A building energy-saving technology evaluation system and method

Abstract

The application relates to a building energy-saving technology evaluation system and method, and relates to the technical field of data processing, wherein the method comprises the following steps: obtaining relevant data of a plurality of sample buildings, clustering the plurality of sample buildings, generating a plurality of building clustering clusters, establishing an evaluation system corresponding to the building clustering clusters and a plurality of evaluation benchmarks; obtaining relevant data of a target building, wherein the relevant data of the target building at least comprises building feature data, environment data and energy consumption data of the target building in a target time period; determining a target building clustering cluster from the plurality of building clustering clusters based on the relevant data of the target building; determining a target evaluation benchmark from the plurality of evaluation benchmarks corresponding to the target building clustering cluster based on the target time period; and evaluating the energy-saving effect of the building energy-saving technology to be evaluated based on the relevant data of the target building, the evaluation system corresponding to the target building clustering cluster and the target evaluation benchmark, so that the accuracy of building energy-saving technology evaluation is improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a building energy-saving technology assessment system and method. Background Technology

[0002] With the promotion and use of building energy efficiency systems, their significance in building energy conservation is becoming increasingly important. Building energy efficiency labeling is a crucial component of these systems, and its core content is how to fairly assess building energy efficiency. Currently, the most widely used method in building energy efficiency systems is to derive a score based on historical values ​​as the basis for energy consumption indicators. For example... Figure 1 As shown, this assessment includes the following steps: obtaining historical building energy consumption values ​​E′ and current building energy consumption values ​​E; calculating a score using the formula S = E / E′; and assessing building energy consumption based on the score S. A value of S less than 1 indicates energy saving, while a value greater than 1 indicates high energy consumption. This method only assesses current building energy consumption based on historical building energy consumption values. This approach is only suitable for building energy consumption assessments when building area A, pedestrian flow B, and planning indicators C are fixed. Because parameters A, B, and C may change significantly in the short term, the assessed score S may have a large deviation, leading to inaccurate and unreasonable building energy consumption assessments.

[0003] Therefore, there is a need to provide a building energy conservation technology assessment system and method to improve the accuracy of building energy conservation technology assessment. Summary of the Invention

[0004] This specification provides an embodiment of a method for evaluating building energy-saving technologies, comprising: acquiring relevant data of multiple sample buildings, wherein the relevant data of the sample buildings includes at least building characteristic data, environmental data, and energy consumption data of the sample buildings in various time periods; clustering the multiple sample buildings based on the relevant data of the multiple sample buildings to generate multiple building clusters; establishing an evaluation system and multiple evaluation benchmarks corresponding to the building clusters based on the relevant data of the multiple sample buildings included in the building clusters, wherein one evaluation benchmark corresponds to one time period; acquiring relevant data of a target building, wherein the target building applies the building energy-saving technology to be evaluated, and the relevant data of the target building includes at least building characteristic data, environmental data, and energy consumption data of the target building in a target time period; determining a target building cluster from the multiple building clusters based on the relevant data of the target building; determining a target evaluation benchmark from the multiple evaluation benchmarks corresponding to the target building cluster based on the target time period; and evaluating the energy-saving effect of the building energy-saving technology to be evaluated based on the relevant data of the target building, the evaluation system corresponding to the target building cluster, and the target evaluation benchmark.

[0005] Furthermore, the building characteristic data includes at least building type, heated building area, energy source information, cooled building area, and pedestrian flow data; the environmental data includes at least temperature data, humidity data, light data, terrain data, and wind data; and the energy consumption data includes at least electricity consumption data, gas consumption data, oil consumption data, coal consumption data, thermal energy consumption data, and water resource consumption data.

[0006] Furthermore, the step of clustering the multiple sample buildings based on their relevant data to generate multiple building clusters includes: for any two sample buildings, calculating a first building feature similarity based on their building feature data; calculating a first environmental similarity based on their environmental data; calculating a first building similarity based on their first building feature similarity and their first environmental similarity; and clustering the multiple sample buildings based on their building similarity to generate multiple building clusters.

[0007] Furthermore, the step of establishing an evaluation system corresponding to the building cluster based on relevant data of multiple sample buildings included in the building cluster includes: establishing multiple candidate feature factors; establishing multiple candidate evaluation indicators corresponding to the multiple candidate feature factors; performing a first screening on the multiple candidate feature factors based on the building feature data of the multiple sample buildings included in the building cluster in various time periods to obtain multiple candidate feature factors after the first screening; for each candidate feature factor after the first screening, determining the energy consumption correlation degree of the candidate feature factor after the first screening based on the energy consumption data of the multiple sample buildings included in the building cluster in various time periods; performing a second screening on the multiple candidate feature factors after the first screening based on the energy consumption correlation degree of each candidate feature factor after the first screening to obtain multiple target feature factors; determining multiple target evaluation indicators from the multiple candidate evaluation indicators based on the multiple target feature factors; and establishing an evaluation system corresponding to the building cluster based on the multiple target evaluation indicators.

[0008] Furthermore, the step of establishing an evaluation system corresponding to the building cluster based on the multiple target evaluation indicators includes: using the analytic hierarchy process (AHP) to determine the weights of the multiple target evaluation indicators; and establishing an evaluation system corresponding to the building cluster based on the multiple target evaluation indicators and their weights.

[0009] Furthermore, based on the relevant data of the multiple sample buildings included in the building cluster, multiple evaluation benchmarks corresponding to the building cluster are established, including: for each time period, based on the evaluation system corresponding to the building cluster and the building characteristic data and energy consumption data of each sample building included in the building cluster in the time period, calculating the energy-saving performance score of each sample building included in the building cluster in the time period; and based on the energy-saving performance score of each sample building included in the building cluster in the time period, calculating the evaluation benchmark of the building cluster corresponding to the time period.

[0010] Furthermore, the acquisition of relevant data of the target building includes: preprocessing the initial relevant data of the target building to generate relevant data of the target building, wherein the preprocessing includes at least abnormal data removal and data completion.

[0011] Furthermore, determining the target building cluster from the plurality of building clusters based on the relevant data of the target building includes: for each building cluster, calculating a second building feature similarity between the building cluster and the target building based on the building feature data of the cluster center of the building cluster and the building feature data of the target building; calculating a second environmental similarity between the building cluster and the target building based on the environmental data of the cluster center of the building cluster and the environmental data of the target building; calculating a second building similarity between the building cluster and the target building based on the second building feature similarity and the second environmental similarity; and determining the target building cluster from the plurality of building clusters based on the second building similarity between each building cluster and the target building.

[0012] Furthermore, the step of evaluating the energy-saving effect of the energy-saving technology of the building to be evaluated based on the relevant data of the target building, the evaluation system corresponding to the target building cluster, and the target evaluation benchmark includes: calculating the energy-saving performance score of the target building in the target time period based on the evaluation system corresponding to the target building cluster and the building characteristic data and energy consumption data of the target building in the target time period; and evaluating the energy-saving effect of the energy-saving technology of the building to be evaluated based on the energy-saving performance score of the target building in the target time period and the target evaluation benchmark.

[0013] This specification provides an embodiment of a building energy conservation technology assessment system, comprising: a sample acquisition module for acquiring relevant data of multiple sample buildings, wherein the relevant data of the sample buildings includes at least building characteristic data, environmental data, and energy consumption data of the sample buildings in various time periods; a sample clustering module for clustering the multiple sample buildings based on the relevant data of the multiple sample buildings to generate multiple building clusters; a standard establishment module for establishing an assessment system and multiple assessment benchmarks corresponding to the building clusters based on the relevant data of the multiple sample buildings included in the building clusters, wherein one assessment benchmark corresponds to one time period; and a data acquisition module for acquiring... The data includes relevant information about the target building, wherein the target building applies energy-saving technologies to be evaluated, and the relevant data includes at least the building characteristic data, environmental data, and energy consumption data of the target building during a target time period; an energy-saving assessment module is used to determine a target building cluster from multiple building clusters based on the relevant data of the target building; the energy-saving assessment module is also used to determine a target assessment benchmark from multiple assessment benchmarks corresponding to the target building cluster based on the target time period; the energy-saving assessment module is also used to evaluate the energy-saving effect of the energy-saving technologies to be evaluated based on the relevant data of the target building, the assessment system corresponding to the target building cluster, and the target assessment benchmark.

[0014] Compared to existing technologies, the building energy efficiency assessment system and method provided in this specification have at least the following advantages:

[0015] 1. By acquiring relevant data from multiple sample buildings, a large amount of effective data support is provided for subsequent building energy conservation technology assessments. Furthermore, based on the relevant data of multiple sample buildings included in a building cluster, an assessment system and multiple assessment benchmarks corresponding to the building cluster are established, providing assessment methods and standards for subsequent building energy conservation technology assessments. Based on the relevant data of the target building, the assessment system corresponding to the target building cluster, and the target assessment benchmarks, the energy-saving effect of the building energy conservation technology to be assessed is evaluated. It is possible to determine a more suitable assessment system and assessment benchmark based on the characteristic data of the target building, thereby improving the accuracy of building energy conservation technology assessments.

[0016] 2. By acquiring building characteristic data, environmental data, and energy consumption data, a more accurate and comprehensive assessment of building energy-saving technologies can be achieved from multi-dimensional data.

[0017] 3. For each building cluster, multiple candidate feature factors and multiple candidate evaluation indicators are screened to establish a more suitable evaluation system and evaluation benchmark for the building cluster. Furthermore, the evaluation benchmark is screened from the time dimension, which improves the accuracy of building energy-saving technology evaluation. Attached Figure Description

[0018] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0019] Figure 1 This is a block diagram of a building energy-saving technology assessment system shown in one embodiment of this application;

[0020] Figure 2 This is a flowchart illustrating a building energy efficiency technology assessment method in one embodiment of this application;

[0021] Figure 3 This is a flowchart illustrating the generation of multiple building clusters in one embodiment of this application;

[0022] Figure 4 This is a flowchart illustrating the evaluation system for establishing building clusters in one embodiment of this application;

[0023] Figure 5 This is a flowchart illustrating the determination of target building clusters in one embodiment of this application. Detailed Implementation

[0024] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0025] Figure 1 This is a block diagram of a building energy efficiency technology assessment system shown in one embodiment of this application, such as... Figure 1 As shown, a building energy-saving technology assessment system may include a sample acquisition module, a sample clustering module, a standard establishment module, a data acquisition module, and an energy-saving assessment module.

[0026] The sample acquisition module can be used to acquire relevant data of multiple sample buildings. The relevant data of the sample buildings includes at least the building characteristic data, environmental data, and energy consumption data of the sample buildings in various time periods.

[0027] The sample clustering module can be used to cluster multiple sample buildings based on relevant data of multiple sample buildings, generating multiple building clusters.

[0028] The standard establishment module can be used to establish an evaluation system and multiple evaluation benchmarks for building clusters based on relevant data of multiple sample buildings included in the building cluster. Each evaluation benchmark corresponds to a time period.

[0029] The data acquisition module can be used to acquire relevant data of the target building. The target building applies energy-saving technologies to be evaluated. The relevant data of the target building includes at least the building characteristic data, environmental data, and energy consumption data of the target building during the target time period.

[0030] The energy-saving assessment module can be used to determine the target building cluster from multiple building clusters based on relevant data of the target building. It can also determine the target assessment benchmark from multiple assessment benchmarks corresponding to the target building cluster based on a target time period. Furthermore, the module can evaluate the energy-saving effect of energy-saving technologies in the building under evaluation based on relevant data of the target building, the assessment system corresponding to the target building cluster, and the target assessment benchmark.

[0031] For more detailed descriptions of the sample acquisition module, sample clustering module, standard establishment module, data acquisition module, and energy-saving assessment module, please refer to [link to relevant documentation]. Figures 2-5 The details and related descriptions will not be repeated here.

[0032] Figure 2 This is a flowchart illustrating a building energy conservation technology assessment method in one embodiment of this application. Further, a building energy conservation technology assessment method can be executed by a building energy conservation technology assessment system. Figure 2 As shown, a method for assessing building energy efficiency technologies may include the following process.

[0033] Step 210: Obtain relevant data for multiple sample buildings.

[0034] The relevant data for the sample buildings includes at least the building characteristics, environmental data, and energy consumption data for each time period. Each time period can represent a quarter, a month, two months, etc.

[0035] Furthermore, building characteristic data should include at least the building type (e.g., school, office building, shopping mall, hospital, etc.), heated building area, energy source information, cooled building area, and pedestrian traffic data. The energy source can be the source of electricity, such as the power grid or renewable energy generation equipment.

[0036] Furthermore, the environmental data should include at least temperature data, humidity data, light data, topographic data, and wind data.

[0037] Furthermore, energy consumption data includes at least electricity consumption data, gas consumption data, oil consumption data, coal consumption data, thermal energy consumption data, and water resource consumption data.

[0038] Step 220: Based on the relevant data of multiple sample buildings, cluster the multiple sample buildings to generate multiple building clusters.

[0039] Figure 3 This is a flowchart illustrating the generation of multiple building clusters in one embodiment of this application, such as... Figure 3 As shown, further, based on the relevant data of multiple sample buildings, multiple sample buildings are clustered to generate multiple building clusters, including:

[0040] For any two sample buildings

[0041] Based on the architectural feature data of two sample buildings, calculate the first architectural feature similarity between the two sample buildings;

[0042] Based on the environmental data of the two sample buildings, calculate the first environmental similarity between the two sample buildings;

[0043] The first building similarity between two sample buildings is calculated based on the first building feature similarity and the first environment similarity between the two sample buildings.

[0044] Based on the architectural similarity between any two sample buildings, multiple sample buildings are clustered to generate multiple building clusters.

[0045] Specifically, the similarity of building types between two sample buildings can be calculated. When the building types are identical, the similarity is 1; when they are different, the similarity is 0. The similarity of heated building areas can also be calculated. Similarly, the similarity of energy sources can be calculated. When the energy sources are identical, the similarity is 1; when they are different, the similarity is 0. The similarity of cooled building areas can also be calculated. Finally, the similarity of pedestrian traffic can be calculated based on pedestrian traffic data for different time periods.

[0046] As an example only, the similarity of the first architectural features of two sample buildings can be calculated based on the following formula:

[0047] S (1，(i，j)) =a 11 *S type +a 12 *S heating +a 13 *S source +a 14 *S cooling +a 15 *S traffic

[0048] Among them, S (1，(i，j)) S represents the first architectural feature similarity between the i-th and j-th sample buildings. type S represents the building type similarity between the i-th and j-th sample buildings. heating S represents the similarity of the heating building area between the i-th and j-th sample buildings. source S represents the energy source similarity between the i-th and j-th sample buildings. sooling S represents the similarity of the cooling building area between the i-th and j-th sample buildings. traffic For the similarity of pedestrian flow between the i-th and j-th sample buildings, a 11 a 12 a 13 a 14 and a 15 All are preset weights.

[0049] The similarity of temperature between two sample buildings can be calculated based on temperature data, humidity data, illumination data, terrain data, and wind data over various time periods.

[0050] As an example only, the similarity of the first environment of two sample buildings can be calculated based on the following formula:

[0051] S (2，(i，j)) =a 21 *S temperature +a 22 *S humidity +a 23 *S illumination +a 24 *S terrain +a 25 *S wind

[0052] Among them, S (2，(i，j)) S represents the first environmental similarity between the i-th and j-th sample buildings. temperature S represents the temperature similarity between the i-th and j-th sample buildings. humidity S represents the humidity similarity between the i-th and j-th sample buildings. illumination S represents the energy source similarity between the i-th and j-th sample buildings. terrain S represents the terrain similarity between the i-th and j-th sample buildings. windFor the wind force similarity between the i-th and j-th sample buildings, a 21 a 22 a 23 a 24 and a 25 All are preset weights.

[0053] The first architectural similarity between two sample buildings can be calculated based on the first architectural feature similarity and the first environmental similarity between them, according to the following formula:

[0054] S (i，j) =a 31 *S (1，(i，j)) +a 32 *S (2，(i，j))

[0055] Among them, S (i，j) Let a be the first building similarity between the i-th and j-th sample buildings. 31 and a 32 All are preset weights.

[0056] Furthermore, clustering algorithms can be used to cluster multiple sample buildings based on the architectural similarity between any two sample buildings, generating multiple building clusters. Specifically, if the architectural similarity between two sample buildings is greater than a preset similarity threshold, then the two sample buildings can be clustered into the same building cluster.

[0057] Step 230: Based on the relevant data of multiple sample buildings included in the building cluster, establish an evaluation system and multiple evaluation benchmarks corresponding to the building cluster.

[0058] Each evaluation benchmark corresponds to a time period.

[0059] Figure 4 This is a flowchart illustrating the evaluation system for establishing building clusters in one embodiment of this application, as shown below. Figure 4 As shown, further, based on the relevant data of multiple sample buildings included in the building cluster, an evaluation system corresponding to the building cluster is established, including:

[0060] Establish multiple candidate feature factors. For example, multiple candidate feature factors should include at least the building area, heating building area, cooling building area, energy user information, building function information, parameter information of new energy power generation equipment, building internal structure and building materials, etc.

[0061] Establish multiple candidate evaluation indicators corresponding to multiple candidate feature factors, such as thermal insulation performance indicators, energy consumption per unit area for heating, energy consumption per unit area for cooling, electricity consumption indicators, water consumption indicators, gas consumption indicators, and oil consumption indicators.

[0062] Based on the building feature data of multiple sample buildings included in the building cluster at various time periods, multiple candidate feature factors are first screened to obtain multiple candidate feature factors after the first screening. For example, if none of the sample buildings included in the building cluster contain new energy power generation equipment, then the parameter information of new energy power generation equipment can be screened out during the first screening.

[0063] For each candidate feature factor after the first screening, the energy consumption correlation degree of the candidate feature factor after the first screening is determined based on the energy consumption data of multiple sample buildings included in the building cluster in various time periods.

[0064] Based on the energy consumption correlation of each candidate feature factor after the first screening, a second screening is performed on multiple candidate feature factors after the first screening to obtain multiple target feature factors.

[0065] Multiple target evaluation indicators are determined from multiple candidate evaluation indicators based on multiple target feature factors;

[0066] An evaluation system for building clusters is established based on multiple target evaluation indicators.

[0067] Furthermore, for each candidate feature factor, a correlation coefficient can be calculated between the energy consumption data of multiple sample buildings included in the building cluster and the energy consumption data of multiple sample buildings in various time periods. This correlation coefficient can characterize the energy consumption correlation degree of the candidate feature factor. Candidate feature factors that have undergone the first screening and whose energy consumption correlation degree is greater than a preset energy consumption correlation degree threshold can be used as target feature factors.

[0068] Furthermore, based on multiple target evaluation indicators, an evaluation system corresponding to the building clusters is established, including:

[0069] The weights of multiple target evaluation indicators are determined using the analytic hierarchy process (AHP).

[0070] An evaluation system for building clusters is established based on multiple target evaluation indicators and their weights.

[0071] Specifically, the weights of multiple target evaluation indicators can be determined using the analytic hierarchy process (AHP) as follows:

[0072] Establish a hierarchical structure model based on the analytic hierarchy process (AHP);

[0073] Construct the judgment matrix;

[0074] Calculate eigenvalues ​​and eigenvectors;

[0075] Consistency test analysis;

[0076] The overall hierarchy is ranked to determine the weight values ​​of each target evaluation indicator.

[0077] Furthermore, based on the relevant data of multiple sample buildings included in the building clusters, multiple evaluation benchmarks corresponding to the building clusters are established, including:

[0078] For each time period

[0079] Based on the evaluation system corresponding to the building cluster and the building characteristic data and energy consumption data of each sample building included in the building cluster during the time period, the energy-saving performance score of each sample building included in the building cluster during the time period is calculated. Specifically, for each sample building included in the building cluster, the scores of multiple target evaluation indicators corresponding to the sample building in the building cluster can be calculated based on the building characteristic data and energy consumption data of the sample building during the time period. The scores of the sample building in the multiple target evaluation indicators corresponding to the building cluster are weighted and summed to obtain the energy-saving performance score of the sample building during the time period.

[0080] Based on the energy-saving performance score of each sample building included in the building cluster over a time period, the evaluation benchmark for the corresponding time period of the building cluster is calculated.

[0081] For example, the evaluation benchmark for the corresponding time period of building clusters can be calculated based on the following formula:

[0082]

[0083] Among them, S mean value The evaluation criterion for the corresponding time period of the building cluster is F, where F is the total number of sample buildings included in the building cluster, and S is the total number of sample buildings included in the building cluster. f The energy efficiency score of the f-th sample building included in the building cluster during the time period.

[0084] Understandably, the higher the energy efficiency score, the better the energy efficiency of the sample building.

[0085] Step 240: Obtain relevant data for the target building.

[0086] Among them, the target building applies building energy-saving technologies that need to be evaluated, and the relevant data of the target building shall include at least the building characteristic data, environmental data and energy consumption data of the target building during the target time period.

[0087] Furthermore, step 240 specifically includes: preprocessing the initial relevant data of the target building to generate relevant data of the target building, wherein the preprocessing includes at least the removal of abnormal data and data completion.

[0088] Furthermore, the initial relevant data of the target building can be preprocessed based on the data processing model to generate relevant data for the target building. The data processing model can be a machine learning model such as an Artificial Neural Network (ANN), a Recurrent Neural Network (RNN), a Long Short-Term Memory (LSTM), or a Bidirectional Recurrent Neural Network (BRNN).

[0089] Step 250: Based on the relevant data of the target building, determine the target building cluster from multiple building clusters.

[0090] Figure 5 This is a flowchart illustrating the determination of target building clusters in one embodiment of this application, such as... Figure 5 As shown, further, based on relevant data of the target building, the target building cluster is determined from multiple building clusters, including:

[0091] For each building cluster

[0092] Based on the building feature data of the cluster centers of building clusters and the building feature data of the target building, the second building feature similarity between the building clusters and the target building is calculated.

[0093] Based on the environmental data of the cluster centers of building clusters and the environmental data of the target building, the second environmental similarity between the building clusters and the target building is calculated.

[0094] Based on the second building feature similarity and the second environment similarity, the second building similarity between the building cluster and the target building is calculated;

[0095] The target building cluster is determined from multiple building clusters based on the second building similarity between each building cluster and the target building.

[0096] For example, the building cluster with the highest similarity to the second building can be used as the target building cluster.

[0097] Step 260: Based on the target time period, determine the target evaluation benchmark from multiple evaluation benchmarks corresponding to the target building cluster.

[0098] For example, if the target time period is the second quarter of the year, and the second evaluation benchmark corresponding to the target building cluster is also the second quarter of the year, then the second evaluation benchmark can be used as the target evaluation benchmark.

[0099] Step 270: Based on the relevant data of the target building, the evaluation system corresponding to the target building cluster, and the target evaluation benchmark, evaluate the energy-saving effect of the energy-saving technology of the building to be evaluated.

[0100] Furthermore, step 270 specifically includes:

[0101] Based on the evaluation system corresponding to the target building cluster and the building characteristic data and energy consumption data of the target building in the target time period, calculate the energy-saving performance score of the target building in the target time period.

[0102] Based on the energy-saving performance score of the target building during the target time period and the target evaluation benchmark, the energy-saving effect of the energy-saving technology of the building to be evaluated is assessed.

[0103] For example, the energy-saving effect of the building's energy-saving technology can be evaluated based on the target building's energy-saving performance score over a target time period and the target evaluation benchmark, using the following formula:

[0104]

[0105] Among them, P current To evaluate the energy-saving effects of building energy-saving technologies, S current S represents the energy efficiency score of the target building over the target time period. (target,mean value) Used as the benchmark for target evaluation.

[0106] Understandable, when P current When the value is greater than 1, the energy-saving effect of the building energy-saving technology to be evaluated is better than the energy-saving effect of the building energy-saving technology currently used in similar buildings.

[0107] Furthermore, based on the energy-saving effect of the energy-saving technology in the building to be evaluated, the energy-saving technology can be classified into energy-saving levels. 1 < S < 1.2 is defined as a one-star energy-saving state, 1.2 ≤ S < 1.5 is defined as a two-star energy-saving state, 1.5 ≤ S < 2 is defined as a three-star energy-saving state, 2 ≤ S < 3 is defined as a four-star energy-saving state, and S ≥ 3 is defined as a five-star energy-saving state.

[0108] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A method for evaluating building energy-saving technologies, characterized in that, include: Acquire relevant data from multiple sample buildings, wherein the relevant data of the sample buildings includes at least the building characteristic data, environmental data, and energy consumption data of the sample buildings in various time periods; Based on the relevant data of the multiple sample buildings, the multiple sample buildings are clustered to generate multiple building clusters; Based on the relevant data of multiple sample buildings included in the building cluster, an evaluation system and multiple evaluation benchmarks are established for the building cluster, wherein each evaluation benchmark corresponds to a time period. Acquire relevant data of the target building, wherein the target building applies building energy-saving technologies to be evaluated, and the relevant data of the target building includes at least the building characteristic data, environmental data, and energy consumption data of the target building during the target time period; Based on the relevant data of the target building, the target building cluster is determined from the multiple building clusters; Based on the target time period, a target evaluation benchmark is determined from multiple evaluation benchmarks corresponding to the target building cluster; Based on the relevant data of the target building, the evaluation system corresponding to the target building cluster, and the target evaluation benchmark, the energy-saving effect of the energy-saving technology of the building to be evaluated is evaluated. The step of establishing an evaluation system corresponding to the building cluster based on relevant data of multiple sample buildings included in the building cluster includes: Establish multiple candidate feature factors; Establish multiple candidate evaluation indicators corresponding to the multiple candidate feature factors; Based on the building feature data of multiple sample buildings included in the building cluster in various time periods, the multiple candidate feature factors are first screened to obtain multiple candidate feature factors after the first screening. For each candidate feature factor after the first screening, the energy consumption correlation degree of the candidate feature factor after the first screening is determined based on the energy consumption data of multiple sample buildings included in the building cluster in various time periods. Based on the energy consumption correlation of each candidate feature factor after the first screening, a second screening is performed on the multiple candidate feature factors after the first screening to obtain multiple target feature factors. Based on the multiple target feature factors, multiple target evaluation indicators are determined from the multiple candidate evaluation indicators; Based on the aforementioned multiple target evaluation indicators, an evaluation system corresponding to the building clusters is established; Based on the relevant data of multiple sample buildings included in the building cluster, multiple evaluation benchmarks corresponding to the building cluster are established, including: For each time period, based on the evaluation system corresponding to the building cluster and the building characteristic data and energy consumption data of each sample building included in the building cluster during the time period, the energy-saving performance score of each sample building included in the building cluster during the time period is calculated. For each sample building included in the building cluster, based on the building characteristic data and energy consumption data of the sample building during the time period, the score of the sample building corresponding to the building cluster is calculated. The scores of the sample building corresponding to the building cluster are weighted and summed to obtain the energy-saving performance score of the sample building during the time period. Based on the energy-saving performance score of each sample building included in the building cluster during the time period, the evaluation benchmark of the building cluster corresponding to the time period is calculated; The step of determining the target building cluster from the plurality of building clusters based on the relevant data of the target building includes: for each building cluster, calculating a second building feature similarity between the building cluster and the target building based on the building feature data of the cluster center of the building cluster and the building feature data of the target building; Based on the environmental data of the cluster centers of the building clusters and the environmental data of the target building, a second environmental similarity between the building clusters and the target building is calculated. Based on the second building feature similarity and the second environment similarity, calculate the second building similarity between the building cluster and the target building; The target building cluster is determined from the plurality of building clusters based on the second building similarity between each building cluster and the target building.

2. The method for evaluating building energy-saving technologies according to claim 1, characterized in that, The building characteristic data includes at least the building type, heated building area, energy source information, cooled building area, and pedestrian flow data. The environmental data mentioned above includes at least temperature data, humidity data, light data, terrain data, and wind data; The energy consumption data includes at least electricity consumption data, gas consumption data, oil consumption data, coal consumption data, thermal energy consumption data, and water resource consumption data.

3. A method for evaluating building energy-saving technologies according to claim 1 or 2, characterized in that, Based on the relevant data of the multiple sample buildings, the multiple sample buildings are clustered to generate multiple building clusters, including: For any two of the sample buildings Based on the architectural feature data of the two sample buildings, calculate the first architectural feature similarity between the two sample buildings; Based on the environmental data of the two sample buildings, the first environmental similarity of the two sample buildings is calculated; The first architectural similarity between the two sample buildings is calculated based on the first architectural feature similarity and the first environmental similarity between them. Based on the first building similarity between any two sample buildings, the multiple sample buildings are clustered to generate multiple building clusters.

4. The building energy-saving technology assessment method according to claim 3, characterized in that, The establishment of an evaluation system corresponding to the building cluster based on the multiple target evaluation indicators includes: The weights of the multiple target evaluation indicators are determined using the analytic hierarchy process (AHP). An evaluation system corresponding to the building cluster is established based on the multiple target evaluation indicators and their weights.

5. A method for evaluating building energy-saving technologies according to claim 1 or 2, characterized in that, The acquisition of relevant data for the target building includes: The initial relevant data of the target building is preprocessed to generate relevant data of the target building, wherein the preprocessing includes at least abnormal data removal and data completion.

6. The method for evaluating building energy-saving technologies according to claim 5, characterized in that, The evaluation of the energy-saving effect of the energy-saving technology of the building to be evaluated, based on relevant data of the target building, the evaluation system corresponding to the cluster of the target building, and the target evaluation benchmark, includes: Based on the evaluation system corresponding to the target building cluster and the building characteristic data and energy consumption data of the target building in the target time period, the energy-saving performance score of the target building in the target time period is calculated. Specifically, for each sample building included in the building cluster, based on the building characteristic data and energy consumption data of the sample building in the time period, the scores of multiple target evaluation indicators corresponding to the sample building in the building cluster are calculated. The scores of the sample building in the multiple target evaluation indicators corresponding to the building cluster are weighted and summed to obtain the energy-saving performance score of the sample building in the time period. Based on the energy-saving performance score of the target building during the target time period and the target evaluation benchmark, the energy-saving effect of the energy-saving technology of the building to be evaluated is evaluated.

7. A system for implementing the building energy efficiency technology assessment method as described in any one of claims 1-6, characterized in that, include: The sample acquisition module is used to acquire relevant data of multiple sample buildings, wherein the relevant data of the sample buildings includes at least the building characteristic data, environmental data and energy consumption data of the sample buildings in various time periods; The sample clustering module is used to cluster the multiple sample buildings based on the relevant data of the multiple sample buildings, and generate multiple building clusters; The standard establishment module is used to establish an evaluation system and multiple evaluation benchmarks corresponding to the building cluster based on the relevant data of multiple sample buildings included in the building cluster, wherein one evaluation benchmark corresponds to a time period; The data acquisition module is used to acquire relevant data of the target building, wherein the target building applies building energy-saving technologies to be evaluated, and the relevant data of the target building includes at least the building characteristic data, environmental data, and energy consumption data of the target building during the target time period. An energy-saving assessment module is used to determine the target building cluster from the multiple building clusters based on relevant data of the target building; The energy-saving assessment module is also used to determine a target assessment benchmark from multiple assessment benchmarks corresponding to the target building cluster based on the target time period. The energy-saving assessment module is also used to assess the energy-saving effect of the energy-saving technology of the building to be assessed based on the relevant data of the target building, the assessment system corresponding to the cluster of the target building, and the target assessment benchmark.

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