire building and the thermal load of the entire building represents the air conditioning efficiency (cooling device characteristics).

On the other hand, in the multiple regression analysis method, the cooling / heating device characteristic 131 can be obtained by the following equation.
E = a1 * x1 + a2 * x2 + a3 * x3 + (4)
E: Energy consumption of the cooling / heating device 2 (air conditioner), xi (i = 1, 2, 3,...): Input, ai (i = 1, 2, 3,...): Multiple regression for input xi. coefficient

  The input xi is the space heat load 4, the load component obtained by the mathematical expressions (1) to (3), the outside air temperature, the operation time of the cooling device 2, and the like.

  The cooling / heating device characteristics 131 obtained by the statistical analysis unit 17 as described above are stored in the storage unit 13.

  Next, the energy consumption calculation method of this embodiment will be described. First, the first heat load analysis unit 12 obtains the spatial heat load 4 using a physical model in which the building information 51, the heating element information 52, the environment information 53, and the operation information 54 are input information 5. Thereafter, the first power consumption amount estimation unit 14 calculates the power consumption amount corresponding to the spatial heat load 4 obtained by the first heat load analysis unit 12 from the cooling / heating device characteristics 131 stored in the storage unit 13. presume.

  By the way, the energy consumption calculation device 1 changes at least one parameter of the input information 5 little by little, and compares the power consumption amount of the cooling device 2 with respect to the input information 5 after the change, and the power consumption amount (or peak demand). It has a function for obtaining a condition for reducing. For example, the energy consumption calculation device 1 can find an operation condition that reduces the amount of power consumption by changing the parameters of the operation information 54.

  As shown in FIG. 1, the energy consumption calculation apparatus 1 according to the present embodiment further includes a parameter changing unit 18 and an optimization processing unit 19.

  The parameter changing unit 18 changes at least one parameter in the input information 5 input to the first input unit 11 within a predetermined range.

  Each time the parameter is changed by the parameter changing unit 18, the first heat load analyzing unit 12 obtains the spatial heat load 4 using the input information 5 in which the parameter is changed.

  The first power consumption amount estimation unit 14 calculates the power consumption amount corresponding to the spatial heat load 4 obtained by the first heat load analysis unit 12 from the cooling device characteristics 131 for each input information 5 in which the parameter is changed. presume.

  The optimization processing unit 19 extracts the minimum value of the plurality of power consumption amounts estimated by the first power consumption amount estimation unit 14 as the optimum value for each input information 5 whose parameter has been changed.

  Next, the operation of the optimization process performed by the energy consumption calculation apparatus 1 according to this embodiment will be described with reference to FIG. First, the parameter changing unit 18 receives the input information 5 (building information 51, heating element information 52, environmental information 53, operation information 54) from the first input unit 11 (S11 in FIG. 6). Thereafter, the parameter changing unit 18 changes at least one parameter of the input information 5, and the first thermal load analysis unit 12 obtains a load component using the changed input information 5 to obtain the spatial heat load 4 (S <b> 12). ). Thereafter, the first power consumption estimation unit 14 estimates the power consumption from the spatial heat load 4 (S13). Every time the parameter changing unit 18 changes the parameter, the operations of step S12 and step S13 are repeated. Thereafter, the optimization processing unit 19 extracts the minimum value of the power consumption as the optimum value, and inputs information 5 (building information 51, heating element information 52, environmental information 53, operation information 54) when the optimum value is obtained. Determine (S14).

  Furthermore, as shown in FIG. 1, the energy consumption calculation apparatus 1 of the present embodiment includes a third input unit 71, a second power consumption amount estimation unit 72, and a total power amount calculation unit 73. .

  The third input unit 71 is used when the user inputs information 81 of another device different from the cooling device 2. The information 81 of other devices is information relating to, for example, the rated power, the number or operating time of other devices, or a combination thereof.

  The second power consumption amount estimation unit 72 estimates the power consumption amount of another device different from the cooling device 2. When the other device is a lighting device, the second power consumption estimation unit 72 calculates the power consumption of the lighting device by the product of the capacity (rated power) (W / unit) of the lighting device, the number of units, and the operation time. calculate.

  The total power amount calculation unit 73 obtains the sum of the power consumption amount of the other devices estimated by the second power consumption amount estimation unit 72 and the power consumption amount of the cooling / heating device 2 as the total power amount.

  The energy consumption calculation apparatus 1 according to the present embodiment estimates the power consumption amount while changing the operation information 54 in the energy management mainly performed by the user, thereby reducing the influence of the energy saving effect due to the change of the operation information 54. It can be evaluated (compared).

  The storage unit 13 of the consumption energy calculation device 1 stores programs for the consumption energy calculation device 1 to execute various functions. That is, the storage unit 13 causes the energy consumption calculation device (computer) 1 to function as the first input unit 11, the first thermal load analysis unit 12, the storage unit 13, and the first power consumption amount estimation unit 14. A program is stored for this purpose. In addition, the storage unit 13 causes the energy consumption calculation device 1 to function as the second input unit 15, the second thermal load analysis unit 16, the statistical analysis unit 17, the parameter change unit 18, and the optimization processing unit 19. The program is stored.

  The program is stored in advance in the storage unit 13 when the energy consumption calculation device 1 is shipped. However, when the energy consumption calculation apparatus 1 loads the program after shipment, an example of a technique for the energy consumption calculation apparatus 1 to mount the program is a technique using a computer-readable recording medium in which the program is recorded. . When a recording medium is used, the energy consumption calculation device 1 only needs to include a drive device (not shown) for reading data on the recording medium. As another method, there is a method of downloading the program from a server using a network. When downloading the program via a network, the energy consumption calculation device 1 only needs to have a communication function (not shown) for communicating with the server using the network.

  From the above description, the energy consumption calculation device 1 according to the present embodiment analyzes the space heat load 4 by estimating the space heat load 4 as an analysis based on the physical model when estimating the power consumption of the cooling device 2. The subsequent process is assumed to be an analysis by a statistical model in which the heat load and the power consumption are associated. Thereby, in this embodiment, while reducing the parameter required in order to estimate the power consumption of the cooling device 2, the power consumption of the cooling device 2 can be estimated accurately. As a result, even an administrator who does not have special expertise for the cooling / heating device 2 can use it.

  That is, it is possible to provide an energy-saving estimation simulation tool that is specialized for operation improvement and is more suitable for users who do not have excellent expertise about the cooling / heating device 2. As a result, it is possible to remove the “complexity of input variables that are not regarded as important for operational improvement”, which has been an obstacle to the spread and use.

  Moreover, the energy consumption calculation apparatus 1 of this embodiment extracts the minimum value of the some power consumption estimated for every input information 5 by which the at least 1 parameter in the input information 5 was changed as an optimal value. Thereby, in this embodiment, since the input information 5 used by the 1st thermal load analysis part 12 can be optimized, the manager of a building can receive appropriate advice about an operation policy.

  Furthermore, the energy consumption calculation device 1 of the present embodiment estimates the power consumption of other devices such as lighting equipment based on the rated power, the number of units, and the operation time, and the power consumption of other devices The total power that is the sum of the power consumption of the cooling / heating device 2 is obtained. Thereby, it can compare with the conventional case using total electric energy.

  In addition, although this embodiment demonstrated the case where an air conditioner was used as the cooling / heating apparatus 2, the cooling / heating apparatus 2 is not limited to an air conditioning apparatus, A freezing apparatus may be sufficient. The same applies to the following second and third embodiments. FIG. 7 shows a cooling device characteristic 131 when the cooling device 2 is a refrigeration device. The horizontal axis in FIG. 7 represents the daily integrated heat load, and the vertical axis represents the daily integrated power consumption.

  Further, the input information 5 may be information excluding the building information 51. The same applies to the second embodiment below. The building information 51 is fixed information and is often reflected in the actual power consumption data group 64, that is, in the cooling / heating device characteristics 131. In such a case, the building information 51 is input information 5 The power consumption can be estimated without being input as. If the input of the building information 51 can be omitted as described above, the input work can be further simplified.

(Embodiment 2)
In the second embodiment, as illustrated in FIG. 8, a case will be described in which the statistical analysis unit 17 uses the slip data 65 of the power consumption when obtaining the cooling / heating device characteristics 131. In addition, about the component similar to the consumption energy calculation apparatus 1 (refer FIG. 1) of Embodiment 1, the same code | symbol is attached | subjected and description is abbreviate | omitted.

  The slip data 65 of power consumption is input to the second input unit 15 of the present embodiment. Note that a description of the same functions as those of the second input unit 15 (see FIG. 1) of the first embodiment will be omitted.

  The statistical analysis unit 17 according to the present embodiment receives the power consumption included in the slip data 65 from the second input unit 15 as actual data. The statistical analysis unit 17 uses the statistical model in which the data group of the spatial heat load 4 obtained by the second thermal load analysis unit 16 and the power consumption of the slip data 65 are statistically associated with each other to determine the cooling device characteristic 131. Ask. In addition, description is abbreviate | omitted about the function similar to the statistical analysis part 17 (refer FIG. 1) of Embodiment 1. FIG.

  The energy consumption calculation device 1 according to the present embodiment is used when proposing the sale of an energy saving control device whose main purpose is operational improvement. In the proposal of the energy saving control device, by estimating the power consumption amount while changing the operation information 54, it is possible to evaluate (compare) the power reduction effect by the operation improvement when the energy saving control device is introduced.

(Embodiment 3)
In the third embodiment, as shown in FIG. 9, a case where the cooling / heating device characteristic 131 is obtained from the catalog information 66 will be described. In addition, about the component similar to the consumption energy calculation apparatus 1 (refer FIG. 1) of Embodiment 1, the same code | symbol is attached | subjected and description is abbreviate | omitted.

  The storage unit 13 of the present embodiment stores the cooling device characteristics 131 of each of the plurality of cooling devices 2. The cooling device characteristic 131 of each cooling device 2 is a characteristic set in advance from the catalog information 66 of the cooling device 2. Note that a description of the same functions as those of the storage unit 13 (see FIG. 1) of the first embodiment will be omitted.

  In the energy consumption calculation apparatus 1 of the present embodiment, the second input unit 15, the second thermal load analysis unit 16, and the statistical analysis unit 17 are unnecessary.

  The energy consumption calculation device 1 according to the present embodiment is used when a sale of the cooling / heating device 2 is proposed. For example, when proposing a cooling device 2 that is more efficient than the current situation, it is possible to evaluate (compare) the power consumption reduction effect by estimating the power consumption while changing the cooling device characteristics 131. it can.

  In each embodiment, the 1st input part 11, the 2nd input part 15, and the 3rd input part 71 may be provided individually, and may be provided as one input part. .

  Moreover, the 1st thermal load analysis part 12 and the 2nd thermal load analysis part 16 may be provided separately, and may be provided as one thermal load analysis part.

DESCRIPTION OF SYMBOLS 1 Consumption energy calculation apparatus 12 1st thermal load analysis part 131 Chiller apparatus characteristic 14 1st power consumption estimation part 16 2nd thermal load analysis part 17 Statistical analysis part 18 Parameter change part 19 Optimization processing part 2 Chiller 3 Space 5 Input Information 51 Building Information 52 Heating Element Information 53 Environmental Information 54 Operational Information 61 Heating Element Information Result Data Group 62 Environmental Information Result Data Group 63 Operation Information Result Data Group 64 Power Consumption Data Group 65 Voucher Data 72 Second power consumption estimation unit 73 Total power calculation unit

Claims (7)

An energy consumption calculation device for calculating power consumption of a cooling device that controls the temperature of a predetermined space in a building,
Building information including parameters relating to the building, heating element information including parameters relating to heating elements existing in the building, environmental information including parameters relating to the environment of the installation location of the building, and parameters relating to operation of the cooling and heating apparatus A first thermal load analysis unit that obtains the thermal load of the space using a physical model having the operation information including the input information as input information;
A first power consumption estimation unit that estimates the power consumption corresponding to the thermal load obtained by the first thermal load analysis unit from a cooling device characteristic in which a thermal load and a power consumption are associated; An energy consumption calculating device comprising: A parameter changing unit for changing at least one parameter in the input information within a predetermined range;
An optimization processing unit for obtaining an optimum value of the power consumption when the parameter is changed by the parameter changing unit,
The first thermal load analysis unit obtains the thermal load using the input information in which the parameter is changed each time the parameter is changed by the parameter change unit,
The first power consumption amount estimation unit calculates the power consumption amount corresponding to the heat load obtained by the first heat load analysis unit for each input information in which the parameter has been changed. Estimated from
The optimization processing unit extracts, as the optimum value, a plurality of minimum values of the power consumption estimated by the first power consumption estimation unit for each of the input information whose parameters are changed. The energy consumption calculation apparatus according to claim 1. A second heat load analysis unit for obtaining a past heat load data group using the building information, the result data group of the heating element information, the result data group of the environmental information, and the result data group of the operation information;
Statistical analysis for determining the characteristics of the cooling / heating device using a statistical model that statistically associates the data group of the past thermal load obtained by the second thermal load analysis unit and the past data group of the past power consumption. The energy consumption calculating device according to claim 1 or 2, further comprising: a unit. A second power consumption estimation unit that estimates the power consumption of another device different from the cooling device;
A total power amount calculating unit that obtains a sum of the power consumption amount of the other device estimated by the second power consumption amount estimation unit and the power consumption amount of the cooling device as a total power amount. The energy consumption calculation device according to any one of claims 1 to 3.   The statistical analysis unit sets a total power amount that is a sum of a past power consumption amount of the cooling device and a past power consumption amount of another device different from the cooling device as a data group in the past power consumption amount. The energy consumption calculation device according to claim 3, wherein the characteristics of the cooling / heating device are created. An energy consumption calculation method for calculating power consumption of a cooling device that controls the temperature of a predetermined space in a building,
Building information including parameters relating to the building, heating element information including parameters relating to heating elements existing in the building, environmental information including parameters relating to the environment of the installation location of the building, and parameters relating to operation of the cooling and heating apparatus A first step of determining a thermal load of the space using a physical model having the operation information including as input information;
A second step of estimating the power consumption corresponding to the thermal load obtained in the first step from a cooling device characteristic in which the thermal load and the power consumption are associated with each other. Energy calculation method. In order to calculate the power consumption of the cooling device that controls the temperature of a predetermined space in the building,
Building information including parameters relating to the building, heating element information including parameters relating to heating elements existing in the building, environmental information including parameters relating to the environment of the installation location of the building, and parameters relating to operation of the cooling and heating apparatus Using a physical model with the operation information including as input information, a thermal load analysis means for obtaining the thermal load of the space, and
The program for functioning as a power consumption amount estimation means for estimating the power consumption amount corresponding to the heat load obtained by the heat load analysis means from the cooling / heating device characteristics in which the heat load and the power consumption amount are associated.

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