Energy consumption data management system and method suitable for smart cities

Abstract

The present invention discloses an energy consumption data management system and method suitable for smart cities. The energy consumption data analysis model is constructed to analyze and predict the collected energy consumption data of smart cities, and then an optimization control strategy and an energy consumption management operation model are formulated to provide a decision-making basis for decision makers and operators. At the same time, a smart city energy consumption defect description model is constructed to identify, store and classify energy consumption data that meets the predefined energy defects, and a data repair decision is used to repair the defects of the identified and classified energy consumption data, so as to accurately locate the main causes of the energy consumption defects in the smart city and obtain a disposal plan for the energy defects, grasp the latest status of the energy consumption defects in the smart city, and quickly and scientifically dispose of the defects when they recur. Moreover, by constructing an energy consumption differentiation management model, the main characteristics and related levels of energy consumption use in smart cities are analyzed, and energy-using departments can be provided with energy use plans suitable for their own conditions.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of data management, and in particular to an energy consumption data management system and method suitable for smart cities. Background Art

[0002] Smart city construction is an important trend in future urban development and an inevitable requirement for sustainable urban development. Intelligent mining of energy consumption data is the foundation and an important part of the smart city energy conservation supervision system, and plays a decisive role in improving smart city energy efficiency and energy conservation and emission reduction. Summary of the invention

[0003] In view of this, the purpose of the present invention is to provide an energy consumption data management system and method suitable for smart cities, which will help promote the networking, real-time and intelligent supervision of energy consumption in smart cities.

[0004] The present invention provides an energy consumption data management system suitable for smart cities, which is used to effectively manage energy consumption data collected by an external energy consumption data collection system, including an energy consumption data prediction system, an energy consumption data defect management system and an energy consumption data differentiation management system, wherein:

[0005] The energy consumption data prediction system is used to store, analyze and predict the collected energy consumption data, and formulate an optimization control strategy and an energy consumption management operation model based on the analysis and prediction results;

[0006] The energy consumption data defect management system is used to identify, classify and repair defects in the collected energy consumption data that meets the predefined energy defect.

[0007] The energy consumption data differentiation management system performs differentiated classification and valuation on the collected energy consumption data based on a preset energy consumption data classification model and a preset energy consumption differentiation knowledge base, and obtains the usage trends of different energy sources based on the results of differentiated classification and valuation, and then formulates differentiated plans for different users to guide user energy consumption.

[0008] Furthermore, the energy consumption data prediction system includes an energy consumption prediction cluster server and an optimization control strategy module. The energy consumption prediction cluster server stores the collected energy consumption data and constructs an energy consumption data analysis model. Based on the energy consumption data analysis model, the energy consumption data correlation, energy consumption distribution and energy consumption change are analyzed to thereby form an energy consumption data prediction model. Based on a preset BP neural network, the energy consumption change trend is predicted by hour, day, week, month, quarter and year. The optimization control strategy module designs optimization control strategies and energy consumption management operation models for different users according to the predicted energy consumption change trend, providing a basis for decision makers and operators.

[0009] Furthermore, the energy consumption data defect management system includes an energy defect library, an energy defect repairer, an energy defect expert decision library and an energy classification knowledge base, wherein:

[0010] The energy defect database is used to mine the collected energy consumption data that meets the predefined energy defect, and to identify and store the energy consumption data;

[0011] The energy defect repairer is used to classify and repair the energy defects corresponding to the energy consumption data stored in the energy defect library, and feed back the recorded repair situation to the energy defect library;

[0012] The energy defect expert decision library improves the data repair decision according to the energy consumption data stored by the energy defect repairer, transmits the data repair decision corresponding to each type of energy defect to the energy defect repairer, and feeds back the energy defect classification to the energy classification knowledge library;

[0013] The energy classification knowledge base classifies and analyzes the energy consumption data in the energy defect library based on the SOM technology, and feeds back the classification analysis results to the energy defect library to improve the energy defect library, while providing basic decision rules for the energy defect expert decision library.

[0014] Furthermore, the specific method in which the energy defect repairer is used to classify and repair the energy defects corresponding to the energy consumption data stored in the energy defect library is as follows: the energy defect repairer classifies the energy defects corresponding to the energy consumption data stored in the energy defect library into explicit defects and implicit defects. For explicit defects, the energy defect repairer is used to reduce the excessive single attribute value to a reasonable level; for implicit defects, the main cause is found, and the attribute value associated with the main cause is reduced.

[0015] Furthermore, the energy consumption differentiation management module includes an energy consumption differentiation classifier, an energy consumption differentiation estimator, an energy consumption differentiation knowledge base, an energy consumption differentiation predictor and an energy consumption differentiation solution module, wherein:

[0016] The energy consumption differential classifier is used to establish an energy consumption data classification model, and to perform differential classification on the collected energy consumption data based on the established energy consumption data classification model;

[0017] The energy consumption differentiation knowledge base generates an energy consumption differentiation rule set according to the external environment data input, and guides the differentiated valuation analysis and pre-value analysis of various energy consumptions based on the energy consumption differentiation rule set, and updates the energy consumption differentiation knowledge base in real time based on the valuation analysis results and pre-value analysis results;

[0018] The energy consumption differential estimator analyzes and evaluates various types of energy consumption data according to the energy consumption differential rule set and the differential classification results of the energy consumption differential classifier to obtain differential valuations of various types of energy consumption;

[0019] The energy consumption differentiation predictor analyzes and mines various differentiated energy consumption data based on the energy consumption differentiation knowledge base and the differentiated valuation of various energy consumptions to obtain the usage trends of different energy sources;

[0020] The energy consumption differentiation scheme module formulates a differentiation scheme based on the usage trends of different energy sources to guide the user's energy consumption.

[0021] Furthermore, the external environment data of the energy consumption differentiation knowledge base includes the actual geographical environment, economic development level, residents' income and corporate profitability.

[0022] Furthermore, the differentiated plans include differentiated plans for decision-making departments and differentiated plans for enterprise departments. The differentiated plans for decision-making departments include regional energy consumption analysis, regional energy consumption law analysis and analysis of the specific distribution of various energy-consuming units; the differentiated plans for enterprise departments include guidance on charging plan selection, differentiated cost forecasting and enterprise energy-saving guidance plans.

[0023] Another aspect of the present invention further provides an energy consumption data management method applicable to a smart city, the method performing data management based on the energy consumption data management system applicable to a smart city as described above, comprising the following steps:

[0024] S1. Obtain energy consumption data of the smart city through a data acquisition system and transmit the collected energy consumption data to an energy consumption data management system;

[0025] S2. Build an energy consumption data prediction system, use the energy consumption data collected by the energy consumption data prediction system to store, analyze and predict, and formulate an optimization control strategy and energy consumption management operation model based on the analysis and prediction results;

[0026] S3, constructing an energy consumption data defect management system, and using the energy consumption data defect management system to identify, classify and repair defects in the collected energy consumption data that meets the predefined energy defects;

[0027] S4. Construct a differentiated management system for energy consumption data. Based on the preset energy consumption data classification model and the preset energy consumption differentiation knowledge base, the energy consumption differentiation management system is used to perform differentiated classification and valuation on the collected energy consumption data. Based on the results of differentiated classification and valuation, the usage trends of different energy sources are obtained, and then differentiated plans for different users are formulated to guide user energy consumption.

[0028] In the present invention, an energy consumption data analysis model is constructed to analyze and predict the collected smart city energy consumption data, and then an optimization control strategy and an energy consumption management operation model are formulated to provide a decision-making basis for decision makers and operators; at the same time, a smart city energy consumption defect description model is constructed to identify, store and classify energy consumption data that meets the predefined energy defects, and the identified and classified energy consumption data are repaired using data repair decisions, so as to accurately locate the main causes of smart city energy consumption defects and obtain energy defect disposal plans, grasp the latest status of smart city energy consumption defects, and quickly and scientifically deal with defects when they recur; moreover, by constructing an energy consumption differentiation management model, the main characteristics and related levels of smart city energy consumption use are analyzed, and energy-using departments can be provided with energy use plans suitable for their own conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0030] Figure 1 It is a structural block diagram of an energy consumption data management system suitable for smart cities in the present invention;

[0031] Figure 2 It is a structural schematic diagram of the energy consumption data prediction system in the present invention;

[0032] Figure 3 It is a structural schematic diagram of the energy consumption data defect management system in the present invention;

[0033] Figure 4 It is a structural diagram of the energy consumption data differentiation management system in the present invention. DETAILED DESCRIPTION

[0034] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0035] In the present invention, the energy consumption data collection system includes all systems suitable for collecting energy consumption data of smart cities, including but not limited to energy consumption data collection systems based on Internet of Things technology.

[0036] like Figure 1 As shown, the present invention provides an energy consumption data management system suitable for smart cities, the energy consumption data management system is used to effectively manage the energy consumption data collected by the external energy consumption data collection system, including an energy consumption data prediction system, an energy consumption data defect management system and an energy consumption data differentiation management system, wherein:

[0037] The energy consumption data prediction system is used to store, analyze and predict the collected energy consumption data, and formulate an optimization control strategy and an energy consumption management operation model based on the analysis and prediction results;

[0038] The energy consumption data defect management system is used to identify, classify and repair defects in the collected energy consumption data that meets the predefined energy defect.

[0039] The energy consumption data differentiation management system performs differentiated classification and valuation on the collected energy consumption data based on a preset energy consumption data classification model and a preset energy consumption differentiation knowledge base, and obtains the usage trends of different energy sources based on the results of differentiated classification and valuation, and then formulates differentiated plans for different users to guide user energy consumption.

[0040] like Figure 2 As shown, the energy consumption data prediction system includes an energy consumption prediction cluster server and an optimization control strategy module. The energy consumption prediction cluster server stores the collected energy consumption data and constructs an energy consumption data analysis model. Based on the energy consumption data analysis model, the energy consumption data correlation, energy consumption distribution and energy consumption change are analyzed to further form an energy consumption data prediction model. Based on the preset BP neural network, the energy consumption change trend is predicted by hour, day, week, month, quarter and year; the optimization control strategy module designs the optimization control strategy and the energy consumption management operation model of different users according to the predicted energy consumption change trend, so as to provide a basis for decision makers and operators.

[0041] In this embodiment, the smart city energy consumption forecast includes aspects such as the development trend of energy consumption data, seasonal changes, periodic changes and random changes. In order to provide users with energy consumption warning information and analysis and prediction accuracy in a timely manner, and provide a basis for urban energy scheduling and energy use strategies, a smart city energy consumption data analysis model is constructed in view of the characteristics of smart city energy consumption data such as large quantity, dynamic changes, and multiple departments involved, so as to analyze the relevance of energy consumption data, energy consumption distribution and energy consumption changes. At the same time, a smart city energy consumption prediction model is constructed. Based on the BP neural network technology, the energy consumption change trend is analyzed and predicted, and energy consumption data prediction with accuracy of hours, days, weeks, months and quarters is realized. Then, various operation models suitable for use by various management departments at all levels are established to provide decision-making basis for decision makers and operators.

[0042] In this embodiment, the optimization control strategy module is only for energy-saving expert users, and for ordinary users, only the prediction and warning are made through the analysis and prediction results of the prediction cluster server. When the prediction value in the prediction cluster server exceeds the preset threshold, a warning signal is issued to the ordinary user so that the user can take corresponding measures. In other embodiments, a warning signal can also be issued to energy-saving expert users, and the optimization control strategy and energy consumption management operation model for ordinary users can also be designed through the optimization control strategy module.

[0043] like Figure 3 As shown, the energy consumption data defect management system includes an energy defect library, an energy defect repairer, an energy defect expert decision library and an energy classification knowledge base, wherein:

[0044] The energy defect database is used to mine the collected energy consumption data that meets the predefined energy defect, and to identify and store the energy consumption data;

[0045] The energy defect repairer is used to classify and repair the energy defects corresponding to the energy consumption data stored in the energy defect library, and feed back the recorded repair situation to the energy defect library;

[0046] The energy defect expert decision library improves the data repair decision according to the energy consumption data stored by the energy defect repairer, transmits the data repair decision corresponding to each type of energy defect to the energy defect repairer, and feeds back the energy defect classification to the energy classification knowledge library;

[0047] The energy classification knowledge base classifies and analyzes the energy consumption data in the energy defect library based on the SOM technology, and feeds back the classification analysis results to the energy defect library to improve the energy defect library, while providing basic decision rules for the energy defect expert decision library.

[0048] Among them, the specific method in which the energy defect repairer is used to classify and repair the energy defects corresponding to the energy consumption data stored in the energy defect library is: the energy defect repairer classifies the energy defects corresponding to the energy consumption data stored in the energy defect library, specifically into explicit defects and implicit defects. For explicit defects, the energy defect repairer is used to reduce the excessive single attribute value to a reasonable level; for implicit defects, the main cause is found, and the attribute value associated with the main cause is reduced.

[0049] In this embodiment, the energy consumption defects in the collected energy consumption data are divided into explicit defects and implicit defects according to whether the city's energy consumption attribute value exceeds the standard. In order to effectively reduce the waste caused by "human", "failure" or other unpredictable reasons in the energy consumption process, a smart city energy consumption defect description model is constructed to achieve a standardized description of urban energy consumption defects, and based on this, the smart city energy consumption defects are refinedly classified, so as to accurately locate the main causes of urban energy consumption defects and quickly obtain energy defect disposal solutions for classified repairs, so that the smart city energy consumption defects can be tracked and monitored throughout the process, the latest status of urban energy consumption defects can be accurately grasped, and scientific disposal can be carried out quickly when defects recur.

[0050] like Figure 4 As shown, the energy consumption differentiation management module includes an energy consumption differentiation classifier, an energy consumption differentiation estimator, an energy consumption differentiation knowledge base, an energy consumption differentiation predictor and an energy consumption differentiation solution module, wherein:

[0051] The energy consumption differential classifier is used to establish an energy consumption data classification model, and to perform differential classification on the collected energy consumption data based on the established energy consumption data classification model;

[0052] The energy consumption differentiation knowledge base generates an energy consumption differentiation rule set according to the external environment data input, and guides the differentiated valuation analysis and pre-value analysis of various energy consumptions based on the energy consumption differentiation rule set, and updates the energy consumption differentiation knowledge base in real time based on the valuation analysis results and pre-value analysis results;

[0053] The energy consumption differential estimator analyzes and evaluates various types of energy consumption data according to the energy consumption differential rule set and the differential classification results of the energy consumption differential classifier to obtain differential valuations of various types of energy consumption;

[0054] The energy consumption differentiation predictor analyzes and mines various differentiated energy consumption data based on the energy consumption differentiation knowledge base and the differentiated valuation of various energy consumptions to obtain the usage trends of different energy sources;

[0055] The energy consumption differentiation scheme module formulates a differentiation scheme based on the usage trends of different energy sources to guide the user's energy consumption.

[0056] Among them, the differentiated scheme includes the differentiated scheme of decision-making departments and the differentiated scheme of enterprise departments. The differentiated scheme of decision-making departments includes regional energy consumption analysis, regional energy consumption law analysis and specific distribution analysis of various energy-consuming units; the differentiated scheme of enterprise departments includes guidance on charging scheme selection, differentiated cost forecasting and enterprise energy-saving guidance scheme. According to the actual data of various energy consumption in smart cities, by analyzing the trend of energy consumption data in different regions, different industries and different enterprises, a differentiated management model of smart city energy consumption can be constructed. Based on the constructed differentiated management model of energy consumption, the main characteristics and relevant levels of smart city energy consumption are analyzed to formulate differentiated schemes to guide user energy consumption, such as: formulating a dynamic energy pricing method for government management departments to provide a decision-making basis for energy pricing and energy scheduling of government management departments; formulating a method for studying intelligent energy consumption for energy-using departments to provide energy-using departments with energy use plans suitable for their own conditions.

[0057] In summary, through the above settings, the smart city energy consumption differentiation management system can provide government department managers with regional energy consumption data analysis results, which is convenient for managers to find out whether regional energy consumption has a changing pattern (such as seasonal changes), the specific distribution of "high energy consumption units" and "interruptible energy consumption units"; provide enterprises with actual energy consumption data analysis results, and propose optimization energy consumption suggestions to enterprises based on the analysis results to save energy consumption costs for enterprises. In order to obtain the above results, the energy consumption differentiation management module intends to adopt the following experimental means: First, based on the energy consumption differentiation management data warehouse, analyze various types of energy data in the data center as model input; in addition, the model input also includes: local actual geographical environment, corporate profitability, economic development level, and energy use. Through these inputs, the data is managed differently in the model, and predictive analysis is performed based on actual differences.

[0058] The present invention also provides an energy consumption data management method applicable to a smart city. The method performs data management based on the energy consumption data management system applicable to a smart city as described above, and comprises the following steps:

[0059] S1. Obtain energy consumption data of the smart city through a data acquisition system and transmit the collected energy consumption data to an energy consumption data management system;

[0060] S2. Build an energy consumption data prediction system, use the energy consumption data collected by the energy consumption data prediction system to store, analyze and predict, and formulate an optimization control strategy and energy consumption management operation model based on the analysis and prediction results;

[0061] S3, constructing an energy consumption data defect management system, and using the energy consumption data defect management system to identify, classify and repair defects in the collected energy consumption data that meets the predefined energy defects;

[0062] S4. Construct a differentiated management system for energy consumption data. Based on the preset energy consumption data classification model and the preset energy consumption differentiation knowledge base, the energy consumption differentiation management system is used to perform differentiated classification and valuation on the collected energy consumption data. Based on the results of differentiated classification and valuation, the usage trends of different energy sources are obtained, and then differentiated plans for different users are formulated to guide user energy consumption.

[0063] Based on the above description of the energy consumption data management system suitable for smart cities, the energy consumption data management method has the same technical effect and will not be repeated here.

[0064] In summary, the present invention has the following advantages:

[0065] The present invention constructs an energy consumption data analysis model to analyze and predict the collected smart city energy consumption data, and then formulates an optimization control strategy and an energy consumption management operation model to provide a decision-making basis for decision makers and operators; at the same time, a smart city energy consumption defect description model is constructed to identify, store and classify energy consumption data that meets the predefined energy defects, and use data repair decisions to repair defects in the identified and classified energy consumption data, thereby accurately locating the main causes of smart city energy consumption defects and obtaining energy defect disposal plans, mastering the latest status of smart city energy consumption defects, and quickly and scientifically disposing of defects when they recur; moreover, by constructing an energy consumption differentiation management model, the main characteristics and related levels of smart city energy consumption use are analyzed, and energy-using departments can be provided with energy use plans suitable for their own conditions.

[0066] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An energy consumption data management system suitable for smart cities, It is characterized in that The energy consumption data management system is used to effectively manage the energy consumption data collected by the external energy consumption data collection system, including an energy consumption data prediction system, an energy consumption data defect management system and an energy consumption data differentiation management system, wherein: The energy consumption data prediction system is used to store, analyze and predict the trend of the collected energy consumption data, and provide energy consumption usage suggestions and formulate optimization control strategies for users based on the analysis and trend prediction results; The energy consumption data defect management system is used to identify, classify and repair defects in the collected energy consumption data that meets the predefined energy defect. The energy consumption data differentiation management system performs differentiated classification and valuation on the collected energy consumption data based on a preset energy consumption data classification model and a preset energy consumption differentiation knowledge base, and obtains the usage trends of different energy sources based on the results of the differentiated classification and valuation, and then formulates differentiated plans for different users to guide the energy consumption of users; The energy consumption data defect management system includes an energy defect library, an energy defect repairer, an energy defect expert decision library and an energy classification knowledge library, wherein: The energy defect database is used to mine the collected energy consumption data that meets the predefined energy defect, and to identify and store the energy consumption data; The energy defect repairer is used to classify and repair the energy defects corresponding to the energy consumption data stored in the energy defect library, and feed back the recorded repair situation to the energy defect library; The energy defect expert decision library improves the data repair decision according to the energy consumption data stored by the energy defect repairer, transmits the data repair decision corresponding to each type of energy defect to the energy defect repairer, and feeds back the energy defect classification to the energy classification knowledge library; The energy classification knowledge base classifies and analyzes the energy consumption data in the energy defect library based on the SOM technology, and feeds back the classification analysis results to the energy defect library to improve the energy defect library, and at the same time provides basic decision rules for the energy defect expert decision library; The specific method in which the energy defect repairer is used to classify and repair the energy defects corresponding to the energy consumption data stored in the energy defect library is as follows: the energy defect repairer classifies the energy defects corresponding to the energy consumption data stored in the energy defect library, specifically into explicit defects and implicit defects. For explicit defects, the energy defect repairer is used to reduce the excessive single attribute value to a reasonable level; for implicit defects, the main cause is found, and the attribute value associated with the main cause is reduced.

2. The energy consumption data management system suitable for smart cities according to claim 1, It is characterized in that The energy consumption data prediction system includes an energy consumption prediction cluster server and an optimization control strategy module. The energy consumption prediction cluster server stores the collected energy consumption data and constructs an energy consumption data analysis model. Based on the energy consumption data analysis model, the energy consumption data correlation, energy consumption distribution and energy consumption change are analyzed, and then an energy consumption data prediction model is formed. Based on a preset BP neural network, the energy consumption change trend is predicted by hour, day, week, month, quarter and year; The optimization control strategy module designs optimization control strategies and energy consumption management operation models for different users according to the predicted energy consumption change trend, providing a basis for decision makers and operators.

3. The energy consumption data management system suitable for smart cities according to claim 1, It is characterized in that The energy consumption differentiation management module includes an energy consumption differentiation classifier, an energy consumption differentiation estimator, an energy consumption differentiation knowledge base, an energy consumption differentiation predictor and an energy consumption differentiation solution module, wherein: The energy consumption differential classifier is used to establish an energy consumption data classification model, and to perform differential classification on the collected energy consumption data based on the established energy consumption data classification model; The energy consumption differentiation knowledge base generates an energy consumption differentiation rule set according to the external environment data input, and guides the differentiated valuation analysis and pre-value analysis of various energy consumptions based on the energy consumption differentiation rule set, and updates the energy consumption differentiation knowledge base in real time based on the valuation analysis results and pre-value analysis results; The energy consumption differential estimator analyzes and evaluates various types of energy consumption data according to the energy consumption differential rule set and the differential classification results of the energy consumption differential classifier to obtain differential valuations of various types of energy consumption; The energy consumption differentiation predictor analyzes and mines various differentiated energy consumption data based on the energy consumption differentiation knowledge base and the differentiated valuation of various energy consumptions to obtain the usage trends of different energy sources; The energy consumption differentiation scheme module formulates a differentiation scheme based on the usage trends of different energy sources to guide the user's energy consumption.

4. The energy consumption data management system suitable for smart cities according to claim 3, It is characterized in that The external environment data of the energy consumption differentiation knowledge base includes the actual geographical environment, economic development level, residents' income and corporate profitability.

5. The energy consumption data management system suitable for smart cities according to claim 4, It is characterized in that The differentiated plans include differentiated plans for decision-making departments and differentiated plans for enterprise departments. The differentiated plans for decision-making departments include regional energy consumption analysis, regional energy consumption law analysis and analysis of the specific distribution of various energy-consuming units; the differentiated plans for enterprise departments include guidance on charging plan selection, differentiated cost forecasts and enterprise energy-saving guidance plans.

6. An energy consumption data management method suitable for smart cities, It is characterized in that The method performs data management based on the energy consumption data management system applicable to smart cities according to any one of claims 1 to 5, comprising the following steps: S1. Obtain energy consumption data of the smart city through a data acquisition system and transmit the collected energy consumption data to an energy consumption data management system; S2. Build an energy consumption data prediction system, use the energy consumption data collected by the energy consumption data prediction system to store, analyze and predict, and formulate an optimization control strategy and energy consumption management operation model based on the analysis and prediction results; S3, constructing an energy consumption data defect management system, and using the energy consumption data defect management system to identify, classify and repair defects in the collected energy consumption data that meets the predefined energy defects; S4. Construct a differentiated management system for energy consumption data. Based on the preset energy consumption data classification model and the preset energy consumption differentiation knowledge base, the energy consumption differentiation management system is used to perform differentiated classification and valuation on the collected energy consumption data. Based on the results of differentiated classification and valuation, the usage trends of different energy sources are obtained, and then differentiated plans for different users are formulated to guide user energy consumption.

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