Energy-saving performance simplified prediction system

Abstract

To provide an energy saving performance simple forecasting system than can forecast an energy saving performance of a building by a simple method.SOLUTION: An energy saving performance simple forecasting system includes an item display unit (display unit 15) that displays a plurality of items in which input information on a building is entered, an input information acquisition unit (control unit 12) that acquires the input information that has been entered, and a forecasting value calculation unit (control unit 12) that calculates a forecasting value of energy saving performance of the building based on the input information using a forecasting model generated on the basis of the plurality of items. The plurality of items includes a required item necessary for the calculation of the forecasting value. When there are unentered items in the plurality of items excluding the required items, the forecasting value calculation unit (control unit 12) automatically compensates for the unentered item with input missing data, and calculates the forecasting value based on the input information and the input missing data.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a simple prediction system for the energy-saving performance of buildings.

Background Art

[0002] Conventionally, technologies for predicting the energy-saving performance of buildings are well-known. For example, it is as described in Patent Document 1.

[0003] Patent Document 1 describes an apparatus that calculates the energy-saving performance (index) of a building by simulation based on the input information of the building.

[0004] In the apparatus described in Patent Document 1, if all items used in the simulation such as the region, the use of the building, the specifications, etc. are input, the calculation of the energy-saving performance is executed.

[0005] However, the number of items used in the simulation of the energy-saving performance is large, and it takes time to input. Therefore, a system that can predict the energy-saving performance of a building in a simple manner is desired.

Prior Art Documents

Patent Documents

[0006]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0007] The present invention has been made in view of the above circumstances, and the problem to be solved is to provide a simple prediction system for the energy-saving performance of a building that can predict the energy-saving performance of a building in a simple manner.

Means for Solving the Problems

[0008] The problems that this invention aims to solve are as described above, and the means for solving these problems will now be explained.

[0009] That is, claim 1 comprises an item display unit that displays a plurality of items into which input information relating to a building is entered, an input information acquisition unit that acquires the entered input information, and a prediction value calculation unit that calculates a predicted value of the energy-saving performance of the building based on the input information using a prediction model created based on the plurality of items, wherein the plurality of items include essential items necessary for calculating the prediction value. The aforementioned input information acquisition unit includes an input unit that can be operated by the operator, The aforementioned prediction value calculation unit calculates the multiple items excluding the essential items. In the case where, among multiple options, one option is selected as input information through an operation on the input unit, the option indicating no input was selected. If there are any missing entries, the system automatically fills in the missing entries with missing data, calculates the predicted values ​​based on the input information and the missing data, and further comprises a missing data acquisition unit that extracts multiple pieces of information corresponding to the missing entries from a database containing information on other buildings different from the building in question, which have the same purpose as the building in question, and obtains the average value of the extracted pieces of information as the missing data.

[0010] In claim 2, the prediction value calculation unit calculates the prediction value by multiple regression analysis.

[0012] Claim 3 In this case, the prediction model is configured such that the input information items are based on the items used when calculating the energy-saving performance of a building using the model building method, and the number of input information items is set to be less than or equal to a limit value for the number of items, which is smaller than the number of items in the model building method.

[0013] Claim 4 In this model, the prediction model is configured such that the number of input information items exceeding the item limit is reduced to or less than the item limit, by removing the required items and reducing the number of items in order of their contribution to the predicted value.

[0014] Claim 5 In this case, the predicted value calculation unit reduces a group of items that are related to the building's thermal mechanism among the plurality of items excluding the essential items and have a lower contribution degree to the predicted value than other items related to the building's thermal mechanism into one reduced item, and calculates the predicted value.

Advantages of the Invention

[0015] As an effect of the present invention, the following effects are achieved.

[0016] In claim 1, the energy-saving performance of a building can be predicted by a simple method. Furthermore, even if some fields are left blank, the energy-saving performance of the building can be predicted with a certain degree of accuracy.

[0017] In claim 2, the predicted value can be calculated by a relatively simple model that is easy for people to understand.

[0019] Claim 3 In this case, by restricting the number of items of the input information, the labor of input can be reduced.

[0020] Claim 4 In this case, by reducing items with a low contribution degree to the predicted value, the labor of input can be reduced, and the energy-saving performance of a building can be predicted with a certain accuracy.

[0021] Claim 5 In this case, by reducing a group of items with a low contribution degree to the predicted value, the calculation burden in the process can be reduced.

Brief Description of the Drawings

[0022] [Figure 2] A block diagram showing an energy-saving performance simple prediction system according to an embodiment of the present invention. [Figure 2] A flowchart showing a prediction process executed by the energy-saving performance simple prediction system. [Figure 3]Schematic diagram showing an input screen of input information displayed by a prediction model. [Figure 4] Schematic diagram showing the continuation of the input screen in FIG. 3. [Figure 5] Schematic diagram showing the continuation of the input screen in FIG. 4. [Figure 6] Schematic diagram showing the creation of a reduced item of "opening ratio of the outer skin". [Figure 7] Schematic diagram showing the creation of a reduced item of "mechanical ventilation equipment: calculated floor area ratio".

Mode for Carrying Out the Invention

[0023] Hereinafter, an energy-saving performance simple prediction system 10 according to an embodiment of the present invention will be described. The energy-saving performance simple prediction system 10 predicts the energy-saving performance of a building.

[0024] In the present embodiment, the building for which the energy-saving performance is predicted assumes a non-residential building. Examples of the use of the building include an office or a store (a retail store).

[0025] Also, in the present embodiment, BEI (Building Energy efficiency Index) is adopted as an index of the energy-saving performance predicted by the energy-saving performance simple prediction system 10. BEI is calculated by dividing the energy consumption of the target building by the reference energy consumption. The smaller the value of BEI, the higher the energy-saving performance of the building is shown.

[0026] One method for predicting the BEI (Building Energy Index) is the model building method, which involves calculating the BEI value by applying the specifications of the equipment and facilities actually installed to a model building that assumes the shape and room configuration for each building use. When calculating the BEI value using the model building method, it is conceivable to perform calculations (simulations, etc.) using a program created based on, for example, the "Act on the Improvement of Energy Consumption Performance of Buildings (Building Energy Conservation Act)." This program calculates the predicted BEI value based on input information about the building (for example, information on the building's shape, envelope performance, and equipment).

[0027] However, performing calculations using the above-mentioned model building method program requires inputting a large amount of information (e.g., more than 140 items), making the input process time-consuming. Furthermore, it is expected that all the data necessary to run the above-mentioned model building method program will only be available in the final stages of building design. For this reason, it was difficult to predict the BEI in the early stages of building design (e.g., the planning stage).

[0028] The simplified energy-saving performance prediction system 10 shown in Figure 1 can predict the Building Energy Investment (BEI) based on input information about a building using a simple method by employing statistical techniques. The simplified energy-saving performance prediction system 10 comprises a control device capable of processing various types of information. A general-purpose personal computer or server can be used as the control device. The simplified energy-saving performance prediction system 10 includes a storage unit 11, a control unit 12, a communication unit 13, an input unit 14, and a display unit 15.

[0029] The memory unit 11 stores various programs (such as the prediction model described later) and various acquired information. The memory unit 11 is composed of an HDD, RAM, ROM, etc.

[0030] The control unit 12 executes the program stored in the memory unit 11. The control unit 12 is composed of a CPU.

[0031] The communication unit 13 is capable of communicating with external devices. The communication unit 13 can exchange information with external devices via various communication methods, such as the internet. By using the communication unit 13, it becomes possible to access, for example, a database on an external server.

[0032] The input unit 14 is for inputting various types of information. The input unit 14 consists of a keyboard, mouse, etc.

[0033] The display unit 15 displays various types of information. The display unit 15 is composed of, for example, a liquid crystal display.

[0034] The energy-saving performance simplified prediction system 10, configured as described above, is managed, for example, by a building design company.

[0035] The energy-saving performance simplified prediction system 10 can perform a process (prediction process) to make a simple prediction of BEI using a prediction model. Here, the prediction model is a model that calculates the predicted value of BEI using input information with statistical methods (statistical analysis). In this embodiment, the prediction model calculates the predicted value of BEI using multiple regression analysis (for example, the stepwise method).

[0036] The prediction model according to this embodiment predicts the BEI calculated using the model building method. The prediction model calculates the predicted BEI using fewer input information items (for example, around 40 items) than the number of input information items (140 or more) used in the model building method. The input information items used in the prediction model are set by reducing the number of input information items used in the model building method. A detailed explanation of how to set the input information items of the prediction model will be given later.

[0037] The prediction process will be explained below using the flowchart in Figure 2.

[0038] In step S101, the energy-saving performance simplified prediction system 10 (control unit 12) performs a process to select the use of the building (hereinafter simply referred to as "building") that is the subject of BEI prediction. Examples of building uses include offices and shops (retail stores), etc. However, the building uses are not limited to the examples mentioned above, and various other uses can be adopted.

[0039] In step S101, the control unit 12 displays a selection screen (not shown) for selecting the building's purpose on the display unit 15. The operator selects the building's purpose on the selection screen by performing operations using the input unit 14. The control unit 12 also stores the selected building purpose information in the storage unit 11. After executing the process in step S101, the control unit 12 proceeds to the process in step S102.

[0040] In step S102, the control unit 12 reads the input information (input values). Here, the input information is the information used to predict energy saving performance in the process executed by the simplified energy saving performance prediction system 10. The input information includes information about the building's shape and envelope, and information about equipment such as air conditioning.

[0041] Input information is entered by the operator using the input unit 14 for each item on the input screens shown in Figures 3 to 5. The input screen displayed corresponds to the building use (e.g., office) selected in step S101. The input information is entered in the process of step S108, which will be described later. The entered information is stored in the storage unit 11. In step S102, the control unit 12 extracts (reads) the input information stored in the storage unit 11 for the calculation of the predicted value (step S105), which will be described later.

[0042] As shown in Figures 3 to 5, each item of input information is classified into multiple categories: "Basic Information," "Building Shape," "Building Envelope Performance," "Window Performance," "Air Conditioning," "Outdoor Air Treatment," "Ventilation Equipment," "Lighting Equipment," "Hot Water Supply Equipment," and "Elevators." Below, we will explain the details of each item of input information for each category.

[0043] The "Basic Information" shown in Figure 3 is the basic information of the building that is the subject of the BEI prediction. The "Basic Information" includes the "Energy Conservation Zone Classification" and the "Area to be Calculated".

[0044] The "Energy Conservation Regional Classification" is information about regions classified for the purpose of evaluating energy conservation performance. The "Energy Conservation Regional Classification" is divided into eight regional classifications, from region 1 to region 8. The regional classification to which the building is located is entered in the "Energy Conservation Regional Classification" field. The input field for "Energy Conservation Regional Classification" allows the user to select one of the eight regional classifications.

[0045] The "area subject to calculation" is the floor area of ​​a building that is subject to evaluation of its energy-saving performance. More specifically, the "area subject to calculation" is the total floor area of ​​rooms belonging to the building's intended use.

[0046] "Building shape" refers to information about the shape of the building. "Building shape" includes "the sum of the floor heights of each floor" and "the outer perimeter length of the non-spaced core."

[0047] "The sum of the floor heights" is the sum of the floor heights of each floor in a building (for example, the height from the floor slab of the lower floor to the floor slab of the upper floor).

[0048] "Perimeter length of non-space core area" refers to the perimeter length of the non-space core area (non-air-conditioned areas located at the same position on the floor plan from the ground floor to the top floor, such as elevator shafts and stairwells).

[0049] "Building envelope performance" refers to information about the performance of the building's exterior. Here, the building envelope refers to the structural elements on the exterior perimeter of the building, such as the exterior walls, roof, and floors exposed to the outside air (e.g., pilotis). "Building envelope performance" includes the "exterior wall area," "roof area," "floor area exposed to the outside air," and "average heat transfer coefficient of the building envelope" for each orientation (east, west, north, south).

[0050] "Exterior wall area" refers to the area of ​​the building's exterior walls (including the area of ​​openings). "Exterior wall area" is entered separately for each cardinal direction (east, west, north, south).

[0051] "Roof area" refers to the area of ​​the building's roof (actual area or horizontal projection area).

[0052] "Floor area exposed to the outside air" refers to the area of ​​the building's floor (such as pilotis) that is exposed to the outside air.

[0053] The "average heat transfer coefficient of the building envelope" is the average heat transfer coefficient (W / m²) of the entire floor in contact with the building envelope. 2 K) is the answer.

[0054] The "window performance" shown in Figure 4 is information about the performance of the building's windows. "Window performance" includes "window area" and "average solar heat gain coefficient of the windows" by orientation (east, west, north, south).

[0055] "Window area" refers to the area of ​​the building's windows. "Exterior wall area" is entered separately for each direction (east, west, north, south).

[0056] "Average solar heat gain coefficient of windows" is the average solar heat gain coefficient (W / m²) of the windows of a building. 2 K) is the answer.

[0057] "Air Conditioning" refers to information about the building's air conditioning system. This includes "Main Heat Source Type," "Heat Source Capacity per Floor Area," and "Coefficient of Performance (COP)." Each of the above "Air Conditioning" items contains information for both cooling and heating.

[0058] The "Primary Heat Source Equipment" is the primary heat source equipment for the air conditioning system. This field allows you to input the heat source equipment for both cooling and heating. Examples of "Primary Heat Source Equipment" include water chilling units (air-cooled or water-cooled) and packaged air conditioners (air-cooled or water-cooled). The input field for "Primary Heat Source Equipment" allows you to select one of several heat source equipment types, such as the water chilling unit mentioned above.

[0059] "Heat source capacity per floor area" refers to the heat source capacity (rated cooling capacity or rated heating capacity) per floor area of ​​the air conditioning system (cooling or heating). The "Heat source capacity per floor area" is entered separately for cooling and heating.

[0060] "Heat Source Efficiency COP" refers to the heat source efficiency (COP) of the air conditioning system (cooling or heating). The "Heat Source Efficiency COP" can be entered separately for cooling and heating.

[0061] "Outdoor air treatment" refers to information about the building's total heat exchanger. Information for "outdoor air treatment" is entered by selecting one of the following options: "none," "yes," or "do not select." "Outdoor air treatment" includes "presence or absence of a total heat exchanger," "total heat exchanger-automatic ventilation switching function," and "presence or absence of stopping outdoor air intake during preheating."

[0062] "Presence or absence of a total heat exchanger" refers to information about the presence or absence of a total heat exchanger in the building.

[0063] "Total heat exchanger - automatic ventilation switching function" refers to information about the presence or absence of an automatic ventilation switching function in the total heat exchanger (a function that automatically switches between heat exchange ventilation and normal ventilation based on the temperature and humidity of the outside air and the room).

[0064] "Whether or not outside air intake is stopped during preheating" indicates whether or not there is a function to stop the intake of outside air during preheating operation.

[0065] The "Ventilation Equipment" shown in Figure 5 is information about the building's ventilation equipment. Ventilation equipment is installed, for example, in machine rooms, toilets, parking lots, and kitchens. "Ventilation Equipment" includes "Ventilation Equipment Evaluation," "Presence or Absence of Ventilation Equipment," and "Floor Area for Calculation."

[0066] The "Ventilation Equipment Evaluation" section allows you to choose whether or not to evaluate the ventilation equipment. Information input for the "Ventilation Equipment Evaluation" is done by selecting one of two options: "Do not evaluate" or "Evaluate."

[0067] The "Presence or Absence of Ventilation Equipment" field provides information about the presence or absence of ventilation equipment in the areas subject to the "Ventilation Equipment" calculation (machine rooms, toilets, parking lots, and kitchens). Information for "Presence or Absence of Ventilation Equipment" is entered by selecting one of the following options: "None," "Yes," or "Do not select." This information is entered separately for each area subject to the calculation (machine rooms, toilets, parking lots, and kitchens).

[0068] The "Floor Area to be Calculated" is information about the floor area of ​​the area to be calculated (machine room, toilet, parking lot, and kitchen). The "Floor Area to be Calculated" is entered separately for each area to be calculated (machine room, toilet, parking lot, and kitchen).

[0069] "Lighting equipment" refers to information about the lighting equipment in a building. This includes "power consumption per floor area" and "presence or absence of brightness detection control."

[0070] "Power consumption per floor area" is the power consumption (W / m²) per floor area of ​​lighting equipment (for example, the floor area of ​​an office). 2 )

[0071] The "Presence or Absence of Brightness Detection Control" field indicates whether or not the lighting equipment employs brightness detection control. Input for "Presence or Absence of Brightness Detection Control" is done by selecting one of the following options: "No," "Yes," or "Do not select."

[0072] "Hot water supply facilities" refers to information about the building's hot water supply system. This includes, for example, hot water supply systems for washrooms, bathrooms, and heating. "Hot water supply facilities" also includes whether or not a hot water supply system is in place.

[0073] The "Presence or Absence of Hot Water Supply Equipment" field provides information about the presence or absence of hot water supply equipment for each area. Input for "Presence or Absence of Hot Water Supply Equipment" is done by selecting one of the following options: "None," "Yes," or "Do not select." This information is entered separately for each area (washroom, bathroom, heating).

[0074] "Elevators" refers to information about elevators in a building. This includes whether or not elevators are present.

[0075] The "Presence or Absence of Elevator" field provides information about the presence or absence of an elevator. Input for "Presence or Absence of Elevator" is done by selecting one of the following options: "No," "Yes," or "Do not select."

[0076] The input information items have been explained above. Note that the input information is not limited to the examples described above; various types of information usable for predicting energy-saving performance can be used. After executing the process in step S102 (reading the input information), the control unit 12 proceeds to the process in step S103.

[0077] In step S103, the control unit 12 determines whether all required items among the input information items have been entered. Here, required items are items necessary for predicting energy saving performance. In this embodiment, "energy saving region classification," "area to be calculated," and "total floor height of each floor" are set as required items. In addition, in this embodiment, "building use" selected in step S101 is also included as a required item.

[0078] If the control unit 12 determines that all required fields have been entered, it proceeds to step S103. On the other hand, if the control unit 12 determines that there are required fields that have not been entered, it proceeds to step S107.

[0079] In step S104, the control unit 12 automatically completes the missing input data. Here, missing input data refers to data that completes the missing items among the input information items, excluding required items. In this embodiment, for example, if the input field for an item where a numerical value such as area is entered is blank, it is determined to be missing. Also, in an item with the options "None," "Yes," and "Do not select," if "Do not select" is selected, it is determined to be missing.

[0080] The control unit 12 uses data from a predetermined database to fill in any missing input data. This database stores information about buildings other than the building (the building in question) for which energy-saving performance is being predicted.

[0081] If there are any missing entries, the control unit 12 extracts information corresponding to the missing entries from the database for several other buildings that share the same specifications as the building in question (for example, the same structure or use). The control unit 12 also calculates the average value of the extracted information and uses this average value to fill in the missing input data. After executing the process in step S104, the control unit 12 proceeds to the process in step S105.

[0082] In step S105, the control unit 12 calculates a predicted value of energy saving performance based on the input information. Specifically, the control unit 12 calculates a predicted value of energy saving performance using a prediction model based on the input information (including required items) and missing input data. After executing the process in step S105, the control unit 12 proceeds to the process in step S106.

[0083] In step S106, the control unit 12 displays the calculation result of the predicted value on the display unit 15. As shown in Figure 3, the calculation result of the predicted value (predicted BEI) is displayed on the input screen. After executing the process in step S106, the control unit 12 proceeds to the process in step S107.

[0084] In steps S107 and S108, the control unit 12 waits for input information to be received and prepares the system so that the operator can input the information. The operator inputs the information by performing an operation using the input unit 14. The control unit 12 stores the input information in the storage unit 11. After executing the process in step S108, the control unit 12 proceeds to the process in step S102.

[0085] The above describes the prediction process using the prediction model. The above prediction process is executed in a continuous loop. In the prediction process, if input information for at least the required items is entered (step S103: YES), the predicted value of BEI is calculated (step S105). Furthermore, if input information for items other than the required items is entered thereafter (steps S107, S108), the predicted value of BEI is calculated based on that input information in place of the missing input data (step S105). In this case, the calculated prediction value is expected to be relatively more accurate than when using the missing input data. Thus, in the prediction process, the calculated prediction value is updated each time new input information is entered.

[0086] According to the above prediction process, the predicted BEI value can be calculated simply by inputting the required information (four pieces of information in this embodiment). This reduces the effort required for input, unlike when using a model building method program that requires inputting information on many items. Furthermore, even if there are missing items, the prediction process can calculate the predicted BEI value with a certain degree of accuracy using missing input data calculated based on information on buildings with specifications common to the building in question.

[0087] Furthermore, in this embodiment, the prediction model calculates the predicted BEI value using multiple regression analysis (e.g., stepwise method). This allows for the calculation of predicted values ​​using a relatively simple model that is easy for humans to understand, unlike models that use neural networks, for example.

[0088] Furthermore, the number of input information items for the prediction models shown in Figures 3 to 5 is set to be less than or equal to a predetermined limit on the number of items. For example, a limit of around 40 items can be used. Note that the limit on the number of items is not limited to the examples above, and any value can be set considering the effort required for input and the accuracy of the data.

[0089] The input information items for the prediction model are determined by performing an evaluation (variable evaluation) using multiple regression analysis (stepwise method) on the input information items (more than 140 items) of the model building method. Specifically, based on the results of the multiple regression analysis performed on the input information items (variables) of the model building method, items exceeding the above item limit are removed in order of lowest evaluation value, and the remaining items are set as the input information items for the prediction model. Here, the evaluation value is the coefficient (contribution) of each variable to the calculation result of the multiple regression analysis (stepwise method).

[0090] The input information items for the prediction model described above are configured before the prediction process is executed. By reducing the number of input information items as described above, it is possible to reduce the number of input items for the prediction model that have a low evaluation value (contribution) to the calculation result of the BEI prediction, thereby reducing the effort required for input. In addition, by executing the prediction process using a prediction model configured so that the number of items is below the item limit, the computational burden in the prediction process can be reduced.

[0091] Furthermore, in this embodiment, prediction processing can be performed using a modified model in which a "contracted item" is added to the prediction model, which is a combination of several items (groups of items) with relatively low evaluation values ​​(coefficients) from among the specific items of the input information of the prediction model. When the control unit 12 performs prediction processing using the modified model, it treats the contracted item as a single variable and calculates the predicted value by multiple regression analysis.

[0092] Here, "specific items" refer to items from the input information of the prediction model that are particularly related to the thermal mechanism of the building (items that contribute relatively greatly to the thermal performance of the building). In this embodiment, "specific items" include the items "building envelope performance," "window performance," and "ventilation equipment" (items enclosed by dashed lines in Figures 3 to 5). The abbreviation of items will be explained below using Figures 6 and 7.

[0093] Figure 6 illustrates the case where the group of items with low evaluation values ​​among specific items are "exterior wall area" under "exterior performance" and "window area" under "window performance". As shown in Figure 6, the control unit 12 can reduce the items (8 items) for "exterior wall area" and "window area" by orientation (east, west, north, south) to a single item called "exterior opening ratio". Here, the "exterior opening ratio" is the value obtained by dividing the sum of the "window area" by orientation by the sum of the "exterior wall area" by orientation.

[0094] Figure 7 illustrates the case where the items with low evaluation values ​​among specific items are "Presence or Absence of Ventilation Equipment" and "Calculated Floor Area" in the "Ventilation Equipment Evaluation" section. As shown in Figure 7, the control unit 12 can reduce the items (8 items) of "Presence or Absence of Ventilation Equipment" and "Calculated Floor Area" for ventilation equipment by installation location (machine room, toilet, parking lot, and kitchen) to a single item called "Mechanical Ventilation Equipment: Calculated Floor Area Ratio" using the "Calculated Area" from the mandatory item "Basic Information". Here, "Mechanical Ventilation Equipment: Calculated Floor Area Ratio" is the value obtained by dividing the sum of the "Calculated Floor Area" for each installation location (location with ventilation equipment) by the "Calculated Area" of the entire building.

[0095] In this embodiment, groups of items are reduced if the evaluation value of the reduced items is higher than the evaluation value (contribution) of the individual items before reduction. Note that the groups of items to be reduced are not limited to the examples shown in Figures 6 and 7, and various items can be used.

[0096] The reduction of the above items is performed before executing the prediction process. As mentioned above, by adding a reduced item, which is a reduction of items with low evaluation values ​​(coefficients) among specific items, to the prediction model and treating the reduced item as a single variable in the calculation of the predicted value, the computational burden on the prediction model (modified model) can be reduced, and the accuracy of the prediction can be improved. In other words, if the number of items used in the calculation is large, the calculation formula for predicting BEI becomes complex, but the computational burden can be reduced by using a reduced item that combines multiple items. In addition, by combining items with low evaluation values ​​(contributions) individually into a reduced item with a relatively high evaluation value (contribution), the accuracy of the calculation can be improved.

[0097] As described above, the simplified energy-saving performance prediction system 10 according to one embodiment of the present invention is The item display unit (display unit 15) displays multiple items into which input information about the building is entered (step S106), The input information acquisition unit (control unit 12) acquires the input information (step S102), A prediction value calculation unit (control unit 12) calculates a predicted value of the energy-saving performance of the building based on the input information using a prediction model created based on the aforementioned multiple items (step S105), It is equipped with, The aforementioned items include essential items necessary for calculating the predicted value, The aforementioned predicted value calculation unit (control unit 12) If there are any missing entries in the multiple items excluding the required items, the missing entries are automatically filled in with the missing input data (step S104), and the predicted value is calculated based on the input information and the missing input data.

[0098] This configuration allows for the prediction of a building's energy efficiency performance using a simple method. In other words, by simply inputting the required information, the building's energy efficiency performance can be predicted. This reduces the effort required for data entry.

[0099] Furthermore, the predicted value calculation unit (control unit 12) The aforementioned predicted values ​​are calculated using multiple regression analysis.

[0100] This configuration allows for the calculation of predicted values ​​using a relatively simple model that is easy for humans to understand, unlike models that use neural networks, for example.

[0101] Furthermore, the energy-saving performance simplified prediction system 10 is The system includes an input missing data acquisition unit (control unit 12) that extracts multiple pieces of information from a database containing information about other buildings different from the aforementioned building, which have the same specifications as the aforementioned building and correspond to the missing items, and acquires the average value of the extracted pieces of information as the input missing data.

[0102] This configuration allows for the prediction of a building's energy efficiency with a certain degree of accuracy, even when some fields are left blank. In other words, even when some fields are left blank, the energy efficiency of the building can be predicted with a certain degree of accuracy using missing input data calculated based on information from other buildings with specifications similar to the building in question.

[0103] Furthermore, the prediction model is The items of the aforementioned input information are set based on the items used when calculating the energy-saving performance of a building using the model building method. The number of items in the aforementioned input information is set to be less than or equal to the item limit (approximately 40 items) which is less than the number of items in the Model Building Law (140 or more items).

[0104] This configuration reduces the effort required for data entry by limiting the number of input fields. It also reduces the computational burden during processing.

[0105] Furthermore, the prediction model is The number of input information items exceeding the item limit is reduced to less than or equal to the item limit, by excluding the required items and reducing the number of items in order of their contribution to the predicted value.

[0106] This configuration reduces the effort required for data entry by eliminating items that have a low contribution to the predicted value, while also enabling the prediction of a building's energy-saving performance with a certain level of accuracy.

[0107] Furthermore, the predicted value calculation unit (control unit 12) Of the multiple items excluding the mandatory items, the items relating to the thermal mechanism of the building, whose contribution to the predicted value is lower than that of other items relating to the thermal mechanism of the building, are reduced to a single reduced item, and the predicted value is calculated.

[0108] This configuration allows us to reduce the computational burden during processing by abbreviating items that have a low contribution to the predicted value.

[0109] The control unit 12 according to this embodiment is one form of the input information acquisition unit, predicted value calculation unit, and input missing data acquisition unit according to the present invention. Furthermore, the display unit 15 according to this embodiment is one form of the item display unit according to the present invention.

[0110] Although one embodiment of the present invention has been described above, the present invention is not limited to the above configuration, and various modifications are possible within the scope of the invention as described in the claims.

[0111] For example, in this embodiment, we have shown an example in which a prediction model is used to calculate the predicted value of BEI by multiple regression analysis (e.g., stepwise method), but the configuration is not limited to this. Various models can be used as means for calculating the predicted value.

[0112] Furthermore, while this embodiment demonstrates an example where the average value of multiple pieces of information extracted from a database is used as input missing data, the configuration is not limited to this. For example, the median of multiple pieces of information extracted from a database may be used as input missing data.

[0113] Furthermore, while this embodiment shows an example in which BEI is used as an indicator of energy-saving performance predicted by the energy-saving performance simplified prediction system 10, the system is not limited to this configuration, and various energy-saving performance indicators can be used.

[0114] Furthermore, although this embodiment shows an example where the energy-saving performance simplified prediction system 10 is a control device such as a personal computer or server, it is not limited to this configuration. For example, the energy-saving performance simplified prediction system 10 may be a terminal such as a tablet or smartphone. [Explanation of Symbols]

[0115] 10. Simple Energy Saving Performance Prediction System 12 Control Unit 15 Display section

Claims

1. An item display unit that displays multiple items into which input information about the building is entered, An input information acquisition unit that acquires the input information, A prediction value calculation unit that uses a prediction model created based on the aforementioned multiple items to calculate a predicted value of the energy-saving performance of the building based on the input information, It is equipped with, The aforementioned items include essential items necessary for calculating the predicted value, The aforementioned input information acquisition unit includes an input unit that can be operated by the operator, The aforementioned prediction value calculation unit, Among the multiple items excluding the required items, if there are any items for which one option is selected as input information by an operation on the input unit from among multiple options, and an option indicating "not entered" is selected, the missing items will be automatically filled in with missing input data, and the predicted value will be calculated based on the input information and the missing input data. The aforementioned required items include the building's purpose, The system further comprises an input missing data acquisition unit that extracts multiple pieces of information from a database containing information about other buildings different from the aforementioned building, which have the same purpose as the aforementioned building and correspond to the missing items, and obtains the average value of the extracted multiple pieces of information as the input missing data. A simplified energy-saving performance prediction system.

2. The aforementioned prediction value calculation unit, The predicted values ​​are calculated using multiple regression analysis. The simplified energy-saving performance prediction system according to claim 1.

3. The aforementioned prediction model, The items of the aforementioned input information are set based on the items used when calculating the energy-saving performance of a building using the model building method. The number of items in the aforementioned input information is set to be less than or equal to the item limit value which is smaller than the number of items in the Model Building Law. A simplified energy-saving performance prediction system according to claim 1 or claim 2.

4. The aforementioned prediction model, The number of items in the input information that exceeds the item limit is reduced to less than or equal to the item limit, by removing the required items and reducing the number of items in order of their contribution to the predicted value. The simplified energy-saving performance prediction system according to claim 3.

5. The aforementioned prediction value calculation unit, Of the multiple items excluding the mandatory items, the items relating to the thermal mechanism of the building, whose contribution to the predicted value is lower than the contribution of other items relating to the thermal mechanism of the building, are reduced to a single reduced item to calculate the predicted value. A simplified energy-saving performance prediction system according to any one of claims 1 to 4.

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