Enterprise energy efficiency evaluation and promotion model construction method based on electric power big data

Abstract

The invention discloses an enterprise energy efficiency evaluation and promotion model construction method based on electric power big data, and belongs to the field of data analysis. Comprising the steps of data collection; preprocessing the data; extracting characteristic indexes; constructing a model; evaluating and optimizing the model; and model interpretation application. Based on multi-dimensional electric power big data, an energy efficiency evaluation and improvement model is constructed, energy efficiency analysis and evaluation of key energy consumption enterprises are realized, the value of the electric power big data is fully mined, the enterprises are helped to understand own energy use efficiency, the energy efficiency improvement potential of the enterprises is mined, the enterprises are supported to formulate energy use strategies and energy saving measures, and the energy cost is reduced. And energy conservation, emission reduction and dual-carbon targets can be achieved. The method can be widely applied to the fields of power supply and consumption operation management and key energy consumption enterprise energy efficiency analysis and evaluation.

Description

Technical Field

[0001] The present invention belongs to the field of data analysis, and in particular relates to a method for constructing an enterprise energy efficiency evaluation and improvement model based on power big data for enterprise energy efficiency evaluation and energy conservation and emission reduction applications. Background Art

[0002] Electric power big data includes multi-dimensional data such as electricity volume, electricity price, electricity charges, and business expansion. The accumulation and analysis of electric power big data is of great significance in serving economic and social operations, serving enterprises and users, etc.

[0003] Key energy-consuming enterprises refer to enterprises with large energy consumption and a greater impact on energy efficiency, such as industrial enterprises, commercial buildings, public institutions, etc.

[0004] By conducting energy efficiency analysis of key energy-consuming enterprises through power big data and building energy efficiency assessment and improvement models, we can give full play to the added value of power big data, help enterprises and users identify high-energy-consuming links, improve energy utilization efficiency, reduce energy costs, and assist relevant departments in implementing energy-saving and emission reduction policies and achieving dual carbon goals.

[0005] At present, the energy efficiency analysis of enterprises mainly builds energy efficiency analysis models from the dimensions of multi-source energy consumption, kilowatt-hour output value, peak and valley energy consumption, etc., lacking analysis of electricity prices, electricity charges, loads, and carbon emissions. Users are still unclear about what parts of electricity charges are composed of, what optimization space there is, and the implementation of electricity prices. It is even more impossible to support high-energy-consuming enterprises in formulating and evaluating the effects of measures such as energy conservation and emission reduction and reducing energy costs. Therefore, there is an urgent need for a scientific and comprehensive method and technology that comprehensively considers the method of building energy efficiency evaluation and improvement models based on multi-dimensional power big data such as electricity volume, electricity prices, electricity charges, and loads, so as to realize energy efficiency analysis and evaluation of key energy-consuming enterprises and help enterprises save energy and reduce consumption.

[0006] The invention patent with the authorization announcement date of 2022.04.01 and the authorization announcement number of CN 113971535 B discloses "an energy carbon efficiency evaluation method and system", including collecting enterprise user electricity consumption data, performing distributed data processing on enterprise user electricity consumption data, and generating enterprise electric energy efficiency comprehensive evaluation scores based on energy carbon efficiency models, generating enterprise energy efficiency codes based on enterprise electric energy efficiency comprehensive evaluation scores, and enterprises scanning enterprise energy efficiency codes to obtain electric energy efficiency analysis and electric energy efficiency recommendations. The energy carbon efficiency evaluation method and system disclosed in this technical solution generates enterprise electric energy efficiency comprehensive evaluation scores based on energy carbon efficiency models, and then generates enterprise energy efficiency codes, conducts a comprehensive evaluation of enterprise electric energy efficiency, obtains enterprise electric energy efficiency analysis and electric energy efficiency recommendations by scanning enterprise energy efficiency codes, thereby helping enterprises achieve energy conservation and consumption reduction. Since it generates a comprehensive evaluation score for corporate electricity efficiency based on an energy carbon efficiency model, it lacks analysis of dimensions such as electricity prices, electricity charges, loads, and carbon emissions. It is of limited help in formulating and evaluating the effectiveness of measures such as energy conservation and emission reduction and reducing energy costs for high-energy-consuming enterprises.

[0007] The invention patent application with the application publication date of 2022.02.18 and the application publication number of CN 114066200A discloses "an enterprise user energy efficiency analysis system", including an enterprise user energy efficiency data collection module, an enterprise user energy efficiency data storage module, an enterprise user energy efficiency data analysis module and an enterprise user energy efficiency data display module. The enterprise user energy efficiency data collection module collects information data such as enterprise transformer capacity and operation information, time-divided electricity consumption information, electricity fee composition and strategy optimization; the enterprise user data storage module stores energy efficiency data of various types and sources in the data warehouse; the enterprise user energy efficiency data analysis module analyzes electricity consumption, electricity fee, peak and valley analysis, power adjustment analysis and other contents, and uploads the analysis result data to the enterprise user energy efficiency data display module. This technical solution fully combines power supply services with customer energy consumption scenarios, displays customer electricity consumption data in a visual way, helps enterprise customers understand their own electricity consumption and electricity consumption characteristics, provides energy efficiency analysis for enterprises, and puts forward suggestions for optimizing energy consumption diagnosis, helping customers to reduce costs and increase efficiency. Since it generates a comprehensive evaluation score for corporate electricity efficiency based on the collection of corporate user energy efficiency data, it also lacks analysis of dimensions such as electricity prices, electricity charges, loads, and carbon emissions. Its role in helping high-energy-consuming enterprises to formulate and evaluate the effectiveness of measures such as energy conservation and emission reduction and reduction of energy costs is also limited. Summary of the invention

[0008] The technical problem to be solved by the present invention is to provide a method for constructing an enterprise energy efficiency evaluation and improvement model based on power big data. It aims to fully tap the value of power big data, help enterprises understand their own energy efficiency, discover abnormal situations and potential problems in enterprise energy consumption, tap the potential for improving enterprise energy efficiency, support enterprises in formulating energy use strategies and energy-saving measures, help enterprises reduce energy costs, and help relevant departments achieve energy conservation, emission reduction and dual carbon goals.

[0009] The technical solution of the present invention is to provide a method for building an enterprise energy efficiency evaluation and improvement model based on power big data, which is characterized by comprising the following steps:

[0010] S1. Data collection;

[0011] S2, data preprocessing;

[0012] S3, feature index extraction;

[0013] S4, model construction;

[0014] S5, model evaluation and optimization;

[0015] S6, model interpretation and application;

[0016] The method for constructing an enterprise energy efficiency evaluation and improvement model based on power big data is based on an enterprise energy efficiency evaluation and improvement model that uses power data to reflect the energy consumption rate of the enterprise. It is used for energy efficiency analysis, evaluation and improvement of key energy-consuming enterprises, discovers abnormal situations and potential problems in enterprise energy consumption, and helps enterprises reduce energy costs.

[0017] Specifically, the step S1 matches the power data including power consumption, load curve, electricity price, and electricity fee of power users in the region through fields including the enterprise electricity user number and the unified social credit code, and obtains other energy consumption related data; these data are collected in real time through devices including smart meters, sensors, and monitoring systems, and can also be collected offline through historical data.

[0018] Specifically, in step S2, the collected data is cleaned and normalized, invalid or erroneous data records are deleted during the cleaning process, and all data are converted into values ​​between 0 and 1 during the normalization process to facilitate subsequent analysis and modeling;

[0019] The normalized formula is:

[0020]

[0021] Among them, x scal is the normalized value, x is the original value, and x min is the minimum value, x maxis the maximum value.

[0022] Specifically, the step S3 analyzes the enterprise's power data, uses an anomaly detection algorithm and benchmarks with the same industry to identify data points with abnormal energy consumption, and extracts valuable features based on factors such as the enterprise's load conditions and production operations.

[0023] Furthermore, the anomaly detection algorithm includes at least a box plot and a Z-Score method;

[0024] The valuable features include at least the changing trend of electricity consumption and the peak-to-valley difference of the load curve.

[0025] Specifically, in step S4, an enterprise energy efficiency evaluation and improvement model is established based on the energy consumption data of key energy-consuming enterprises.

[0026] Furthermore, the enterprise energy efficiency evaluation and improvement model is divided into three parts: enterprise energy efficiency analysis, enterprise energy saving evaluation, and enterprise energy efficiency improvement;

[0027] The enterprise energy efficiency analysis part is to analyze the enterprise's energy efficiency situation by carrying out average electricity price analysis, billing method analysis, average load rate analysis, peak electricity consumption ratio analysis, carbon emission analysis, and load overload analysis based on the enterprise's daily electricity consumption, active power, peak, flat and valley electricity consumption distribution indicators, and support the enterprise's energy efficiency potential assessment and the formulation of enterprise energy efficiency improvement strategies;

[0028] The energy conservation assessment part of the enterprise is based on the classification of basic attributes of the enterprise, and analyzes the electricity consumption behavior of the enterprise and the industry benchmarking. The energy conservation potential and energy conservation level of the enterprise are evaluated through the decision tree model. Through the energy consumption analysis of different dimensions, the energy consumption of the enterprise is mastered, and the energy conservation potential enterprises are discovered to form a list of energy conservation potential enterprises, and an energy conservation electricity consumption recommendation report is provided for energy conservation potential.

[0029] The above energy consumption analysis shall at least include total energy consumption, energy consumption structure, energy consumption trend, and comparative analysis of energy consumption in different industries;

[0030] The enterprise energy efficiency improvement part, after conducting enterprise energy efficiency analysis and enterprise energy-saving potential assessment, provides energy consumption optimization suggestions and designs energy efficiency improvement application scenarios; helps enterprises to fully understand their own energy consumption situation and characteristics, find the potential for energy consumption optimization, and formulate corresponding improvement measures, so as to improve energy efficiency levels, reduce energy consumption costs, promote industrial upgrading and transformation, and optimize energy policies and management.

[0031] Specifically, the step S5 optimizes and adjusts the accuracy and stability of the model through the verification of historical energy consumption data in the enterprise energy efficiency evaluation and improvement model, predicts the energy consumption and energy cost of key energy-consuming enterprises, and supports enterprises in formulating energy management plans based on production conditions.

[0032] Specifically, the step S6 explains and analyzes the energy efficiency of the power system according to the results of the model, and proposes corresponding optimization suggestions and measures;

[0033] The suggestions include at least load regulation, power consumption optimization, and electricity price strategy adjustment to improve energy efficiency and reduce electricity costs.

[0034] The method for constructing an enterprise energy efficiency evaluation and improvement model based on electric power big data described in the present invention is an enterprise energy efficiency evaluation and improvement model based on electric power data that reflects the energy consumption rate of the enterprise. It fully taps the value of electric power big data and is used for energy efficiency analysis, evaluation and improvement of key energy-consuming enterprises. It discovers abnormal situations and potential problems in enterprise energy consumption, taps the potential for improving enterprise energy efficiency, supports enterprises in formulating energy use strategies and energy-saving measures, and helps enterprises reduce energy costs.

[0035] Compared with the prior art, the advantages of the present invention are:

[0036] 1. The technical solution of the present invention can be applied to improve the energy utilization efficiency of enterprises. By analyzing the big data of electricity, it is possible to gain an in-depth understanding of the energy usage of enterprises, discover abnormal situations and potential problems in energy consumption, and take timely measures to adjust and optimize, thereby reducing energy costs.

[0037] 2. The application of the technical solution of the present invention supports energy conservation, emission reduction and dual carbon goals. The energy efficiency analysis and improvement model can provide data support to help relevant departments monitor and manage energy consumption and promote energy conservation and emission reduction.

[0038] 3. The technical solution of the present invention comprehensively considers the method of constructing an energy efficiency evaluation and improvement model based on multi-dimensional power big data such as electricity volume, electricity price, electricity fee, and load, to realize energy efficiency analysis and evaluation of key energy-consuming enterprises, and help enterprises save energy and reduce consumption; it aims to fully tap the value of power big data, help enterprises understand their own energy utilization efficiency, discover abnormal situations and potential problems in enterprise energy consumption, tap the potential for improving enterprise energy efficiency, support enterprises in formulating energy use strategies and energy-saving measures, help enterprises reduce energy costs, and help relevant departments achieve energy conservation, emission reduction and dual carbon goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the process steps of the present invention;

[0040] Figure 2 It is a schematic diagram of the enterprise energy efficiency evaluation and improvement model of the present invention. DETAILED DESCRIPTION

[0041] The present invention will be further described below in conjunction with the accompanying drawings.

[0042] At present, the energy efficiency evaluation and analysis of enterprises mainly evaluates the energy utilization efficiency of enterprises through enterprise consumption indicators, such as energy consumption per unit of output value and energy consumption per unit of product, but the ability to evaluate economic benefits is relatively weak. By building an enterprise energy efficiency evaluation and improvement model that considers electricity data such as electricity volume, electricity price, electricity fee, and load, we can comprehensively evaluate the energy utilization efficiency and economic benefits of enterprises.

[0043] The technical solution of the present invention provides a method for constructing an energy efficiency evaluation and improvement model based on multi-dimensional power big data. It adopts an enterprise energy efficiency evaluation and improvement model based on power data to reflect the enterprise's energy consumption rate, fully taps the value of power big data, and is used for energy efficiency analysis, evaluation and improvement of key energy-consuming enterprises. It helps enterprises understand their own energy utilization efficiency, discover abnormal situations and potential problems in enterprise energy consumption, tap the potential for improving enterprise energy efficiency, support enterprises in formulating energy utilization strategies and energy-saving measures, help enterprises reduce energy costs, and assist relevant departments in achieving energy conservation, emission reduction and dual carbon goals.

[0044] like Figure 1 As shown in , the specific implementation steps of the present invention are as follows:

[0045] S1 Data Collection: The model collects various data sources in the area, including energy consumption data, equipment characteristic data, etc. These data can be obtained through sensors, monitoring equipment and public databases.

[0046] S2 Data preprocessing: The model cleans and preprocesses the collected data, including data cleaning, data normalization, and missing value processing, to ensure the accuracy and consistency of the data.

[0047] S3 characteristic indicator extraction: Based on electricity data, by comparing the energy consumption of different time periods and different enterprises, we can find out the problems such as energy consumption peak and abnormal energy consumption, analyze the causes, and select appropriate energy efficiency evaluation indicators according to the business and industry characteristics of the enterprise.

[0048] S4 model construction: Build an enterprise energy efficiency evaluation and improvement model based on selected power data such as electricity consumption, electricity price, electricity fee, load, and equipment energy consumption data, calculate power data indicators for energy efficiency analysis, and compare them with historical data or industry standards to evaluate the enterprise's energy efficiency performance and improve energy efficiency improvement strategies and application scenarios.

[0049] S5 Model Evaluation and Optimization: Evaluate and optimize the constructed enterprise energy efficiency evaluation and improvement model. Use historical data to verify the model and evaluate the accuracy and applicability of the model. According to the evaluation results, adjust and optimize the model to improve the model's predictive ability and practicality.

[0050] S6 Model interpretation and application: Apply the optimized model to actual energy efficiency analysis. According to the results of the model, explain and analyze the energy efficiency of the enterprise. By inputting new electricity data, based on energy consumption analysis and energy efficiency evaluation results, discover potential energy efficiency problems and propose improvement measures.

[0051] Specific implementation method:

[0052] According to the method, the specific implementation contents are as follows:

[0053] According to S1, through the fields such as the enterprise electricity user number and the unified social credit code, the power data such as the power consumption, load curve, electricity price, and electricity fee of the power users in the region are matched, and other energy consumption related data are obtained. These data can be collected in real time through devices such as smart meters, sensors, and monitoring systems, or collected offline through historical data.

[0054] According to S2, the collected data is cleaned and normalized. In the cleaning process, invalid or erroneous data records are deleted. In the normalization process, all data are converted into numerical values ​​between 0 and 1 to facilitate subsequent analysis and modeling.

[0055]

[0056] Among them, x scal is the normalized value, x is the original value, and x min is the minimum value, x max is the maximum value.

[0057] According to S3, by analyzing the enterprise's power data, using anomaly detection algorithms (such as box plots, Z-Score, etc.) and benchmarking analysis with the same industry to identify data points with abnormal energy consumption, and combining the enterprise's load conditions, production and operation conditions and other factors to extract valuable features, such as the changing trend of electricity consumption, the peak-to-valley difference of the load curve, etc.

[0058] Among them, Z-Score is called z-score, also called standard score, which is the process of dividing the difference between a number and the mean by the standard deviation.

[0059] In statistics, a standard score is the number of standard deviations that an observation or data point is above the mean of the observed or measured values.

[0060] According to S4, an enterprise energy efficiency evaluation and improvement model is established based on the energy consumption data of key energy-consuming enterprises.

[0061] like Figure 2 As shown in , the enterprise energy efficiency evaluation and improvement model is divided into three parts: enterprise energy efficiency analysis, enterprise energy saving evaluation, and enterprise energy efficiency improvement.

[0062] (1) Enterprise energy efficiency analysis

[0063] Based on the company's daily electricity consumption, active power, peak, flat and valley electricity consumption distribution, we carry out average electricity price analysis, billing method analysis, average load rate analysis, peak electricity consumption proportion analysis, carbon emission analysis, load overlimit analysis, etc., to analyze the company's energy efficiency, support the assessment of the company's energy efficiency potential and the formulation of the company's energy efficiency improvement strategy.

[0064] (2) Enterprise Energy Saving Assessment

[0065] Based on the classification of basic attributes of enterprises, we analyze the electricity consumption behavior of enterprises and industry benchmarking, and evaluate the energy-saving potential and energy-saving level of enterprises through decision tree models. Through the dimensional energy consumption analysis such as total energy consumption, energy consumption structure, energy consumption trend, industry energy consumption comparison analysis, etc., we can grasp the energy consumption of enterprises, explore energy-saving potential enterprises, form a list of energy-saving potential enterprises, and provide energy-saving electricity recommendation reports for energy-saving potential.

[0066] ① Analysis of electricity consumption behavior, taking into account the influencing factors such as the company's electricity consumption, load, electricity charges, etc., and guiding the rationalization of electricity consumption for potential enterprises with long user age, high peak-to-valley ratio, low load rate, and overcapacity.

[0067] ②Industry benchmarking analysis: analyze the trends of the company's industry and upstream and downstream industries in terms of electricity consumption, business expansion and installation, average electricity prices, etc. This allows users to timely understand the situation of the industry and their own ranking in the industry, identify their own electricity consumption problems, and provide decision-making support for users.

[0068] ③ Energy-saving potential calculation: Based on the analysis of enterprise electricity consumption behavior and industry benchmarking, the cosine similarity algorithm is used to calculate the correlation between key energy-consuming enterprises and industry standards, and the energy-saving potential of the enterprise is judged based on the size of the cosine.

[0069] Table 1 Calculation of enterprise technology and energy saving potential

[0070] Account Number Energy saving potential 18762171 1.0 18746107 0.98 18745170 0.86 18759278 0.79 …… ……

[0071] ④ Energy-saving potential level classification: based on the principle of correlation classification, energy-saving potential levels are divided. The higher the level, the greater the energy-saving potential.

[0072] Table 2 Enterprise technical energy saving potential level

[0073] Energy saving potential Energy saving level 0-0.2 Very high 0.2-0.4 high 0.4-0.6 medium 0.6-0.8 Low 0.8-1.0 Very low

[0074] (3) Improvement of enterprise energy efficiency

[0075] After conducting enterprise energy efficiency analysis and enterprise energy-saving potential assessment, we provide energy consumption optimization suggestions and design energy efficiency improvement application scenarios. We help enterprises fully understand their own energy consumption conditions and characteristics, find the potential for energy consumption optimization, and formulate corresponding improvement measures to improve energy efficiency and reduce energy consumption costs. We help relevant departments promote energy conservation and emission reduction, promote industrial upgrading and transformation, optimize energy policies and management, and promote economic development and sustainable development.

[0076] According to S5, through the verification of the historical energy consumption data in the enterprise energy efficiency evaluation and improvement model, the accuracy and stability of the model are optimized and adjusted, the energy consumption and energy cost of key energy-consuming enterprises are predicted, and the enterprises are supported to formulate energy management plans based on production conditions.

[0077] According to S6, based on the results of the model, the energy efficiency of the power system is explained and analyzed, and corresponding optimization suggestions and measures are proposed. Suggestions include load regulation, power consumption optimization, and electricity price strategy adjustment to improve energy efficiency and reduce electricity costs. For example, based on load shift optimization, according to the power consumption of enterprise users at each hour of the month, according to the forward and backward one hour and two hours, combined with the time-of-use electricity price in the electricity price configuration, the two groups of peak, peak, flat, valley, and total electricity charges of the user are calculated for the whole month, and an optimization energy consumption strategy is proposed through comparative analysis.

[0078] The research and application of enterprise energy efficiency assessment and improvement can better provide energy-consuming enterprises with more efficient and high-quality energy-saving services, while improving the power companies' ability to tap the potential of enterprise energy-consuming units and expand the power companies' interactive service capabilities.

[0079] Therefore, the technical solution of the present invention is an enterprise energy efficiency evaluation and improvement model based on electricity data reflecting the enterprise's energy consumption rate. It targets existing problems and is continuously optimized according to business application feedback. The indicator data is further improved and standardized to support the actual application of energy-saving potential evaluation and enterprise efficiency improvement.

[0080] (1) The present invention can be applied to improve the energy efficiency of enterprises. By analyzing the big data of electricity, it is possible to gain an in-depth understanding of the energy usage of enterprises, discover abnormal situations and potential problems in energy consumption, and take timely measures to adjust and optimize, thereby reducing energy costs.

[0081] (2) The application of the present invention supports energy conservation, emission reduction and dual carbon goals of relevant departments. The energy efficiency analysis and improvement model can provide data support for relevant departments, help them monitor and manage energy consumption, and promote energy conservation and emission reduction.

[0082] (3) The present invention comprehensively considers multi-dimensional power big data such as electricity volume, electricity price, electricity fee, load, etc., and constructs a method for energy efficiency evaluation and improvement model to realize energy efficiency analysis and evaluation of key energy-consuming enterprises, and help enterprises save energy and reduce consumption. It aims to fully tap the value of power big data, help enterprises understand their own energy utilization efficiency, discover abnormal situations and potential problems in enterprise energy consumption, tap the potential for improving enterprise energy efficiency, support enterprises in formulating energy use strategies and energy-saving measures, help enterprises reduce energy costs, and help achieve energy conservation, emission reduction and dual carbon goals.

[0083] The present invention can be widely used in the fields of power supply and utilization operation management and energy efficiency analysis and evaluation of key energy-consuming enterprises.

Claims

1. A method for constructing an enterprise energy efficiency evaluation and improvement model based on power big data, characterized by: The following steps are involved: S1. Data collection; S2, data preprocessing; S3, feature index extraction; S4, model construction; S5, model evaluation and optimization; S6, model interpretation and application; The method for constructing an enterprise energy efficiency evaluation and improvement model based on power big data is based on an enterprise energy efficiency evaluation and improvement model that reflects the energy consumption rate of an enterprise based on power data. It is used for energy efficiency analysis, evaluation and improvement of key energy-consuming enterprises, discovers abnormal situations and potential problems in enterprise energy consumption, and helps enterprises reduce energy costs.

2. The method for constructing an enterprise energy efficiency evaluation and improvement model based on power big data according to claim 1 is characterized in that The step S1 matches the power data including power consumption, load curve, electricity price, and electricity fee of power users in the region through fields including the enterprise power user number and the unified social credit code, and obtains other energy consumption related data; these data are collected in real time through devices including smart meters, sensors, and monitoring systems, and can also be collected offline through historical data.

3. The method for constructing an enterprise energy efficiency evaluation and improvement model based on power big data according to claim 1 is characterized in that The step S2 is to clean and normalize the collected data, delete invalid or erroneous data records during the cleaning process, and convert all data into values ​​between 0 and 1 during the normalization process to facilitate subsequent analysis and modeling; The normalized formula is: Among them, x scal is the normalized value, x is the original value, and x min is the minimum value, x max is the maximum value.

4. The method for constructing an enterprise energy efficiency evaluation and improvement model based on power big data according to claim 1 is characterized in that The step S3 analyzes the enterprise's power data, uses an anomaly detection algorithm and benchmarks with the same industry to identify data points with abnormal energy consumption, and extracts valuable features based on factors such as the enterprise's load conditions and production operations.

5. The method for constructing an enterprise energy efficiency evaluation and improvement model based on power big data according to claim 4 is characterized in that The anomaly detection algorithm includes at least a box plot and a Z-Score method; The valuable features include at least the changing trend of electricity consumption and the peak-to-valley difference of the load curve.

6. The method for constructing an enterprise energy efficiency evaluation and improvement model based on power big data according to claim 1 is characterized in that In step S4, an enterprise energy efficiency evaluation and improvement model is established based on the energy consumption data of key energy-consuming enterprises.

7. The method for constructing an enterprise energy efficiency evaluation and improvement model based on power big data according to claim 6 is characterized by: The enterprise energy efficiency evaluation and improvement model is divided into three parts: enterprise energy efficiency analysis, enterprise energy saving evaluation, and enterprise energy efficiency improvement; The enterprise energy efficiency analysis part is to analyze the enterprise's energy efficiency situation by carrying out average electricity price analysis, billing method analysis, average load rate analysis, peak electricity consumption ratio analysis, carbon emission analysis, and load overload analysis based on the enterprise's daily electricity consumption, active power, peak, flat and valley electricity consumption distribution indicators, and support the enterprise's energy efficiency potential assessment and the formulation of enterprise energy efficiency improvement strategies; The enterprise energy conservation assessment part is based on the classification of basic attributes of the enterprise, and conducts enterprise electricity consumption behavior analysis and industry benchmarking analysis, and evaluates the energy conservation potential and energy conservation level of the enterprise through a decision tree model; Through the energy consumption analysis carried out in different dimensions, the energy consumption of enterprises can be mastered, enterprises with energy-saving potential can be discovered, a list of enterprises with energy-saving potential can be formed, and energy-saving electricity recommendation reports can be provided for energy-saving potential enterprises; The above energy consumption analysis shall at least include total energy consumption, energy consumption structure, energy consumption trend, and comparative analysis of energy consumption in different industries; The aforementioned enterprise energy efficiency improvement part provides energy consumption optimization suggestions and designs energy efficiency improvement application scenarios after conducting enterprise energy efficiency analysis and enterprise energy saving potential assessment; Help enterprises fully understand their own energy consumption situation and characteristics, find the potential for energy consumption optimization, and formulate corresponding improvement measures, so as to improve energy efficiency, reduce energy consumption costs, promote industrial upgrading and transformation, and optimize energy policies and management.

8. The method for constructing an enterprise energy efficiency evaluation and improvement model based on power big data according to claim 1 is characterized by: The step S5 is to optimize and adjust the accuracy and stability of the model through the verification of the historical energy consumption data in the enterprise energy efficiency evaluation and improvement model, predict the energy consumption and energy cost of key energy-consuming enterprises, and support enterprises to formulate energy management plans based on production conditions.

9. The method for constructing an enterprise energy efficiency evaluation and improvement model based on power big data according to claim 1 is characterized in that The step S6 is to explain and analyze the energy efficiency of the power system according to the results of the model, and put forward corresponding optimization suggestions and measures; The suggestions include at least load regulation, power consumption optimization, and electricity price strategy adjustment to improve energy efficiency and reduce electricity costs.

10. The method for constructing an enterprise energy efficiency evaluation and improvement model based on power big data according to claim 1 is characterized in that The method for constructing an enterprise energy efficiency evaluation and improvement model based on power big data is based on an enterprise energy efficiency evaluation and improvement model that reflects the energy consumption rate of an enterprise based on power data. It fully taps the value of power big data and is used for energy efficiency analysis, evaluation and improvement of key energy-consuming enterprises. It discovers abnormal situations and potential problems in enterprise energy consumption, taps the potential for improving enterprise energy efficiency, supports enterprises in formulating energy use strategies and energy-saving measures, and helps enterprises reduce energy costs.

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