Regional carbon emission reduction potential analysis method based on electric power big data

Abstract

The invention discloses a regional carbon emission reduction potential analysis method based on electric power big data, and relates to the technical field of carbon emission reduction potential analysis. Comprising the following steps: S1, a data acquisition step: acquiring electric power related data, including electric power energy consumption data, electric power energy production data and carbon emission data, of a to-be-analyzed area within a preset time period based on electric power big data; s2, a data preprocessing step: constructing an energy consumption intensity learning curve based on the power related data obtained in the S1; s3, an optimization step: optimizing parameters of the energy consumption intensity learning curve obtained in S2 based on a particle swarm optimization algorithm improved by a sparrow algorithm; and S4, based on the optimized energy consumption intensity learning curve obtained in S3, analyzing the carbon emission reduction potential of the target area. The analysis and application of the electric power big data provided by the invention promote the digital transformation of energy management, and through an intelligent tool and system, the refined management and predictive maintenance of energy consumption can be realized, and the energy efficiency is further improved and the emission is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission reduction potential analysis, and in particular to a regional carbon emission reduction potential analysis method based on power big data. Background Art

[0002] Current carbon reduction potential analysis methods face the problem of missing, erroneous or inconsistent data, which directly affects the accuracy of carbon emission calculations and the reliability of analysis. In addition, the real-time nature and update frequency of data are also challenges, as carbon emission monitoring requires frequent and timely data updates.

[0003] In terms of data security and privacy protection: Electricity data usually contains sensitive information, such as the electricity consumption of individuals or enterprises. Therefore, when collecting and processing data, it is necessary to strictly comply with data security and privacy protection regulations to prevent data leakage or abuse.

[0004] Uncertainty in policy and market environment: The formulation of carbon emission reduction strategies needs to take into account policy changes and market fluctuations, such as carbon taxes, subsidy policies, and energy price fluctuations. These factors may have a significant impact on energy consumption and carbon emissions, increasing the complexity and uncertainty of the analysis.

[0005] Continuity of technology updates and applications: With the rapid development of data analysis technologies, such as machine learning and deep learning, we need to continue to pay attention to and introduce the latest technologies to improve the accuracy and efficiency of analysis. At the same time, technology updates and applications require corresponding training and resource investment.

[0006] Implementation and effectiveness evaluation of emission reduction strategies: Even if the analytical method can accurately predict the carbon reduction potential, the actual implementation of emission reduction strategies still faces challenges, including capital investment, technical feasibility, social acceptance, etc. In addition, it is necessary to establish an effective monitoring mechanism to regularly evaluate the implementation effect of emission reduction strategies and make adjustments based on the evaluation results.

[0007] In summary, the implementation of regional carbon emission reduction potential analysis methods based on power big data faces many challenges in data, technology, policy, knowledge integration, etc. It requires collaboration of interdisciplinary teams, continuous technological updates and policy support to overcome these challenges and achieve effective carbon emission reduction goals.

[0008] Therefore, it is an urgent problem for technical personnel in this field to propose a regional carbon emission reduction potential analysis method based on power big data to solve the difficulties existing in the existing technology. Summary of the invention

[0009] In view of this, the present invention provides a regional carbon emission reduction potential analysis method based on power big data, which can improve the accuracy of carbon emission calculation and the reliability of analysis.

[0010] In order to achieve the above object, the present invention adopts the following technical solution:

[0011] A method for analyzing regional carbon emission reduction potential based on power big data includes the following steps:

[0012] S1. Data acquisition step: acquiring power-related data within a preset time period of the area to be analyzed based on power big data, including: power energy consumption data, power energy production data and carbon emission data;

[0013] S2, data preprocessing step: constructing an energy consumption intensity learning curve based on the power-related data obtained in S1;

[0014] S3, optimization step: optimizing the parameters of the energy consumption intensity learning curve obtained in S2 by using a particle swarm optimization algorithm improved based on the sparrow algorithm;

[0015] S4. Based on the optimized energy consumption intensity learning curve obtained in S3, the carbon emission reduction potential of the target area is analyzed.

[0016] In the above method, specifically, the specific contents in S1 include:

[0017] S101. Based on the power big data, the user type is used as an indicator for classification, the total power energy consumption of the target area is obtained, and the power energy consumption data of different user types are obtained;

[0018] S102, based on the power big data, classify according to the power energy production source as an indicator to obtain power energy production data;

[0019] S103. Obtain electric energy carbon emission data based on the EDGAR database.

[0020] In the above method, specifically, the specific contents in S2 include:

[0021] S201, sorting the data obtained in S1: cleaning and sorting the collected data, including removing outliers, filling in missing data and unifying the data format;

[0022] S202, performing preliminary statistical analysis on the processed data, determining the relationship between energy consumption and economic output by calculating the correlation coefficient; and fitting the data by a mathematical model based on the characteristics and goodness of fit of the data;

[0023] S203, verifying the reliability and predictive ability of the model through residual analysis, goodness of fit test and cross validation, and conducting sensitivity analysis to evaluate the impact of model parameter changes on the energy intensity learning curve;

[0024] S204. Draw an EILC graph based on the estimated model parameters to show the changing trend of energy consumption corresponding to unit economic output with production scale or time.

[0025] In the above method, specifically, the specific contents in S3 include:

[0026] S301, setting particle swarm parameters and a convergence threshold of an objective function based on the mathematical model in S202;

[0027] S302, initialize the position and speed of the sparrow population, including the definition of the roles of the guard bird, the discoverer and the joiner, as well as the behavior patterns of the guard bird, the discoverer and the joiner, and define the SSA parameters:

[0028] S303, for each particle in the particle swarm, calculate the fitness of the corresponding EILC model; update the particle position, and according to the rules of the particle swarm optimization algorithm, update the position and speed of the particle to obtain better parameters;

[0029] S304, in each iteration, first use SSA to update the positions of some sparrows, then use PSO to update the positions of particles in the particle swarm, and exchange information between SSA and PSO, and repeat the iterative process until the preset number of iterations is reached or other termination conditions are met;

[0030] S305: Apply the optimal parameters obtained by the optimization algorithm to the EILC model, and update the mathematical expression of the model.

[0031] In the above method, specifically, the specific contents in S4 include:

[0032] Based on the EILC model with optimal parameters obtained in S305, analyze the characteristics of the EILC model, including slope, inflection point and long-term trend, and analyze the impact of technological progress, economies of scale and other factors on energy consumption based on these characteristics;

[0033] The optimized EILC model is used to predict future energy intensity trends under different scenarios; these scenarios include: different technology development paths, policy intervention measures and market changes.

[0034] The above method, specifically, formulates corresponding emission reduction strategies and measures based on the results of carbon emission reduction potential analysis, including: improving energy efficiency, developing renewable energy, implementing carbon capture and storage technology; and establishing a monitoring mechanism to track the progress of emission reduction.

[0035] It can be seen from the above technical solution that, compared with the prior art, the regional carbon emission reduction potential analysis method based on power big data of the present invention has the following beneficial effects:

[0036] Improve the accuracy and real-time nature of carbon emission monitoring: Power big data can provide real-time and accurate energy consumption data, which is a key input for calculating carbon emissions. Through power big data, carbon emissions can be monitored and analyzed more accurately, thus providing decision support for policymakers and enterprises;

[0037] Optimizing energy structure and promoting low-carbon transformation: By analyzing electricity consumption and production data, we can identify hot spots and inefficient links in energy consumption, thereby promoting the optimization of energy structure and the application of low-carbon technologies. This will help reduce the use of fossil fuels and increase the proportion of renewable energy, thereby achieving emission reduction targets;

[0038] Support policy making and market mechanism construction: Power big data can help the government better understand the dynamic changes of energy consumption and carbon emissions, and provide data support for the formulation of carbon emission trading policies, energy efficiency standards and other emission reduction incentives. In addition, the data can also be used to monitor the effectiveness of policy implementation and market reactions, so as to adjust strategies in a timely manner;

[0039] Enhance the carbon management capabilities of enterprises: Enterprises can use power big data to analyze their carbon footprint, identify emission reduction opportunities, and optimize production and operation processes. This will not only help enterprises reduce environmental impact, but also improve energy efficiency and reduce costs;

[0040] Promoting digital transformation and intelligent management: The analysis and application of power big data have promoted the digital transformation of energy management. Through intelligent tools and systems, refined management and predictive maintenance of energy consumption can be achieved, further improving energy efficiency and reducing emissions;

[0041] Support scientific governance and service modernization level: The application of power big data helps to improve the government's scientific governance capabilities. Through data-driven decision support systems, the government can more effectively serve social and economic development and environmental protection goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0043] Figure 1 A flow chart of a method for analyzing regional carbon emission reduction potential based on power big data provided by the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] In this application, relational terms such as first and second, etc. are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of more restrictions, the elements defined by the sentence "comprise one..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0046] Reference Figure 1 As shown, the present invention discloses a method for analyzing regional carbon emission reduction potential based on power big data, comprising the following steps:

[0047] S1. Data acquisition step: acquiring power-related data within a preset time period of the area to be analyzed based on power big data, including: power energy consumption data, power energy production data and carbon emission data;

[0048] S2, data preprocessing step: constructing an energy consumption intensity learning curve based on the power-related data obtained in S1;

[0049] S3, optimization step: optimizing the parameters of the energy consumption intensity learning curve obtained in S2 by using a particle swarm optimization algorithm improved based on the sparrow algorithm;

[0050] S4. Based on the optimized energy consumption intensity learning curve obtained in S3, the carbon emission reduction potential of the target area is analyzed.

[0051] Furthermore, the specific contents of S1 include:

[0052] S101. Based on the power big data, the user type is used as an indicator for classification, the total power energy consumption of the target area is obtained, and the power energy consumption data of different user types are obtained;

[0053] S102, based on the power big data, classify according to the power energy production source as an indicator to obtain power energy production data;

[0054] S103. Obtain electric energy carbon emission data based on the EDGAR database.

[0055] Specifically, S101 user type classification and data collection:

[0056] Define user types: First, clarify the classification standards for user types, such as industrial, commercial, residential, agricultural, etc. This will help to refine energy consumption data and provide a more accurate basis for subsequent analysis.

[0057] Data source connection: Establish cooperative relationships with power companies, energy management agencies, government statistics departments, etc. to ensure the real-time and accuracy of data.

[0058] Data collection process: Design the data collection process, including regular data requests, data format standardization, data transmission security, etc.

[0059] S102 Electricity and energy production data collection:

[0060] Energy production classification: Classification based on energy production sources (such as coal power, hydropower, wind power, solar power, nuclear power) helps to understand the energy structure of the region.

[0061] Production data collection: Establish data sharing agreements with energy producers to collect key indicator data such as energy output, energy consumption, and energy efficiency.

[0062] S103 Carbon emission data acquisition:

[0063] Data source selection: Give priority to authoritative carbon emission databases, such as the EDGAR database, to ensure the authority and reliability of the data.

[0064] Data matching and integration: Match carbon emission data with electricity energy consumption data to establish the correlation between the data for subsequent analysis.

[0065] Furthermore, the specific contents of S2 include:

[0066] S201, sorting the data obtained in S1: cleaning and sorting the collected data, including removing outliers, filling in missing data and unifying the data format;

[0067] S202, performing preliminary statistical analysis on the processed data, determining the relationship between energy consumption and economic output by calculating the correlation coefficient; and fitting the data by a mathematical model based on the characteristics and goodness of fit of the data;

[0068] S203, verifying the reliability and predictive ability of the model through residual analysis, goodness of fit test and cross validation, and conducting sensitivity analysis to evaluate the impact of model parameter changes on the energy intensity learning curve;

[0069] S204. Draw an EILC graph based on the estimated model parameters to show the changing trend of energy consumption corresponding to unit economic output with production scale or time.

[0070] Specifically, S201 data cleaning and organization:

[0071] Outlier detection: Use statistical methods (such as Z-score, box plot) to identify and handle outliers.

[0072] Missing value processing: Use interpolation, regression prediction, mean value filling and other methods to fill missing values.

[0073] Format standardization: Unify the data format to ensure that all data can be compared on the same scale.

[0074] S202 Statistical analysis and model selection:

[0075] Correlation analysis: Use Pearson or Spearman correlation coefficient test to determine the relationship between energy consumption and economic output.

[0076] Model selection: Based on the data characteristics, select the appropriate statistical model, such as linear regression, polynomial regression, time series analysis, etc.

[0077] S203 Model Validation and Sensitivity Analysis:

[0078] Goodness of fit test: Calculate the R of the model 2 Values ​​assess the model's ability to explain data variation.

[0079] Cross-validation: Use cross-validation techniques to evaluate the generalization ability of the model and ensure the prediction accuracy of the model on unknown data.

[0080] Sensitivity analysis: By changing model parameters, observing the changes in model output, and evaluating the stability of parameters.

[0081] Furthermore, the specific contents of S3 include:

[0082] S301, setting particle swarm parameters and a convergence threshold of an objective function based on the mathematical model in S202;

[0083] S302, initialize the position and speed of the sparrow population, including the definition of the roles of the guard bird, the discoverer and the joiner, as well as the behavior patterns of the guard bird, the discoverer and the joiner, and define the SSA parameters:

[0084] S303, for each particle in the particle swarm, calculate the fitness of the corresponding EILC model; update the particle position, and according to the rules of the particle swarm optimization algorithm, update the position and speed of the particle to obtain better parameters;

[0085] S304, in each iteration, first use SSA to update the positions of some sparrows, then use PSO to update the positions of particles in the particle swarm, and exchange information between SSA and PSO, and repeat the iterative process until the preset number of iterations is reached or other termination conditions are met;

[0086] S305: Apply the optimal parameters obtained by the optimization algorithm to the EILC model, and update the mathematical expression of the model.

[0087] Specifically, the S301 particle swarm parameter settings are:

[0088] Parameter definition: clarify the parameters in the particle swarm algorithm, such as inertia weight, learning factor, maximum number of iterations, etc.

[0089] Parameter adjustment: Through experiments, adjust the parameters to the optimal state to improve the convergence speed and optimization effect of the algorithm.

[0090] S302 Sparrow population initialization and behavior pattern definition:

[0091] Role definition: Clarify the roles and behavior patterns of watchdogs, discoverers, and joiners to simulate the search behavior of birds in nature.

[0092] Initialization strategy: Design a reasonable initialization strategy to ensure the diversity of the population and search efficiency.

[0093] S303 fitness calculation and particle position update:

[0094] Fitness function design: Define the fitness function to evaluate the quality of each particle.

[0095] Position update rule: According to the rules of the particle swarm optimization algorithm, the position and velocity of the particles are updated to find the global optimal solution.

[0096] S304 Iterative Optimization and Information Exchange:

[0097] Iteration process control: Monitor the iteration process to ensure that the algorithm converges within a reasonable time.

[0098] Information exchange mechanism: Establish an information exchange mechanism between SSA and PSO algorithms to utilize their respective advantages and improve the optimization effect.

[0099] Furthermore, the specific contents of S4 include:

[0100] Based on the EILC model with optimal parameters obtained in S305, analyze the characteristics of the EILC model, including slope, inflection point and long-term trend, and analyze the impact of technological progress, economies of scale and other factors on energy consumption based on these characteristics;

[0101] The optimized EILC model is used to predict future energy intensity trends under different scenarios; these scenarios include: different technology development paths, policy intervention measures and market changes.

[0102] Specifically, the S401 model features are analyzed:

[0103] Slope analysis: Analyze the slope change of energy consumption intensity as economic output increases to evaluate the potential for improving energy efficiency.

[0104] Inflection point identification: Identify the inflection points of the energy consumption intensity change trend and analyze the reasons for the inflection points, such as technological innovation, policy adjustments, etc.

[0105] Long-term trend forecast: Based on historical data, predict the changing trend of future energy consumption intensity and evaluate the potential space for carbon emission reduction.

[0106] S402 scenario forecast:

[0107] Technology development path: simulate the changes in energy consumption intensity under different technological progress scenarios and evaluate the contribution of technological innovation to carbon emission reduction.

[0108] Policy interventions: Assess the impact of policy interventions (such as carbon taxes, subsidies, emissions trading) on ​​energy intensity and carbon emissions.

[0109] Market changes: Analyze the impact of changes in market supply and demand on energy consumption patterns and predict changing trends in carbon emission reduction potential.

[0110] Emission reduction strategy formulation:

[0111] Energy efficiency improvement: Develop strategies to improve energy efficiency, such as promoting energy-saving technologies and equipment.

[0112] Renewable energy development: Plan the development and utilization of renewable energy to reduce dependence on fossil energy.

[0113] Carbon capture and storage technology: Research on carbon capture, utilization and storage technology (CCUS) to reduce direct emissions.

[0114] Establish monitoring mechanisms:

[0115] Continuous data collection: Establish a long-term data collection mechanism to ensure continuous monitoring of emission reduction effects.

[0116] Regular evaluation reports: Regularly publish emission reduction effect evaluation reports to display emission reduction results in an open and transparent manner.

[0117] Feedback and adjustment: Based on the monitoring results, regularly evaluate the effectiveness of emission reduction strategies and adjust the strategies in a timely manner to achieve the best emission reduction results.

[0118] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The system embodiment described above is only schematic, wherein the unit described as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative work.

[0119] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for analyzing regional carbon emission reduction potential based on power big data, characterized in that: The following steps are involved: S1. Data acquisition step: acquiring power-related data within a preset time period of the area to be analyzed based on power big data, including: power energy consumption data, power energy production data and carbon emission data; S2, data preprocessing step: constructing an energy consumption intensity learning curve based on the power-related data obtained in S1; S3, optimization step: optimizing the parameters of the energy consumption intensity learning curve obtained in S2 by using a particle swarm optimization algorithm improved based on the sparrow algorithm; S4. Based on the optimized energy consumption intensity learning curve obtained in S3, the carbon emission reduction potential of the target area is analyzed.

2. According to claim 1, a method for analyzing regional carbon emission reduction potential based on power big data is characterized in that: The specific contents of S1 include: S101. Based on the power big data, the user type is used as an indicator for classification, the total power energy consumption of the target area is obtained, and the power energy consumption data of different user types are obtained; S102, based on the power big data, classify according to the power energy production source as an indicator to obtain power energy production data; S103. Obtain electric energy carbon emission data based on the EDGAR database.

3. According to claim 2, a method for analyzing regional carbon emission reduction potential based on power big data is characterized in that: The specific contents of S2 include: S201, sorting the data obtained in S1: cleaning and sorting the collected data, including removing outliers, filling in missing data and unifying the data format; S202, performing preliminary statistical analysis on the processed data, determining the relationship between energy consumption and economic output by calculating the correlation coefficient; and fitting the data by a mathematical model based on the characteristics and goodness of fit of the data; S203, verifying the reliability and predictive ability of the model through residual analysis, goodness of fit test and cross validation, and conducting sensitivity analysis to evaluate the impact of model parameter changes on the energy intensity learning curve; S204. Draw an EILC graph based on the estimated model parameters to show the changing trend of energy consumption corresponding to unit economic output with production scale or time.

4. According to claim 3, a method for analyzing regional carbon emission reduction potential based on power big data is characterized in that: The specific contents of S3 include: S301, setting particle swarm parameters and a convergence threshold of an objective function based on the mathematical model in S202; S302, initialize the position and speed of the sparrow population, including the definition of the roles of the guard bird, the discoverer and the joiner, as well as the behavior patterns of the guard bird, the discoverer and the joiner, and define the SSA parameters: S303, for each particle in the particle swarm, calculate the fitness of the corresponding EILC model; update the particle position, and according to the rules of the particle swarm optimization algorithm, update the position and speed of the particle to obtain better parameters; S304, in each iteration, first use SSA to update the positions of some sparrows, then use PSO to update the positions of particles in the particle swarm, and exchange information between SSA and PSO, and repeat the iterative process until the preset number of iterations is reached or other termination conditions are met; S305: Apply the optimal parameters obtained by the optimization algorithm to the EILC model, and update the mathematical expression of the model.

5. According to claim 4, a method for analyzing regional carbon emission reduction potential based on power big data is characterized in that: The specific contents of S4 include: Based on the EILC model with optimal parameters obtained in S305, analyze the characteristics of the EILC model, including slope, inflection point and long-term trend, and analyze the impact of technological progress, economies of scale and other factors on energy consumption based on these characteristics; The optimized EILC model is used to predict future energy intensity trends under different scenarios; these scenarios include: different technology development paths, policy intervention measures and market changes.

6. According to claim 5, a method for analyzing regional carbon emission reduction potential based on power big data is characterized in that: Based on the results of the carbon emission reduction potential analysis, formulate corresponding emission reduction strategies and measures, including: improving energy efficiency, developing renewable energy, implementing carbon capture and storage technology; and establishing a monitoring mechanism to track the progress of emission reduction.

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