Renewable energy consumption industry accounting decomposition method and system
By constructing a high-precision industry data analysis module and introducing a variety of intelligent algorithms, the problem of insufficient decomposition accuracy in the accounting of renewable energy consumption industry has been solved, realizing accurate decomposition and dynamic prediction of energy consumption data, and improving the intelligence and real-time optimization capabilities of energy management.
Patent Information
- Application Number
- CN202510804161.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-17
- Publication Date
- 2025-11-07
AI Technical Summary
Existing technologies in renewable energy consumption accounting suffer from problems such as insufficient decomposition accuracy, lack of dynamic prediction capabilities, insufficient time granularity, insufficient collaborative analysis capabilities, and low levels of intelligence and automation. These issues lead to theoretical flaws in energy system optimization schemes, making it difficult to meet the real-time monitoring and control needs of modern energy systems.
A high-precision industry data analysis module is constructed, which introduces the extended KAYA identity model and deep learning technology. It adopts the analytic hierarchy process (AHP) and key performance indicator (KPI) algorithms, combined with ridge regression and time series algorithms, to achieve accurate decomposition, dynamic prediction and automated processing of energy consumption data, thereby improving the coordinated optimization of energy supply and consumption structure.
It enables precise decomposition and dynamic prediction of energy consumption data, improves the accuracy, flexibility and efficiency of renewable energy consumption, supports the refined and intelligent development of energy management, and meets the real-time optimization needs of modern energy systems.
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Figure CN120911735A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of energy management, in particular to a renewable energy consumption industry accounting and decomposition method and system. BACKGROUND
[0002] The industry accounting and decomposition of renewable energy consumption is an important technical basis for promoting energy structure transformation and achieving the "double carbon" goal. Currently, the field mainly relies on traditional statistical models for research, but the existing technology has significant limitations in terms of precision, dynamics, and intelligence level. First, the existing decomposition model is limited by insufficient data acquisition dimensions and algorithm limitations, making it difficult to achieve fine-grained accounting at the industry level, especially for cross-industry and emerging industries. Systematic bias often occurs in the statistical renewable energy consumption of these industries, resulting in a lack of reliable basis for regional energy distribution decisions. Second, traditional prediction methods mostly use single-dimensional time series analysis, which fails to effectively integrate multi-dimensional dynamic driving factors such as economic development index, industrial structure coefficient, and population flow parameters, making it difficult for medium and long-term energy planning to match the actual needs of regional development. In terms of time dimension, existing technology mostly stops at the annual total decomposition level, lacking the ability to dynamically decompose energy consumption to monthly or even weekly time units, which cannot meet the needs of real-time monitoring and regulation of modern energy systems. More importantly, the existing accounting system generally analyzes renewable energy and traditional fossil fuels separately, ignoring the synergistic effects of both in scenarios such as grid peak shaving and energy storage configuration, resulting in theoretical flaws in the overall optimization of energy systems.
[0003] In addition, traditional methods rely too much on manual experience and manual data processing, which has efficiency bottlenecks in the fusion of multi-source heterogeneous data and dynamic parameter adjustment of complex energy systems, making it difficult to meet the data analysis needs brought by large-scale renewable energy grid connection. These technical defects have hindered energy policy making, grid dispatching optimization, and carbon emission rights trading, especially in the context of accelerating the construction of new power systems. The existing technical system has increasingly shown that theoretical methods lag behind practical needs in supporting high-proportion renewable energy consumption and achieving source-grid-load-storage collaborative optimization.
[0004] In view of the above problems existing in the prior art, the present application is proposed. SUMMARY
[0005] In view of the above problems existing in the prior art, the present application is proposed.
[0006] Therefore, the technical problem to be solved by the present application is to solve the problems of insufficient decomposition precision, lack of dynamic prediction ability, insufficient time granularity, insufficient collaborative analysis ability, and low intelligence and automation in the decomposition of renewable energy consumption in industry accounting. By constructing a high-precision industry data analysis module, introducing an extended KAYA identity model and deep learning technology, using the analytic hierarchy process (AHP) and key performance indicator (KPI) algorithm, realizing the collaborative optimization of energy supply and consumption structure, and developing intelligent and automated tools, the present application realizes accurate decomposition, dynamic prediction, fine-grained time decomposition and automated processing of energy consumption data. Ultimately, the present application improves the precision, flexibility and efficiency of renewable energy consumption in industry accounting decomposition, and promotes the fine and intelligent development of energy management.
[0007] To solve the above technical problems, the present application provides the following technical solutions: a renewable energy consumption industry accounting decomposition method, comprising: analyzing data and establishing an industry data analysis module; constructing an electricity estimation sub-model based on ridge regression and time series algorithm according to industrial comprehensive data; and establishing a monthly model based on the analytic hierarchy process and the key performance indicator algorithm according to the total energy consumption.
[0008] As a preferred scheme of the renewable energy consumption industry accounting decomposition method of the present application, the industry data analysis module includes statistical and analytical energy consumption from the industry dimension, and displays the proportion of energy consumption in the industry and the ranking of industry energy consumption.
[0009] As a preferred scheme of the renewable energy consumption industry accounting decomposition method of the present application, the ridge regression and time series algorithm is based on the regression algorithm of linear regression, and a penalty term is added to the regression coefficient; the results of ridge regression are optimized and predicted by combining the time series algorithm.
[0010] The regression coefficient is calculated by the ridge regression algorithm.
[0011] As a preferred scheme of the renewable energy consumption industry accounting decomposition method of the present application, the electricity estimation sub-model is constructed based on historical electricity consumption, historical comprehensive energy consumption data and historical energy consumption data, and the industrial comprehensive energy consumption is calculated.
[0012] As a preferred scheme of the renewable energy consumption industry accounting decomposition method of the present application, the analytic hierarchy process includes constructing an energy consumption decomposition framework, constructing a hierarchical structure by taking the total energy consumption as the target layer, the monthly energy consumption as the scheme layer, and setting the criterion layer in the middle, forming a judgment matrix by comparing data, and calculating the weight of monthly energy consumption.
[0013] As a preferred scheme of the renewable energy consumption industry accounting decomposition method, the key performance indicator algorithm includes ensuring that the decomposition result conforms to the historical trend and reflects the monthly energy consumption characteristics.
[0014] As a preferred scheme of the renewable energy consumption industry accounting decomposition method, the monthly model includes establishing an energy consumption total amount to month distribution model using a key performance indicator algorithm according to historical data and weights obtained by an analytic hierarchy process.
[0015] Another object of the present application is to provide a renewable energy consumption industry accounting decomposition system that realizes full-process automatic processing from data acquisition, industry energy consumption analysis, electric energy calculation to energy consumption total amount decomposition through the cooperative work of the data analysis module, the electric energy calculation module and the monthly analysis module, improves the intelligentization, refinement and efficiency of energy management, and supports accurate accounting and prediction of renewable energy consumption in the industry and region.
[0016] To solve the above technical problems, the present application provides the following technical scheme: a renewable energy consumption industry accounting decomposition system, comprising: a data analysis module, an electric energy calculation module and a monthly analysis module.
[0017] The data analysis module analyzes data and establishes an industry data analysis module.
[0018] The electric energy calculation module constructs an electric energy calculation sub-model based on ridge regression and time series algorithm according to comprehensive industrial data.
[0019] The monthly analysis module establishes a monthly model based on an analytic hierarchy process and a key performance indicator algorithm according to the energy consumption total amount.
[0020] A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the renewable energy consumption industry accounting decomposition method as described above when executing the computer program.
[0021] A computer readable storage medium having a computer program stored thereon, characterized in that the computer program is executed by a processor to implement the steps of the renewable energy consumption industry accounting decomposition method as described above.
[0022] The application has the advantages that: by fine decomposition and analysis model, the application can accurately calculate the energy consumption distribution of regions and industries, especially in renewable energy consumption, which is significantly better than traditional technology. Considering multiple dimensions driving factors (economy, population, industry, energy supply and demand, etc.), the prediction and calculation results are closer to the actual situation, and dynamic adjustment is supported. The energy consumption decomposition model supports the decomposition of the energy consumption of the whole province and city to the monthly level, further improving the time efficiency and flexibility of energy management, and meeting the real-time optimization demand. Based on deep learning and extended KAYA identity model, intelligent prediction and control of energy consumption and related indicators are realized, which significantly improves the decision support capability. Through accurate decomposition of industry and regional energy consumption and supply and demand optimization, the renewable energy consumption efficiency is improved, and the energy structure transformation is promoted. The model design has wide applicability and can be popularized to multiple industries and regions to meet the energy management needs of different scales. BRIEF DESCRIPTION OF DRAWINGS
[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.
[0024] Figure 1 The flow chart of the renewable energy consumption industry accounting decomposition method provided by an embodiment of the present application.
[0025] Figure 2 The system structure diagram of the renewable energy consumption industry accounting decomposition method provided by an embodiment of the present application. DETAILED DESCRIPTION
[0026] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the protection scope of the present application.
[0027] Embodiment 1, refer to Figure 1 For an embodiment of the present application, the embodiment provides a renewable energy consumption industry accounting decomposition method, which comprises:
[0028] S1: analyze data and establish an industry data analysis module.
[0029] It should be noted that, for example Figure 1S1 shows that the energy consumption of each city and each industry classification in the province is statistically analyzed and displayed, the energy consumption situation is deeply analyzed from the dimension of industry, the proportion of each energy consumption in the industry and the ranking of industry energy consumption are displayed, and the industry energy consumption data of each city in the province is timely mastered.
[0030] Further, the input parameters are: regional code, industry code, historical coal, oil, natural gas, and electricity consumption data;
[0031] The output parameters are: return code, return information, and return data.
[0032] S2: According to the industrial comprehensive data, a power estimation sub-model is constructed based on ridge regression and time series algorithm.
[0033] It should be noted that, as shown in Figure 1 S2, the ridge regression and time series algorithm is a regression algorithm based on linear regression, which adds a penalty term to the regression coefficient; the results of ridge regression are optimized and predicted by combining time series algorithm; the regression coefficient is calculated by ridge regression algorithm; the power estimation sub-model is constructed based on historical industrial electricity consumption, historical comprehensive energy consumption data, and historical energy consumption data, and the industrial comprehensive energy consumption is estimated.
[0034] Further, based on the historical electricity consumption of the three major industries, the historical comprehensive energy consumption data of the three major industries, and the historical energy consumption data of various types, a three major industry power estimation sub-model based on ridge regression and time series algorithm is constructed; the algorithm is based on the historical electricity consumption of the three major industries, the historical comprehensive energy consumption data of the three major industries, and the historical energy consumption data of various types, and a three major industry power estimation sub-model based on ridge regression and time series algorithm is constructed; the model first uses ridge regression algorithm to fit the historical data, ridge regression is a regression algorithm based on linear regression, which adds a penalty term to the regression coefficient to improve the fitting and robustness of the model; the regression coefficient is calculated by ridge regression algorithm, which reflects the influence of independent variables on comprehensive energy consumption; the error term represents the difference between the predicted value and the actual value; the results of ridge regression are further optimized and predicted by combining time series algorithm, and the accurate estimation of the comprehensive energy consumption of the three major industries is realized.
[0035] Further, the input parameters are: domain code, industry code, historical electricity consumption, and historical energy consumption;
[0036] The output parameters are: return code, return information, and power estimation data.
[0037] S3: According to the total energy consumption, a monthly model is established based on analytic hierarchy process and key performance indicator algorithm.
[0038] It should be noted that, as shown in Figure 1As shown in S3, the analytic hierarchy process includes constructing an energy consumption decomposition framework, taking the total energy consumption as a target layer, the monthly energy consumption as a scheme layer, and setting a criterion layer in the middle, forming a judgment matrix through data comparison, and calculating the weight of the monthly energy consumption; the key performance indicator algorithm includes ensuring that the decomposition result conforms to the historical trend and reflects the characteristics of the monthly energy consumption; and the monthly model includes using the key performance indicator algorithm to establish a distribution model of the total energy consumption to the month according to the weight obtained by the analytic hierarchy process and the historical data.
[0039] Further, based on the historical electricity consumption of Yunnan Province and the historical total energy consumption of Yunnan Province, the total energy consumption of the whole province is decomposed to each month through the total energy consumption index to monthly model based on the analytic hierarchy process + key performance indicator algorithm; the algorithm is based on the historical electricity consumption and energy consumption data of Yunnan Province, and uses the analytic hierarchy process (AHP) to construct an energy consumption decomposition framework; the AHP constructs a hierarchical structure, takes the total energy consumption of the whole province as a target layer, takes the monthly energy consumption as a scheme layer, and sets a reasonable criterion layer (such as seasonal variation, economic activity, etc.) in the middle; a judgment matrix is formed by data comparison, and the weight of each monthly energy consumption is calculated; the key performance indicator (KPI) algorithm is used to establish a distribution model of the total energy consumption to each month according to the weight obtained by the historical data and the AHP; the KPI ensures that the decomposition result conforms to the historical trend and reflects the characteristics of the monthly energy consumption;
[0040] Specifically, the input parameters include regional code, historical electricity consumption, and historical total energy consumption;
[0041] The output parameters include return code, return information, and return data.
[0042] Further, the total energy consumption index of the whole province is decomposed to each month based on the analytic hierarchy process and the key performance indicator algorithm, and the total energy consumption of the city is decomposed to each month; including based on the historical electricity consumption of 16 prefectures and the historical total energy consumption of 16 prefectures.
[0043] The above is a schematic scheme of the renewable energy consumption industry accounting decomposition method of the embodiment. It should be noted that the technical scheme of the renewable energy consumption industry accounting decomposition method system belongs to the same concept as the above-mentioned renewable energy consumption industry accounting decomposition method, and the technical scheme of the renewable energy consumption industry accounting decomposition system in this embodiment is not described in detail. The details can be seen from the description of the technical scheme of the renewable energy consumption industry accounting decomposition method.
[0044] Embodiment 2, as an embodiment of the present application, provides a renewable energy consumption industry accounting decomposition method, in order to verify the beneficial effects of the present application, through economic benefit calculation and simulation experiment for scientific demonstration.
[0045] I. Design function: industry data analysis
[0046] Statistical analysis and display of energy consumption of all cities and industries in the province, in-depth statistics and analysis of energy consumption from the perspective of industry, display of energy consumption proportion and industry energy consumption ranking in the industry. Timely grasp of industry energy consumption data of all cities in the province.
[0047] The input parameters are shown in Table 1 as follows:
[0048] Table 1 Input parameter description
[0049] Parameter Name Parameter Description Request Type Mandatory Data Type areaCode Area Code true String industryCode Industry Code true String
[0050] The output parameters are shown in Table 2 as follows:
[0051] Table 2 Output parameter description
[0052] Parameter Name Parameter Description Type code Return Code Integer msg Return Information String data Return Data Object
[0053] The interface elements are shown in Table 3 as follows:
[0054] Table 3 Interface elements
[0055]
[0056]
[0057] The interface element events are shown in Table 4 as follows:
[0058] Table 4 Interface element events
[0059] Element Sequence Number Element Name Event Event Description 00 Query Click Click to query data that meets the conditions
[0060] II. Design function: Yunnan three major industries electricity-energy estimation sub-model
[0061] Based on the historical electricity consumption of three major industries, historical comprehensive energy consumption data of three major industries, and historical energy consumption data of various types, through the three major industries electricity-energy estimation sub-model based on ridge regression + time series algorithm, the comprehensive energy consumption of three major industries is realized.
[0062] The input parameters are shown in Table 5 as follows:
[0063] Table 5 Input parameter description
[0064] Parameter Name Parameter Description Request Type Mandatory Data Type domainCode Domain Code true String
[0065] The output parameters are shown in Table 6 as follows:
[0066] Table 6 Output parameter description
[0067] Parameter Name Parameter Description Type code Return Code Integer msg Return Information String data Electricity Measurement Data Object
[0068] The interface elements are shown in Table 7 as follows:
[0069] Table 7 Interface elements
[0070] Element Sequence Number Element Type Element Name 00 Form Item Domain Code 01 Operation Button Electricity Measurement
[0071] Interface element events are shown in Table 8 below:
[0072] Table 8 Interface element events
[0073] Element Sequence Number Element Name Event Event Description 00 Electricity Measurement Click Click to perform electricity measurement
[0074] III. Design function: Total energy consumption index of the province to the monthly model
[0075] Based on the historical electricity consumption of Yunnan Province, the historical total energy consumption of Yunnan Province, the historical electricity consumption of 16 prefectures, and the historical total energy consumption of 16 prefectures, the total energy consumption index of the province to the monthly model based on the analytic hierarchy process (AHP) + key performance indicators (KPIs) algorithm is used to realize the decomposition of the total energy consumption of the province to each month.
[0076] The input parameters are shown in Table 9 below:
[0077] Table 9 Input parameter description
[0078] Parameter Name Parameter Description Request Type Mandatory Data Type areaCode Area Code true String
[0079] The output parameters are shown in Table 10 below:
[0080] Table 10 Output parameter description
[0081] year Year true String Parameter Name Parameter Description Type code Return Code Integer msg Return Information
[0082] The interface elements are shown in Table 11 below:
[0083] Table 11 Interface elements
[0084] String data Return Data 00 Object Element Sequence Number 01 Element Type Element Name
[0085] The interface element events are shown in Table 12 below:
[0086] Table 12 Interface element events
[0087] Form Item Area Code Form Item Year 00 Operation Button Energy Consumption Total Decomposition Element Sequence Number
[0088] IV. Design function: Total energy consumption index of the city to the monthly model
[0089] Based on the historical electricity consumption of Yunnan Province, the historical total energy consumption of Yunnan Province, the historical electricity consumption of 16 prefectures, and the historical total energy consumption of 16 prefectures, the total energy consumption index of the province to the monthly model based on the analytic hierarchy process (AHP) + key performance indicators (KPIs) algorithm is used to realize the decomposition of the total energy consumption of the province to each month.
[0090] The input parameters are shown in Table 9 above, the output parameters are shown in Table 10 above, the interface elements are shown in Table 11 above, and the interface element events are shown in Table 12 above.
[0091] V. Design function: energy consumption prediction and analysis submodel
[0092] Based on economic development driving factors, GDP growth, economic development driving factors, per capita GDP, economic development driving factors, industrial field key industry development target data, urbanization process driving factors, population, urbanization process driving factors, urbanization rate, industrial structure driving factors, primary, secondary and tertiary industry proportion, industrial structure driving factors, industrial added value accounting for GDP, energy supply structure driving factors, power installed capacity, energy supply structure driving factors, renewable power installed capacity proportion, energy supply structure driving factors, thermal power, wind power, solar power, biomass and other power generation utilization hours, energy consumption structure driving factors, non-fossil energy accounting for primary consumption proportion, energy consumption structure driving factors, coal consumption change rate, energy consumption structure driving factors, oil product consumption change rate, energy consumption structure driving factors, natural gas consumption change rate, energy consumption structure driving factors, social electricity consumption growth rate, energy consumption structure driving factors, industrial electricity consumption growth rate, utilization efficiency level driving factors, unit GDP energy consumption cumulative decline rate, utilization efficiency level driving factors, unit carbon emission cumulative decline rate, utilization efficiency level driving factors, unit GDP energy consumption intensity, utilization efficiency level driving factors, unit added value energy consumption data of primary, secondary and tertiary industries, utilization efficiency level driving factors, unit industrial added value energy consumption decline rate, utilization efficiency level driving factors, per capita living energy consumption data, transportation field carbon emission driving factors, passenger traffic volume and passenger turnover volume, transportation field carbon emission driving factors, freight traffic volume and freight turnover volume, transportation field carbon emission driving factors, transportation structure: highway, railway, inland waterway operation mileage proportion, transportation field carbon emission driving factors, transportation industry energy consumption average annual growth rate and other data, through the energy consumption prediction and analysis submodel based on KAYA identity equation extension model deep learning, annual energy consumption, energy consumption intensity and related index measurement are realized.
[0093] The input parameters are shown in Table 13 as follows:
[0094] Table 13 Input parameter explanation
[0095] Element Name Event Event Description Energy Consumption Total Decomposition Click Click to decompose energy consumption total indicators to each month Parameter Name Parameter Description Request Type Mandatory Data Type areaCode Area Code
[0096] The output parameters are shown in Table 14 as follows:
[0097] Table 14 Output parameter explanation
[0098] true String year Year true String Parameter Name Parameter Description Type code Return Code Integer
[0099] The interface elements are shown in Table 15 below:
[0100] Table 15 Interface Elements
[0101] msg Return Information String 00 data Return Data 01 Object Element Sequence Number 02 Element Type Element Name 03 Form Item Area Code
[0102] The UI element events are shown in Table 16 below:
[0103] Table 16 Interface Element Events
[0104]
[0105] Example 3, as Form Item Year Operation Button Energy Consumption Indicator Analysis Operation Button Energy Intensity Indicator Analysis Figure 2 As shown in the figure, an embodiment of the present invention provides a renewable energy consumption industry accounting and decomposition system, including: a data analysis module, an energy calculation module, and a monthly analysis module;
[0106] The data analysis module analyzes data and establishes an industry data analysis module.
[0107] The power calculation module constructs a power calculation sub-model based on comprehensive industry data and ridge regression and time series algorithms.
[0108] The monthly analysis module establishes a monthly model based on the total energy consumption and using the analytic hierarchy process (AHP) and key performance indicator (KPI) algorithms.
[0109] This embodiment also provides a computing device applicable to the accounting and decomposition methods in the renewable energy consumption industry, including:
[0110] The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement the renewable energy consumption industry accounting and decomposition method proposed in the above embodiments.
[0111] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the renewable energy consumption industry accounting and decomposition method proposed in the above embodiments.
[0112] The storage medium proposed in this embodiment and the renewable energy consumption industry accounting and decomposition method proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.
[0113] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the technical solutions that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.
[0114] The logic and / or steps described herein in other manners, for example, can be considered as a sequence list of executable instructions for implementing logical functions, which can be specifically embodied in any computer readable medium for use by or in conjunction with an instruction execution system, device or equipment (such as a computer-based system, a system including a processor or other system that can fetch and execute instructions from an instruction execution system, device or equipment). For the purpose of this specification, the "computer readable medium" can be any device that can contain, store, communicate, propagate or transport programs for use by or in conjunction with an instruction execution system, device or equipment or in conjunction with these instruction execution systems, devices or equipment.
[0115] It should be understood that parts of the present application can be implemented in hardware, software, firmware or a combination thereof. In the above-described embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one or a combination of the following technologies known in the art: discrete logic circuit with logic gate circuit for implementing logical functions on data signals, application specific integrated circuit with suitable combination logic gate circuit, programmable gate array (PGA), field programmable gate array (FPGA) and the like.
[0116] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit it. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application, which should be covered in the scope of the claims of the present application.
Claims
1. A method for decomposing the accounting of the renewable energy consumption industry, characterized in that: The method comprises the following steps: analyzing data to establish an industry data analysis module; constructing an electricity calculation sub-model based on ridge regression and time series algorithm according to comprehensive industrial data; establishing a monthly model based on analytic hierarchy process and key performance indicator algorithm according to total energy consumption. 2.The renewable energy consumption industry accounting decomposition method of claim 1, wherein: The industry data analysis module comprises statistics and analysis of energy consumption from the industry dimension, display of the proportion of energy consumption in the industry, and ranking of industry energy consumption. 3.The renewable energy consumption industry accounting decomposition method of claim 2, wherein: The ridge regression and time series algorithm is based on a linear regression algorithm, which adds a penalty term to the regression coefficient and optimizes and predicts the results of ridge regression by combining time series algorithm. The regression coefficient is calculated by the ridge regression algorithm. 4.The renewable energy consumption industry accounting decomposition method of claim 3, wherein: The electricity calculation sub-model is constructed based on historical electricity consumption, historical comprehensive energy consumption data, and historical energy consumption data to calculate the comprehensive energy consumption of the industry. 5.The renewable energy consumption industry accounting decomposition method of claim 4, wherein: The analytic hierarchy process comprises constructing an energy consumption decomposition framework, constructing a hierarchical structure, taking total energy consumption as the target layer, monthly energy consumption as the scheme layer, setting a criterion layer in the middle, forming a judgment matrix by comparing data, and calculating the weight of monthly energy consumption. 6.The renewable energy consumption industry accounting decomposition method of claim 5, wherein: The key performance indicator algorithm comprises ensuring that the decomposition results conform to historical trends and reflect the characteristics of monthly energy consumption. 7.The renewable energy consumption industry accounting decomposition method of claim 6, wherein: The monthly model comprises using the key performance indicator algorithm to establish an energy consumption total-to-month distribution model according to historical data and the weight obtained by the analytic hierarchy process.
8. A system for renewable energy consumption industry accounting decomposition based on any one of claims 1-7, characterized in that: The method comprises the following steps: a data analysis module, an electricity calculation module, and a monthly analysis module; The data analysis module analyzes data to establish an industry data analysis module. The electricity calculation module constructs an electricity calculation sub-model based on ridge regression and time series algorithm according to comprehensive industrial data. The monthly analysis module establishes a monthly model based on analytic hierarchy process and key performance indicator algorithm according to total energy consumption. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to realize the steps of the renewable energy consumption industry accounting decomposition method in any one of claims 1 to 7.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the renewable energy consumption industry accounting decomposition method in any one of claims 1 to 7.