A server for selecting buildings to be targeted for energy efficiency improvements, and a method for selecting buildings to be targeted for energy efficiency improvements using that server.

Abstract

This invention discloses a server for selecting buildings that require energy conservation by analyzing the degree of change in a building's energy consumption over time, and a technology for selecting buildings that require energy conservation using this server, as well as a method for selecting buildings that require energy conservation using this server.

Description

Technical Field

[0001] The present invention relates to a server for selecting a building to be energy-efficient and a method for selecting a building to be energy-efficient using the same. More specifically, it relates to a server for selecting a building to be energy-efficient that can analyze the degree of change in the energy consumption of a building over time and select a target building that requires energy conservation, and a method for selecting a building to be energy-efficient using the same.

Background Art

[0002] Generally, a building energy management system (BEMS) is a system for efficiently managing the energy used in a building. That is, it is a system that collects and analyzes various information on energy management facilities in a building and improves the energy usage efficiency. By using the building energy management system, an average of 5 to 15% of energy can be saved. Therefore, recently, as various laws and standards regarding building energy consumption have been strengthened and the need for energy conservation has increased, there has been a tendency to actively introduce the building energy management system. The building energy management system constructs a building energy model based on the characteristics of the building and controls and manages the operation of the building's energy facilities using the constructed building energy model.

[0003] However, the building energy model for implementing the conventional building energy management system is constructed at the initial stage during construction and is used as it is until the building reaches the end of its life. Therefore, there is a problem that changes in energy performance due to aging or replacement of the building and equipment system are not reflected. When the administrator controls the operation depending on experience, there is a problem that serious energy waste occurs.

[0004] Furthermore, energy consumption in buildings has recently emerged as a major cause of environmental problems. As energy consumption in buildings increases depending on the weather, leading to increased carbon emissions from electricity, gas, and other resources, reducing building energy consumption is being presented as a condition for carbon neutrality. [Prior art documents] [Patent Documents]

[0005] [Patent Document 1] Korean Registered Patent Publication No. 10-1653763 [Overview of the Initiative] [Problems that the invention aims to solve]

[0006] The present invention aims to provide a server for selecting target buildings for energy efficiency improvements, which analyzes the degree of change in a building's energy consumption over time and enables the selection of target buildings that require energy conservation, and a method for selecting target buildings for energy efficiency improvements using the same. [Means for solving the problem]

[0007] The method by which the server of the present invention selects buildings to be targeted for energy efficiency improvement based on energy consumption benchmarking information includes the steps of: acquiring energy consumption data for a plurality of buildings; identifying at least one change point information relating to the energy consumption data; classifying the plurality of buildings into a plurality of categories; generating basic load information based on the energy consumption data; identifying heating balance temperature and cooling balance temperature from the change point information; generating heating sensitivity information relating to the energy consumption data based on the heating balance temperature; generating cooling sensitivity information relating to the energy consumption data based on the cooling balance temperature; and generating trend information for the heating sensitivity information and the cooling sensitivity information. The process may include: - The trend information corresponds to time-series change amount information relating to the heating sensitivity information and the cooling sensitivity information - and generating energy consumption benchmarking information for the multiple 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 data to be analyzed, which corresponds to energy consumption data of the building under analysis; identifying the category to be analyzed from the multiple categories to which the building under analysis belongs; identifying unique information of the data to be analyzed based on the energy consumption benchmarking information relating to the category under analysis; and selecting the building under analysis as a building to be targeted for energy conservation based on the unique information.

[0008] In the embodiment, in the step of dividing into multiple categories, the server may divide the multiple buildings into multiple categories based on usage information for the multiple buildings.

[0009] In the embodiment, the steps may further include obtaining energy cost information over time, and reflecting the energy cost information in at least one of the heating sensitivity information and the cooling sensitivity information to generate at least one of the heating cost sensitivity level and the cooling cost sensitivity level.

[0010] In the embodiment, buildings to be targeted for energy efficiency improvement can be selected based on energy consumption benchmarking information.

[0011] In the embodiment, the energy consumption data includes energy consumption data for a first energy source and energy consumption data for a second energy source, and the conventional energy consumption benchmarking information may include energy consumption benchmarking information for the first energy source and energy consumption benchmarking information for the second energy source.

[0012] In the embodiment, the step of identifying conventional change point information may include the step of performing change-point regression analysis on the energy consumption data.

[0013] In the embodiment, the heating sensitivity information may be information relating to the change in energy consumption with respect to the external temperature in the interval below the heating balance temperature.

[0014] In the embodiment, the cooling sensitivity information may be information relating to the change in energy consumption with respect to the external temperature in the range above the cooling balance temperature.

[0015] In the embodiment, the basic load information may be generated based on energy consumption information in the interval between the heating balance temperature and the cooling balance temperature.

[0016] The server for selecting buildings to be targeted for energy efficiency improvement based on energy consumption benchmarking information of the present invention includes a memory and a processor connected to the memory and configured to execute instructions contained in the memory, wherein the processor performs the steps of: acquiring energy consumption data for a plurality of buildings; identifying at least one change point information relating to the energy consumption data; classifying the plurality of buildings into a plurality of categories; generating basic load information based on the energy consumption data; identifying heating balance temperature and cooling balance temperature from the change point information; generating heating sensitivity information relating to the energy consumption data based on the heating balance temperature; generating cooling sensitivity information relating to the energy consumption data based on the cooling balance temperature; and the heating sensitivity information The system may be configured to perform the following steps: generate trend information for the cooling sensitivity information—the trend information corresponds to time-series change amount information relating to the heating sensitivity information and the cooling sensitivity information; generate energy consumption benchmarking information for the multiple 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; acquire data to be analyzed, which corresponds to energy consumption data of the building under analysis; identify the category to be analyzed from the multiple categories to which the building under analysis is included; identify unique information of the data to be analyzed based on the energy consumption benchmarking information relating to the category to be analyzed; and select the building under analysis as a building to be targeted for energy conservation based on the unique information. [Effects of the Invention]

[0017] This invention has the advantage of being able to estimate a building's energy consumption by benchmarking the building's energy consumption over time against the building's energy consumption data, which includes benchmarking information, in order to select a building to be targeted for energy efficiency improvement.

[0018] Specifically, the energy performance of the building to be analyzed, which attempts to measure and analyze the energy performance of the building, can be compared with the energy performance of the building whose energy performance has been measured. Next, when the energy consumption of the building to be analyzed is inefficient, complementary work can be performed to improve the energy consumption efficiency of the building to be analyzed.

[0019] In addition, there is an advantage that the energy consumption can be measured for each building to be analyzed based on the estimated energy consumption, and the buildings to be energy-efficient can be set based on the measured energy consumption.

Brief Description of Drawings

[0020] [Figure 1] It is a diagram showing an environment for selecting a building to be energy-efficient by a server that selects a building to be energy-efficient according to an embodiment of the present invention. [Figure 2] It is a diagram showing the configuration of a server that selects a building to be energy-efficient according to an embodiment of the present invention. [Figure 3] It is a diagram showing a process for selecting a building to be energy-efficient according to an embodiment of the present invention. [Figure 4] It is a diagram showing the energy-saving prediction degree of a target building based on the cooling sensitivity information measured to select a building to be energy-efficient according to the present invention. [Figure 5] It is a diagram showing the result of analyzing the change point regression analysis based on the electricity use of a target building to select a building to be energy-efficient according to the present invention.

Modes for Carrying Out the Invention

[0021] Hereinafter, the embodiments disclosed in the present invention will be described in detail with reference to the attached drawings. However, regardless of the reference numerals in the drawings, the same or similar components are given the same reference numerals and the overlapping descriptions thereof are omitted. In addition, when explaining the embodiments disclosed in this specification, if it is determined that the specific description of the related known technology may obscure the gist of the embodiments disclosed in this specification, the detailed description thereof will be omitted.

[0022] Terms including ordinal numbers, such as "first," "second," etc., may be used to describe a variety of components, but the components are not limited to those specified by these terms. These terms are used solely for the purpose of distinguishing one component from another.

[0023] A singular expression includes plural expressions unless the context clearly indicates otherwise.

[0024] In this application, each step described may be performed in any order, except where such an order is to be followed by a specific causal relationship.

[0025] In this application, terms such as “includes” or “having” should be understood to indicate the existence of features, figures, steps, actions, components, parts, or combinations thereof described in the specification, without prejudice to the existence or possibility of adding one or more other features, figures, steps, actions, components, parts, or combinations thereof.

[0026] The present invention will be described below with reference to the attached drawings. Figure 1 shows the environment for selecting buildings to be targeted for energy efficiency improvement by a server according to an embodiment of the present invention, and Figure 2 shows the configuration of the server for selecting buildings to be targeted for energy efficiency improvement according to an embodiment of the present invention.

[0027] The environment 10 for selecting a building to be targeted for energy efficiency improvement according to an embodiment of the present invention can be realized by a server 100 that stores conditions for selecting a building 50 to be analyzed for energy saving and selects a building to be targeted for energy efficiency improvement based on those conditions.

[0028] More specifically, server 100 can propose an energy benchmarking framework for multiple buildings B in order to select target buildings that require energy conservation, energy management support, etc.

[0029] In this embodiment, the server 100 can select buildings to be analyzed by breaking down monthly energy consumption data for approximately 75,000 buildings into hourly segments, quantifying the monthly energy consumption data of the broken-down buildings, and estimating the increase or decrease in energy consumption over time (season).

[0030] More specifically, by measuring the changes in energy use (e.g., gas and weather) in Building B due to changes in time (season), it is possible to estimate the building's energy use ratio and energy use level due to changes in time (season). Next, the degree of energy conservation can be estimated by determining how much the changes in the target building's energy use due to changes in time (season) deviate from the building's energy use change pattern due to changes in time (season).

[0031] 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.

[0032] The first data generation unit 120 is configured to acquire energy consumption data for multiple buildings in order to benchmark the energy usage of the building 50 under analysis.

[0033] More specifically, the first data generation unit 120 can acquire energy consumption. Here, energy consumption can mean, for example, the amount of electricity and gas used over time (season). In this embodiment, we will assume that the amount of electricity used is the first energy consumption and the amount of gas used is the second energy consumption for the explanation.

[0034] The first data generation unit 120 can quantify monthly energy usage trends based on the acquired energy usage data, and by acquiring monthly energy usage trends, it is possible to grasp the degree of seasonal fluctuations in energy usage and the direction of increase or decrease. On the other hand, in the embodiment of the present invention, an example of acquiring monthly energy usage trends based on energy usage data is given and explained, but it is of course possible to acquire hourly (24-hour) energy usage trends and quantify the energy usage trends over time.

[0035] Once energy consumption data is acquired, at least one change point of information related to the acquired energy consumption data can be identified. Change point information means performing a change point regression analysis on the energy consumption data, which can be used to estimate the energy consumed by building heating, cooling, and the building's basic load using monthly energy charges and outdoor temperature data.

[0036] More specifically, change point regression analysis is used to estimate the energy consumption of a building due to weather changes, as well as the energy used by the building itself regardless of the weather (basic load).

[0037] After identifying change point information for energy consumption data, the first data generation unit 120 can standardize the energy use of buildings according to their characteristics, or group buildings indoors based on arbitrary features. In other words, multiple buildings can be divided into multiple categories. For example, multiple buildings can be divided into multiple categories based on their intended use (e.g., shopping district, apartments, mixed-use buildings, etc.). By dividing multiple buildings into multiple categories, the average energy consumption for each building use can be estimated.

[0038] In the examples provided, we will explain how to classify buildings by their use, but other classifications may also be made based on the building's height, intended use (school, hospital, shopping center, etc.), etc.

[0039] Furthermore, the first data generation unit 120 can generate basic load information for the building based on the energy usage data acquired earlier. Basic load information may refer to the basic electricity and gas consumption generated by the building itself, excluding the time when the building is being cooled or heated and consuming energy.

[0040] In this 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 said to be the average heating temperature and the average cooling temperature. Based on the identified heating balance temperature and cooling balance temperature, basic load information can be estimated, and the basic load information is obtained based on energy consumption information in the interval between the heating balance temperature and the cooling balance temperature.

[0041] Furthermore, the first data generation unit 120 can generate heating sensitivity information related to energy consumption data based on the heating balance temperature, and can generate cooling sensitivity information related to energy consumption data based on the cooling balance temperature. Cooling and heating sensitivity may refer to the degree of change in the building's heating energy or cooling energy when the building's external temperature changes.

[0042] As explained, change point regression analysis is a technique for estimating a building's heating sensitivity, cooling sensitivity, heating balance temperature, cooling balance temperature, and basic load based on monthly energy charges and outdoor temperature data. If heating and cooling sensitivity changes due to weather changes, based on the energy consumption of heating, cooling, and basic load estimated by change point regression analysis, it can be estimated that the building's energy consumption is high.

[0043] Next, the first data generation unit 120 can generate trend information for the heating and cooling sensitivity information. Trend information may mean the amount of change over time for the heating and cooling sensitivity information.

[0044] Based on this basic load information, heating and cooling balance temperature, heating and cooling sensitivity information, and trend information, benchmarking information for a building can be generated. While benchmarking information for a building can be generated for each individual building, it is preferable to generate it according to the previously classified categories and generate standard benchmarking information according to the building's use.

[0045] Next, the second data generation unit 140 can acquire energy consumption data for the building 50 under analysis.

[0046] More specifically, the second data generation unit 140 acquires the data to be analyzed, which corresponds to the energy consumption data of the building under analysis. Then, the identification unit 160 can identify the category to which the building under analysis belongs from among the previously classified categories. In other words, it attempts to estimate the energy consumption of the building under analysis by identifying its intended use.

[0047] Next, unique information in the data to be analyzed can be identified based on benchmarking information for energy consumption in the category being analyzed. Specifically, if the benchmarking information generated earlier is about apartments, and the building being analyzed is an apartment, then anomalies in the energy consumption of the building being analyzed are identified based on the benchmarking information for the apartments that was generated.

[0048] For example, if the energy consumption of the building under analysis for air conditioning costs in the summer is higher than the energy consumption of a building (apartment) generated from benchmarking information, it is determined that unnecessary energy consumption is occurring in the building under analysis. Next, the building under analysis is selected as a building to be improved for energy conservation, and supplementary work such as interior renovations, structural replacements, and installations can be carried out to reduce heating and cooling costs in the building under analysis.

[0049] Furthermore, by classifying and categorizing the buildings used for benchmarking at the time of construction, it is possible to estimate the energy consumption of the buildings within any given period. For example, buildings can be classified in 5-year cycles, such as buildings built within the last 5 years, buildings built within the last 6 to 10 years, and buildings built within the last 11 to 15 years. Assuming that the buildings to be benchmarked are those classified by their construction time, it is possible to estimate the energy usage of the buildings for each construction time. Based on this, the energy usage of the building under analysis can be generated, and by identifying unique information by comparing it with the energy usage of the benchmarked buildings, it is possible to estimate the year of construction of the building under analysis and unnecessary energy consumption due to its age.

[0050] As a result, after analyzing monthly energy consumption data to understand a building's energy usage patterns, if it is estimated that the building under analysis has increased its energy consumption, supplementary checks can be implemented to reduce the building's energy consumption.

[0051] Memory 180 can store operational code for the server 100 to operate, and operational data for estimating the degree of energy consumption for multiple buildings.

[0052] The processor 190 can control the overall operation of the server 100. The control unit 190 can process signals, data, information, etc. that are input or output via the above-described components, or drive application programs stored in the memory 180, to provide or process appropriate information or functions for estimating the energy consumption of the building.

[0053] Figure 3 shows the process for selecting a target building for energy efficiency improvement according to an embodiment of the present invention; Figure 4 shows the predicted energy savings of the target building based on cooling sensitivity information measured to select a target building for energy efficiency improvement according to the present invention; and Figure 5 shows the results of analyzing change point regression analysis based on electricity usage of the target building to select a target building for energy efficiency improvement according to the present invention.

[0054] Referring to the diagram, server 100 can acquire energy usage data for changes in energy (e.g., gas and electricity) usage due to changes in the building's time (season) S110. Here, energy usage data can mean, for example, the amount of electricity and gas used depending on the time (season). In particular, the energy usage data can be converted into energy usage benchmarking information, and the energy usage benchmarking information, including the respective first and second energy usage benchmarking information, can serve as reference data for analyzing the energy usage information of the building under analysis.

[0055] This type of energy consumption data is acquired in response to changes in energy use (e.g., gas and electricity) due to changes in time (season). In other words, information is acquired regarding the energy usage ratio and degree of energy use of a building as it changes over time (season).

[0056] Next, S120 identifies at least one change point information regarding the acquired energy consumption data. Change point information means performing a change point regression analysis on the energy consumption data.

[0057] For details, please refer to Figure 4, where the monthly electricity consumption of the building under analysis (50_U) is represented by the gray dots, and the optimal change point regression analysis (CPR) can be represented by the red graph. Based on this, it can be seen that the balance temperatures for cooling and heating in the building under analysis are 6.8°C and 17.4°C, respectively. Based on this, it can be seen that the heating system may operate when the outdoor temperature is 6.8°C or lower, and the cooling system may operate when the outdoor temperature is higher than 17.4°C, based on the average outdoor temperature.

[0058] Furthermore, the heating sensitivity performance of the building under analysis (0.22 kWh / m²) 2 (°C) is the cooling sensitivity performance (0.77kWh / m²). 2 Since it is shown as / ℃, it indicates that the heating sensitivity performance of the building under analysis is lower than its cooling sensitivity performance, and that building remodeling is necessary to improve the heating efficiency of the building under analysis.

[0059] After identifying change point information for energy consumption data, S130 allows for standardizing the building's energy use based on building characteristics or grouping buildings indoors based on arbitrary features.

[0060] On the other hand, S140 can generate energy performance indicators (EPIs_Energy Performance Indicators), which are benchmarking information for the building's energy consumption, based on energy consumption data. Specifically, the building's energy consumption benchmarking information can be generated based on basic load information, heating and cooling balance temperature, heating and cooling sensitivity information, and trend information.

[0061] First, basic load information can refer to the basic electricity and gas consumption generated by the building itself, excluding the time when energy is consumed by cooling or heating the building.

[0062] The heating balance temperature and cooling balance temperature can be described as the average heating temperature and the average cooling temperature, respectively, and the basic load information is obtained based on energy consumption information in the interval between the heating balance temperature and the cooling balance temperature.

[0063] Furthermore, heating sensitivity information regarding energy consumption data can be generated based on the heating balance temperature, and cooling sensitivity information regarding energy consumption data can be generated based on the cooling balance temperature. Cooling and heating sensitivity can refer to the degree to which the building's heating energy or cooling energy changes when the building's external temperature changes.

[0064] Furthermore, trend information can refer to information regarding the amount of change over time in heating and cooling sensitivity information.

[0065] Next, S150 can acquire energy consumption data for the building 50 under analysis. Similar to the energy usage benchmarking information for the building generated earlier, it can acquire the energy consumption data for the building under analysis by identifying the building's use and the building's use for analysis.

[0066] S160 acquires unique information by benchmarking the energy consumption of the building under analysis against the building's energy usage benchmarking information. For example, if the energy consumption of the building under analysis for air conditioning costs in the summer is higher than the energy consumption of the building (apartment) generated from the benchmarking information, it is determined that unnecessary energy consumption is occurring in the building under analysis. Next, the building under analysis is selected as a building to be reduced in energy consumption, and supplementary work such as replacing interiors, replacing and installing structural elements is carried out to reduce heating and cooling costs in the building under analysis.

[0067] For more details, refer to Figure 8 to see the estimated sensitivity distribution for buildings in the same category (Figure 8(a)) and, based on that, the proposed improvements to the air conditioning sensitivity of the building under analysis (Figure 8(b)).

[0068] The air conditioning sensitivity of the building under analysis was 0.77 kWh / m². 2 If it is / ℃, improve it to 0.09kWh / m 2 If the temperature is reduced to / ℃, the total energy consumption will be approximately 46.68 kWh / m³. 2 It can be estimated that a temperature difference of approximately / ℃ can be achieved.

[0069] In this way, the information generated based on the implementation example includes basic load information, heating and cooling balance temperature, heating and cooling sensitivity information, and trend information to create benchmark information on the building's energy consumption. By correlating the energy consumption of the building under analysis with this benchmark information, improvement plans to save energy consumption in the building under analysis can be presented.

[0070] The technical features disclosed in each embodiment of the present invention are not limited to that embodiment alone, and the technical features disclosed in each embodiment can be combined and applied to different embodiments, provided that they are not incompatible with each other.

[0071] Therefore, while each embodiment will be described primarily for its respective technical features, other technical features can be combined and applied as long as they are not mutually incompatible.

[0072] The present invention is not limited to the embodiments described above and the accompanying drawings, and is subject to various modifications and variations from the perspective of a person with ordinary skill in the art to which the present invention pertains. Therefore, the scope of the present invention should be determined not only by the claims herein but also by those equivalent to these claims.

Claims

1. In a method in which a server selects buildings for energy efficiency improvement based on energy consumption benchmarking information, Steps include obtaining energy usage data for multiple buildings, The steps include identifying at least one change point information relating to the energy consumption data, The steps include dividing the aforementioned multiple buildings into multiple categories according to their use, The steps include identifying the heating balance temperature and the cooling balance temperature from the aforementioned change point information, The steps include generating basic load information based on the energy consumption data, the basic load information being based on energy consumption information in the interval between the heating balance temperature and the cooling balance temperature, A step of generating heating sensitivity information related to the energy consumption data based on the heating balance temperature, The steps include generating cooling sensitivity information related to the energy consumption data based on the cooling balance temperature, The steps include generating trend information for the heating sensitivity information and the cooling sensitivity information, and the corresponding trend information to time-series change amount information for the heating sensitivity information and the cooling sensitivity information, A step of generating energy consumption 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, The steps include obtaining the data to be analyzed, which corresponds to the energy consumption data of the building being analyzed, The steps include: identifying the category of analysis that includes the building to be analyzed from among the aforementioned multiple categories; A step of identifying unique information of the data to be analyzed based on the energy consumption benchmarking information relating to the category to be analyzed, The step includes selecting the building to be analyzed as a building to be targeted for energy conservation based on the aforementioned unique information, The aforementioned energy consumption benchmarking information is generated based on information that classifies the construction time within the category at predetermined intervals. The aforementioned energy consumption benchmarking information is generated based on the energy consumption in the interval between the heating balance temperature and the cooling balance temperature from the basic load information. A method for selecting buildings to target for energy efficiency improvements based on energy consumption benchmarking information.

2. In the step of dividing into multiple categories, the server divides the multiple buildings into multiple categories based on usage information for the multiple buildings. A method for selecting buildings to be targeted for energy efficiency improvement based on energy consumption benchmarking information as described in claim 1.

3. Steps to obtain energy cost information over time, The step further includes reflecting the energy cost information in at least one of the heating sensitivity information and the cooling sensitivity information to generate at least one of the heating cost sensitivity level and the cooling cost sensitivity level. A method for selecting buildings to be targeted for energy efficiency improvement based on energy consumption benchmarking information as described in claim 1.

4. The aforementioned energy consumption data includes consumption data for the first energy source and consumption data for the second energy source, The aforementioned energy consumption benchmarking information includes consumption benchmarking information for the first energy and consumption benchmarking information for the second energy. A method for selecting buildings to be targeted for energy efficiency improvement based on energy consumption benchmarking information as described in claim 1.

5. In the step of identifying the change point information, This includes the step of performing a change-point regression analysis on the aforementioned energy consumption data. A method for selecting buildings to be targeted for energy efficiency improvement based on energy consumption benchmarking information as described in claim 1.

6. The aforementioned heating sensitivity information is, This information pertains to the change in energy consumption relative to the external temperature in the interval below the aforementioned heating balance temperature. A method for selecting buildings to be targeted for energy efficiency improvement based on energy consumption benchmarking information as described in claim 1.

7. The aforementioned air conditioning sensitivity information is, This information pertains to the change in energy consumption relative to the external temperature in the range above the aforementioned cooling balance temperature. A method for selecting buildings to be targeted for energy efficiency improvement based on energy consumption benchmarking information as described in claim 1.

8. Memory and The processor is connected to the memory and configured to execute instructions contained in the memory, The aforementioned processor, Steps to obtain energy usage data for multiple buildings, A step of identifying at least one change point information relating to the energy consumption data, The steps of classifying the aforementioned multiple buildings into multiple categories according to their use, A step of identifying the heating balance temperature and the cooling balance temperature from the aforementioned change point information. A step of generating basic load information based on the energy consumption data — the basic load information is based on energy consumption information in the interval between the heating balance temperature and the cooling balance temperature — A step of generating heating sensitivity information related to the energy consumption data based on the heating balance temperature, A step of generating cooling sensitivity information related to the energy consumption data based on the cooling balance temperature, A step of generating trend information for the heating sensitivity information and the cooling sensitivity information - the trend information corresponds to time-series change amount information for the heating sensitivity information and the cooling sensitivity information - A step of generating energy consumption 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. Steps to obtain the data to be analyzed, which corresponds to the energy consumption data of the building to be analyzed. A step of identifying the category of analysis to which the building to be analyzed is included among the aforementioned multiple categories, A step of identifying unique information of the data to be analyzed based on the energy consumption benchmarking information relating to the category to be analyzed, The system is configured to perform the step of selecting the building to be analyzed as a building to be targeted for energy conservation based on the aforementioned unique information. The aforementioned energy consumption benchmarking information is generated based on information that classifies the construction time within the category at predetermined intervals. The aforementioned energy consumption benchmarking information is generated based on the energy consumption in the interval between the heating balance temperature and the cooling balance temperature from the basic load information. A server that selects buildings for energy efficiency improvements based on energy consumption benchmarking information.

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