Server for selecting building subject to energy efficiency improvement and method for selecting building subject to energy efficiency improvement using same

Abstract

The present invention relates to a server for selecting a building subject to energy efficiency improvement and a method for selecting a building subject to energy efficiency improvement using same, which can select a target building requiring energy saving by analyzing the degree of change in energy consumption of buildings over time. The present invention can estimate the energy consumption of a building by benchmarking the energy consumption of the building over time against building energy consumption including benchmarking information. Specifically, by measuring the energy performance of buildings and comparing the energy performance of the building subject to analysis with the measured energy performance of the buildings, if the energy consumption of the building subject to analysis is inefficient, it is possible to perform supplementary actions capable of improving the energy consumption efficiency of the building subject to analysis.

Description

A server for selecting buildings subject to energy efficiency improvement and a method for selecting buildings subject to energy efficiency improvement using the server.

[0001] The present invention relates to a server for selecting a building as a target for energy efficiency improvement and a method for selecting a building as a target for energy efficiency improvement using the same, and more specifically, to a server for selecting a building as a target for energy efficiency improvement capable of selecting a building as a target for energy efficiency improvement requiring energy savings by analyzing the degree of change in the building's energy consumption over time, and a method for selecting a building as a target for energy efficiency improvement using the same.

[0002] A Building Energy Management System (BEMS) is a system that efficiently manages the energy used within a building. Specifically, it collects and analyzes various information from energy management facilities within the building to improve energy efficiency. Using the BEMS can result in energy savings of 5-15% on average. Therefore, with the recent strengthening of various laws and standards regarding building energy consumption and the growing need for energy conservation, there is a growing trend toward actively adopting BEMS. The BEMS establishes a building energy model based on the characteristics of the building and controls and manages the operation of the building's energy facilities based on this model.

[0003] However, existing building energy models used to implement building energy management systems are initially established during construction and remain in place throughout the building's lifespan. Consequently, changes in energy performance due to aging or replacement of building and facility systems are not reflected. Furthermore, if managers rely on experience to control operations, significant energy waste can occur.

[0004] Furthermore, energy consumption in buildings has recently emerged as a major cause of environmental problems. Carbon emissions increase as electricity, gas, and other consumption in buildings increase depending on the weather, and reducing energy consumption in buildings is being proposed as a prerequisite for achieving carbon neutrality.

[0005] [Prior Art Literature]

[0006] [Patent Document]

[0007] (Patent Document 1) Korean Patent No. 10-1653763 (August 29, 2016)

[0008] The purpose of the present invention is to provide a server for selecting a building for energy efficiency improvement, which analyzes the degree of change in energy consumption of a building over time to select a building for energy conservation, and a method for selecting a building for energy efficiency improvement using the server.

[0009] A method for selecting a building as a target for energy efficiency improvement based on energy usage benchmarking information of a server of the present invention comprises the steps of: obtaining energy usage data for a plurality of buildings; identifying at least one change point information for the energy usage data; dividing the plurality of buildings into a plurality of categories; generating basic load information based on the energy usage data; identifying a heating balance temperature and a cooling balance temperature from the change point information; generating heating sensitivity information for the energy usage data based on the heating balance temperature; generating cooling sensitivity information for the energy usage data based on the cooling balance temperature; generating trend information for the heating sensitivity information and the cooling sensitivity information, wherein the trend information corresponds to time-series change amount information for the heating sensitivity information and the cooling sensitivity information; generating the energy usage benchmarking information for the plurality of categories based on the basic load information, the heating balance temperature, the cooling balance temperature, the heating sensitivity information, the cooling sensitivity information, and the trend information; acquiring analysis target data corresponding to energy usage data of an analysis target building; selecting the analysis target from among the plurality of categories. The method may include a step of identifying an analysis target category including a building, a step of identifying specific information of the analysis target data based on the energy usage benchmarking information for the analysis target category, and a step of selecting the analysis target building as the energy saving target building based on the specific information.

[0010] In an embodiment, in the step of classifying into the plurality of categories, the server may classify the plurality of buildings into the plurality of categories based on the use information for the plurality of buildings.

[0011] In an embodiment, the method may further include a step of obtaining energy cost information over time and a step of generating at least one of heating cost sensitivity information and cooling cost sensitivity information by reflecting the energy cost information in at least one of the heating sensitivity information and the cooling sensitivity information.

[0012] In an embodiment, a building targeting energy efficiency can be selected based on energy usage benchmarking information.

[0013] In an embodiment, the energy usage data may include usage data for the first energy and usage data for the second energy, respectively, and the energy usage benchmarking information may include usage benchmarking information for the first energy and usage benchmarking information for the second energy.

[0014] In an embodiment, the step of identifying change-point information may include a step of performing change-point regression analysis on the energy usage data.

[0015] In an embodiment, the heating sensitivity information may be information on the amount of change in energy usage for an external temperature in a range below the heating balance temperature.

[0016] In an embodiment, the cooling sensitivity information may be information on the amount of change in energy usage for an external temperature in a range above the cooling balance temperature.

[0017] In an embodiment, the basic load information may be generated based on energy usage information in a section between the heating balance temperature and the cooling balance temperature.

[0018] A server for selecting a building as a target for energy efficiency improvement based on energy usage benchmarking information of the present invention comprises: a memory; and a processor connected to the memory and configured to execute commands included in the memory, wherein the processor comprises: a step of obtaining energy usage data for a plurality of buildings; a step of identifying at least one change point information for the energy usage data; a step of dividing the plurality of buildings into a plurality of categories; a step of generating basic load information based on the energy usage data; a step of identifying a heating balance temperature and a cooling balance temperature from the change point information; a step of generating heating sensitivity information for the energy usage data based on the heating balance temperature; a step of generating cooling sensitivity information for the energy usage data based on the cooling balance temperature; a step of generating trend information for the heating sensitivity information and the cooling sensitivity information, wherein the trend information corresponds to time-series change amount information for the heating sensitivity information and the cooling sensitivity information; The method may be configured to perform a step of generating the energy usage benchmarking information for the plurality of categories based on the base load information, the heating balance temperature, the cooling balance temperature, the heating sensitivity information, the cooling sensitivity information, and the trend information; a step of acquiring analysis target data corresponding to energy usage data of the analysis target building; a step of identifying an analysis target category including the analysis target building among the plurality of categories; a step of identifying specific information of the analysis target data based on the energy usage benchmarking information for the analysis target category; and a step of selecting the analysis target building as the energy saving target building based on the specific information.

[0019] The present invention has the advantage of being able to estimate the energy consumption of a building by benchmarking the energy consumption of the building over time against the energy consumption of the building including benchmarking information in order to select a building as a target for energy efficiency improvement.

[0020] Specifically, the energy performance of a building can be measured and compared with the measured energy performance of the building being analyzed. If the energy consumption of the building being analyzed is inefficient, supplementary measures can be taken to improve its energy efficiency.

[0021] Additionally, there is an advantage in that energy consumption can be measured for each building analyzed based on the estimated energy consumption, and buildings targeted for energy efficiency can be set based on the measured energy consumption.

[0022] FIG. 1 is a diagram illustrating an environment for selecting a building to be improved to energy efficiency through a server for selecting a building to be improved to energy efficiency according to an embodiment of the present invention.

[0023] FIG. 2 is a diagram illustrating the configuration of a server for selecting a building to be improved in energy efficiency according to an embodiment of the present invention.

[0024] FIG. 3 is a diagram illustrating a process for selecting a building to be improved in energy efficiency according to an embodiment of the present invention.

[0025] Figure 4 is a diagram showing an expected energy savings of a target building through cooling sensitivity information measured to select a target building for energy efficiency improvement of the present invention.

[0026] FIG. 5 is a diagram illustrating the results of a change-point regression analysis based on electricity usage of a target building to select a target building for energy efficiency improvement of the present invention.

[0027] Hereinafter, embodiments disclosed in this specification will be described in detail with reference to the attached drawings. Regardless of the drawing reference numerals, identical or similar components will be assigned the same reference numerals, and redundant descriptions thereof will be omitted. Furthermore, when describing embodiments disclosed in this specification, if a detailed description of a related known technology is judged to obscure the gist of the embodiments disclosed in this specification, the detailed description thereof will be omitted.

[0028] Terms that include ordinal numbers, such as first, second, etc., may be used to describe various components, but the components are not limited by these terms. These terms are used solely to distinguish one component from another.

[0029] Singular expressions include plural expressions unless the context clearly indicates otherwise.

[0030] In this application, each step described may be performed regardless of the listed order, except in cases where a special causal relationship requires that the steps be performed in the listed order.

[0031] In this application, terms such as “include” or “have” are intended to specify the presence of a feature, number, step, operation, component, part or combination thereof described in the specification, but should be understood not to exclude in advance the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts or combinations thereof.

[0032] Hereinafter, the present invention will be described with reference to the attached drawings.

[0033] FIG. 1 is a diagram illustrating an environment for selecting a building to be improved to energy efficiency through a server for selecting a building to be improved to energy efficiency according to an embodiment of the present invention, and FIG. 2 is a diagram illustrating a configuration of a server for selecting a building to be improved to energy efficiency according to an embodiment of the present invention.

[0034] An environment (10) for selecting a building to be improved in energy efficiency according to an embodiment of the present invention can be implemented from a server (100) that stores conditions for selecting a building to be improved in energy efficiency analysis (50) and selects a building to be improved in energy efficiency according to the conditions.

[0035] Specifically, the server (100) can propose an energy benchmarking framework for multiple buildings (B) to select target buildings that require energy conservation, energy operation supplementation, etc.

[0036] In an embodiment, the server (100) can decompose monthly energy consumption data of about 75,000 buildings by time to select buildings to be analyzed, and quantify the monthly energy consumption data of the decomposed buildings to estimate the increase or decrease in energy over time (season).

[0037] Specifically, the change in energy consumption (e.g., gas and electricity) over time (season) of buildings (B) can be measured, and the building's energy consumption rate and energy consumption level can be estimated over time (season). Thereafter, the degree of energy savings can be estimated based on whether the change in energy consumption over time (season) of the target building deviates to some extent from the pattern of the buildings' energy consumption over time (season).

[0038] Such a server (100) may be configured to include a first data generation unit (120), a second data generation unit (140), an identification unit (160), a memory (180), and a processor (190).

[0039] The first data generation unit (120) is configured to obtain energy consumption data for multiple buildings for benchmarking the energy usage of the target building (50) to be analyzed.

[0040] Specifically, the first data generation unit (120) can acquire energy usage. Here, "energy usage" may refer to, for example, electricity and gas usage based on time (season). In the following description, electricity usage is assumed to be the first energy usage, and gas usage is assumed to be the second energy usage.

[0041] The first data generation unit (120) can quantify monthly energy usage trends through the acquired energy usage data, and by acquiring the monthly energy usage trends, it can determine the degree of seasonal fluctuations and the direction of increase or decrease in energy usage. While the embodiment of the present invention exemplifies acquiring monthly energy usage trends through energy usage data, it is of course also possible to quantify energy usage trends over time by acquiring hourly (24-hour) energy usage trends.

[0042] Once energy usage data is acquired, at least one changepoint can be identified for the acquired energy usage data. Changepoint information refers to performing changepoint regression analysis on the energy usage data. Through changepoint regression analysis, monthly energy bills and outdoor temperature data can be used to estimate the energy consumed by the building's heating, cooling, and base load.

[0043] Specifically, the change point regression analysis estimates the building energy usage according to weather changes and the energy used by the building itself (base load) regardless of the weather.

[0044] After identifying change point information for energy usage data, the first data generation unit (120) can standardize building energy usage based on building characteristics or group buildings into households based on arbitrary characteristics. In other words, multiple buildings can be categorized into multiple categories. For example, multiple buildings can be categorized based on their intended use (e.g., commercial, apartment, multi-purpose building, etc.). By categorizing multiple buildings into multiple categories, the average energy consumption by building intended use can be estimated.

[0045] In the embodiment, an example is given of classifying categories based on the purpose of the building, but it could also be classified based on the height of the building, the purpose of the building use (school, hospital, shopping center, etc.), etc.

[0046] Additionally, the first data generation unit (120) can generate basic load information for the building based on previously acquired energy usage data. Basic load information may refer to the basic electricity and gas consumption generated by the building itself, other than when energy is consumed by cooling or heating the building.

[0047] In the embodiment, the first data generation unit (120) can identify the heating balance temperature and the cooling balance temperature from the change point information. The heating balance temperature and the cooling balance temperature can be defined as the average heating temperature and the average cooling temperature, respectively. Based on the identified heating balance temperature and cooling balance temperature, basic load information can be estimated. The basic load information can be based on energy usage information in the interval between the heating balance temperature and the cooling balance temperature.

[0048] Additionally, the first data generation unit (120) can generate heating sensitivity information for energy usage data based on the heating balance temperature, and can generate cooling sensitivity information for energy usage data based on the cooling balance temperature. Heating and cooling sensitivity can refer to the degree of change in a building's heating energy or cooling energy when the building's external temperature changes.

[0049] As described, changepoint regression analysis is a technique for estimating a building's heating sensitivity, cooling sensitivity, heating balance temperature, cooling balance temperature, and base load based on monthly energy bills and outdoor temperature data. If the heating and cooling sensitivity of a building changes due to weather changes, based on the building's heating and cooling and base load energy usage estimated through changepoint regression analysis, the building's energy consumption can be estimated to be high.

[0050] Thereafter, the first data generation unit (120) can generate trend information for heating and cooling sensitivity information. Trend information may refer to a time-series change amount for heating and cooling sensitivity information.

[0051] Building benchmarking information can be generated based on this base load information, heating and cooling balance temperature, heating and cooling sensitivity information, and trend information. While building benchmarking information can be generated for each building, it would be preferable to generate benchmarking information by category, as previously mentioned, to create a baseline benchmarking information based on the building's intended use.

[0052] Afterwards, energy consumption data for the building to be analyzed (50) can be obtained through the second data generation unit (140).

[0053] Specifically, the second data generation unit (140) acquires analysis target data corresponding to the energy usage data of the analysis target building, and then identifies the analysis target category that includes the analysis target building among the multiple categories previously classified through the identification unit (160). In other words, the purpose is to identify the purpose of the building to be analyzed and estimate the energy consumption of the analysis target building.

[0054] Afterwards, based on the energy usage benchmarking information for the target category, anomalies in the analysis data can be identified. Specifically, if the previously generated benchmarking information pertains to apartments and the target building is an apartment, anomalies in the energy usage of the target building can be identified based on the benchmarking information generated for the apartment.

[0055] For example, if the energy consumption of the building being analyzed for cooling costs in the summer is higher than the energy consumption of buildings (apartments) generated from benchmarking data, the building is considered to be experiencing unnecessary energy consumption. The building can then be designated as an energy-saving target and supplementary work, such as interior renovations, structural replacement, and installations, can be undertaken to reduce heating and cooling costs.

[0056] Furthermore, by categorizing buildings for benchmarking by their construction date, we can estimate the energy consumption of buildings over a given period. For example, we can categorize buildings by construction date every five years, in the following order: buildings built within the last five years, buildings built between 6 and 10 years, and buildings built between 11 and 15 years. Assuming that the buildings we want to benchmark are classified by construction date in this way, we can estimate the energy consumption of the buildings for each construction date. Based on this, we can generate energy consumption data for the buildings under analysis and identify unusual information by comparing it to the energy consumption of the benchmarked buildings. This allows us to estimate unnecessary energy consumption based on the age of the buildings under analysis.

[0057] As a result, by analyzing monthly energy consumption data, we can identify the building's energy use pattern, and if the building under analysis is estimated to have increased energy consumption, we can conduct supplementary inspections to reduce energy consumption in the building under analysis.

[0058] The memory (180) can store operation codes for the server (100) to operate and operation data for estimating the level of energy consumption for multiple buildings.

[0059] The processor (190) can control the overall operation of the server (100). The processor (190) processes signals, data, information, etc. input or output through the components discussed above, or runs an application program stored in the memory (120) to provide or process appropriate information or functions for estimating the energy consumption of buildings.

[0060] FIG. 3 is a diagram illustrating a process for selecting a building to be improved in energy efficiency according to an embodiment of the present invention, FIG. 4 is a diagram illustrating an expected energy saving rate of a building to be improved in energy efficiency according to the present invention through cooling sensitivity information measured to select a building to be improved in energy efficiency according to the present invention, and FIG. 5 is a diagram illustrating the results of analyzing change-point regression according to electricity usage of a building to be improved in energy efficiency according to the present invention.

[0061] Referring to the drawing, energy usage data can be obtained (S110) based on changes in energy (e.g., gas and electricity) usage according to changes in time (season) of buildings through the server (100). Here, energy usage data may refer to, for example, electricity and gas usage according to time (season). In particular, energy usage data can be converted into energy usage benchmarking information, and the energy usage benchmarking information can serve as reference data for analyzing energy usage information of the target building, including each of the first and second energy usage benchmarking information.

[0062] These energy usage data capture changes in energy use (e.g., gas and electricity) over time (seasonal). This provides information on a building's energy usage rate and energy usage level over time (seasonal).

[0063] Thereafter, at least one change point information can be identified for the acquired energy usage data (S120). Change point information refers to performing change point regression analysis on the energy usage data.

[0064] Specifically, referring to Figure 4, the monthly power consumption (50_U) of the target building is represented by the gray dot, and the optimal change point regression analysis (CPR) can be represented by the red graph. Based on this, the balanced temperatures for cooling and heating in the target building can be found to be 6.8℃ and 17.4℃, respectively. Based on this, it can be seen that the heating system can be operated when the outdoor temperature is 6.8℃ or lower based on the average outdoor temperature, and the cooling system is operated when the outdoor temperature is higher than 17.4℃.

[0065] In addition, the heating sensitivity performance of the building under analysis (0.22 kWh / m 2 / ℃) is the cooling sensitivity performance (0.77kWh / m 2 / ℃), it can be seen that the heating sensitivity performance of the building under analysis is lower than the cooling sensitivity performance, indicating that building remodeling is necessary for the heating efficiency of the building under analysis.

[0066] After identifying change point information for energy usage data, building energy usage can be standardized based on building characteristics or buildings can be grouped into households based on arbitrary characteristics (S130).

[0067] Meanwhile, energy performance indicators (EPIs), which are benchmarking information for a building's energy usage, can be generated based on energy usage data (S140). Specifically, the building's energy usage benchmarking information can be generated based on base load information, heating and cooling balance temperatures, heating and cooling sensitivity information, and trend information.

[0068] First, base load information can refer to the basic electricity and gas consumption generated by the building itself, other than when energy is consumed to cool or heat the building.

[0069] The heating balance temperature and cooling balance temperature can be said to be the average temperature of heating and the average temperature of cooling, and the basic load information can be based on energy usage information in the section between the heating balance temperature and the cooling balance temperature.

[0070] Additionally, heating sensitivity information can be generated for energy usage data based on the heating balance temperature, and cooling sensitivity information can be generated for energy usage data based on the cooling balance temperature. Heating and cooling sensitivity can refer to the degree to which a building's heating or cooling energy changes when the building's external temperature changes.

[0071] Additionally, trend information may refer to information on time-series changes in heating and cooling sensitivity information.

[0072] Thereafter, energy consumption data for the target building (50) can be acquired (S150). The energy consumption of the target building can be acquired by identifying the purpose of the building and analyzing the analysis target data corresponding to the energy consumption data of the target building, such as the previously generated building energy consumption benchmarking information, and identifying the purpose of the building to be analyzed.

[0073] The energy consumption of the target building is benchmarked against the building's energy usage benchmarking information to identify unusual data (S160). For example, if the target building's energy consumption for cooling costs in the summer is higher than the energy consumption of a building (apartment) generated from the benchmarking information, the target building is deemed to be experiencing unnecessary energy consumption. Subsequently, the target building is selected as an energy-saving target building, and supplementary work, such as interior replacement, structural replacement, and installation, is performed to reduce heating and cooling costs.

[0074] Specifically, referring to Fig. 8, one can confirm the sensitivity distribution estimated from buildings of the same category (Fig. 8 (a)) and, based on this, the cooling sensitivity improvement plan for the building under analysis (Fig. 8 (b)).

[0075] The cooling sensitivity of the building under analysis is 0.77 kWh / m 2 / ℃, this is improved to 0.09 kWh / m 2 If improved to / ℃, the total energy consumption is approximately 46.68kWh / m 2 It can be judged that it is possible to save about / ℃.

[0076] In this way, information can be used to generate benchmarking information on energy usage of a building based on basic load information, heating and cooling balance temperature, heating and cooling sensitivity information, and trend information, depending on the embodiment, and to suggest an improvement plan to save energy consumption of the building being analyzed by matching the energy consumption of the building being analyzed to the benchmarking information.

[0077] The technical features disclosed in each embodiment of the present invention are not limited to that embodiment, and, unless they are mutually incompatible, the technical features disclosed in each embodiment may be combined and applied to different embodiments.

[0078] Therefore, although each embodiment focuses on its own technical features, each technical feature can be applied in combination with each other as long as they are not mutually incompatible.

[0079] The present invention is not limited to the above-described embodiments and the attached drawings, and various modifications and variations are possible within the scope of those skilled in the art. Therefore, the scope of the present invention should be defined not only by the claims of this specification but also by equivalents thereof.

Claims

1. In a method for selecting a building to be improved on energy efficiency based on energy usage benchmarking information, A step of obtaining energy usage data for multiple buildings; A step of identifying at least one change point information for the above energy usage data; A step of dividing the above multiple buildings into multiple categories according to their purpose; A step of identifying the heating balance temperature and the cooling balance temperature from the above change point information; A step of generating basic load information based on the above energy usage data, wherein the basic load information is based on energy usage information in a section between the heating balance temperature and the cooling balance temperature; A step of generating heating sensitivity information for the energy usage data based on the above heating balance temperature; A step of generating cooling sensitivity information for the energy usage data based on the above cooling balance temperature; A step of generating trend information on the above heating sensitivity information and the above cooling sensitivity information, wherein the trend information corresponds to time-series change amount information on the heating sensitivity information and the above cooling sensitivity information; A step of generating energy usage benchmarking information for the plurality of categories based on the base load information, the heating balance temperature, the cooling balance temperature, the heating sensitivity information, the cooling sensitivity information, and the trend information; A step of acquiring analysis target data corresponding to energy usage data of the analysis target building; A step of identifying an analysis target category in which the analysis target building is included among the above multiple categories; A step of identifying specific information of the analysis target data based on the energy usage benchmarking information for the analysis target category; and A step of selecting the building to be analyzed as the building to be subject to energy saving based on the above-mentioned specific information is included. The above energy usage benchmarking information is generated based on information about the construction time classified into predetermined cycles within the above categories, The above energy usage benchmarking information is generated based on the energy usage in the section between the heating balance temperature and the cooling balance temperature among the above basic load information. A method for selecting buildings to be energy efficient based on energy usage benchmarking information.

2. In paragraph 1, In the step of classifying into the above multiple categories, the server classifies the multiple buildings into multiple categories based on the purpose information for the multiple buildings. A method for selecting buildings to be energy efficient based on energy usage benchmarking information.

3. In paragraph 1, Step of obtaining energy cost information over time; and A method further comprising the step of generating at least one of heating cost sensitivity information and cooling cost sensitivity information by reflecting the energy cost information in at least one of the heating sensitivity information and the cooling sensitivity information. A method for selecting buildings to be energy efficient based on energy usage benchmarking information.

4. In paragraph 1, The above energy usage data includes usage data for the first energy and usage data for the second energy, respectively. The above energy usage benchmarking information includes usage benchmarking information for the first energy and usage benchmarking information for the second energy. A method for selecting buildings to be energy efficient based on energy usage benchmarking information.

5. In paragraph 1, In the step of identifying the above change point information, Comprising a step of performing change-point regression analysis on the above energy usage data. A method for selecting buildings to be energy efficient based on energy usage benchmarking information.

6. In paragraph 1, The above heating sensitivity information is, Information on the change in energy usage for the external temperature in the section below the above heating balance temperature A method for selecting buildings to be energy efficient based on energy usage benchmarking information.

7. In paragraph 1, The above cooling sensitivity information is, Information on the change in energy usage for the external temperature in the range above the above cooling balance temperature A method for selecting buildings to be energy efficient based on energy usage benchmarking information.

8. Memory; and A processor connected to said memory and configured to execute instructions contained in said memory, The above processor, A step of obtaining energy usage data for multiple buildings; A step of identifying at least one change point information for the above energy usage data; A step of dividing the above multiple buildings into multiple categories according to their purpose; A step of identifying the heating balance temperature and the cooling balance temperature from the above change point information; A step of generating basic load information based on the above energy usage data, wherein the basic load information is based on energy usage information in a section between the heating balance temperature and the cooling balance temperature; A step of generating heating sensitivity information for the energy usage data based on the above heating balance temperature; A step of generating cooling sensitivity information for the energy usage data based on the above cooling balance temperature; A step of generating trend information on the above heating sensitivity information and the above cooling sensitivity information, wherein the trend information corresponds to time-series change amount information on the heating sensitivity information and the above cooling sensitivity information; A step of generating energy usage benchmarking information for the plurality of categories based on the base load information, the heating balance temperature, the cooling balance temperature, the heating sensitivity information, the cooling sensitivity information, and the trend information; A step of acquiring analysis target data corresponding to energy usage data of the analysis target building; A step of identifying an analysis target category in which the analysis target building is included among the above multiple categories; A step of identifying specific information of the analysis target data based on the energy usage benchmarking information for the analysis target category; and It is configured to perform a step of selecting the building to be analyzed as the building to be subject to energy saving based on the above-mentioned specific information, The above energy usage benchmarking information is generated based on information about the construction time classified into predetermined cycles within the above categories, The above energy usage benchmarking information is generated based on the energy usage in the section between the heating balance temperature and the cooling balance temperature among the above basic load information. A server that selects buildings to improve energy efficiency based on energy usage benchmarking information.

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