Residential user electric quantity data processing method oriented to carbon profit

Abstract

The invention belongs to the technical field of data processing, and discloses a residential user electric quantity data processing method for carbon profit, which comprises the following steps: data collection: collecting original electric quantity data of residential users; data preprocessing: cleaning, denoising and formatting the collected original electric quantity data; data mining and analysis: analyzing the preprocessed electric quantity data, and summarizing a residential electricity consumption behavior rule and trend; carbon emission reduction calculation: calculating the carbon emission reduction of the resident user in the distributed photovoltaic power generation and low-carbon power consumption emission reduction scene according to a preset carbon profit methodology and the electric power data; carbon integral assignment: converting the calculated carbon emission reduction amount into a carbon integral, and recording the carbon integral into a corresponding resident user account; and carbon point application: providing a carbon point exchange service. The problem that dynamic adjustment cannot be performed according to actual conditions due to lack of flexibility and accuracy in a conversion model between the electric quantity data and the carbon emission in the prior art is solved.

Description

Technical Field

[0001] The present application belongs to the field of data processing technology, and specifically relates to a method for processing electricity consumption data of residential users for carbon inclusiveness. Background Art

[0002] As global climate change becomes increasingly severe, reducing carbon emissions and promoting green and low-carbon development has become a consensus of the international community. In this context, the carbon credit system, as an innovative low-carbon development mechanism, aims to encourage all sectors of society to actively participate in energy conservation and emission reduction through policy guidance and market incentives. As one of the important sources of carbon emissions, the electricity consumption behavior of residential users is directly related to carbon emissions. Therefore, it is particularly important to accurately process the electricity data of residential users, scientifically evaluate their carbon emissions, and then implement effective carbon credit incentive measures.

[0003] In the existing technology, the residential electricity data processing method mainly focuses on the statistics of electricity consumption and the calculation of electricity charges, while the support for the accurate calculation of carbon emissions and the subsequent carbon credit reward mechanism is relatively insufficient. This makes it difficult to accurately measure the low-carbon contribution of residential users during the implementation of the carbon credit system, and it is also impossible to effectively stimulate their enthusiasm for participating in low-carbon emission reduction.

[0004] In addition, the existing technology lacks flexibility and accuracy in the conversion model between electricity data and carbon emissions, and cannot be dynamically adjusted according to the actual conditions of different regions and different users. At the same time, the existing system also has certain problems in processing efficiency and security of residential user electricity data, which makes it difficult to meet the needs of large-scale promotion of the carbon credit system. Summary of the invention

[0005] The embodiment of the present application provides a method for processing electricity data of residential users for carbon inclusiveness, thereby solving the problem that the conversion model between electricity data and carbon emissions in the prior art lacks flexibility and accuracy, and further, cannot be dynamically adjusted according to actual conditions.

[0006] In order to achieve the above-mentioned purpose, an embodiment of the present invention provides a method for processing residential user electricity data for carbon inclusiveness, comprising the following steps:

[0007] Step 1: Data collection, collecting the original electricity data of residential users;

[0008] Step 2: Data preprocessing: cleaning, denoising and formatting the collected raw power data;

[0009] Step 3: Data mining and analysis: Analyze the pre-processed electricity data and summarize the patterns and trends of residents’ electricity consumption behavior;

[0010] Step 4: Calculate carbon emission reductions. Based on the preset carbon credit methodology and electricity data, calculate the carbon emission reductions of residential users in the scenarios of distributed photovoltaic power generation and low-carbon electricity consumption;

[0011] Step 5: Carbon credit assignment: convert the calculated carbon emission reduction into carbon credits and record them in the corresponding resident user accounts;

[0012] Step 6: Carbon credit application, providing carbon credit redemption service.

[0013] In a possible implementation, step one specifically includes the following steps:

[0014] Step a: Clarify the scope of data collection;

[0015] Step b: Build a data collection system that can automatically obtain data from smart meters of residential users, distributed photovoltaic power generation monitoring equipment and other related power monitoring equipment in real time or periodically;

[0016] Step c: Establish a data sharing mechanism with power companies or energy service providers;

[0017] Step d: Use the Internet of Things technology to remotely monitor and collect data on residential users’ electrical equipment;

[0018] Step e: Perform preliminary data cleaning and formatting on the collected raw power data;

[0019] Step f: Store the cleaned data in a secure database and take encryption measures to ensure data security.

[0020] In a possible implementation, the method further includes a step of performing quality assessment on the collected data, wherein the quality assessment step includes outlier detection, missing value processing, and data consistency check on the data.

[0021] In a possible implementation, step 2 specifically includes the following steps:

[0022] Step a: receiving and storing the original electricity consumption data of residential users obtained from the data collection system;

[0023] Step b: Clean the original electricity data to remove duplicate data, invalid data and data irrelevant to carbon credit calculation;

[0024] Step c: De-noising the cleaned data by using filtering algorithms or data smoothing techniques to eliminate or reduce noise and abnormal fluctuations in the data;

[0025] Step d: Format the denoised data and convert the data into a unified format and unit;

[0026] Step e: Verify the formatted data, check the completeness and accuracy of the data through the set data quality assessment standards, and further process or eliminate the data that does not meet the standards;

[0027] Step f: Output the verified data to the subsequent data processing and analysis stage.

[0028] In a possible implementation, a step of filling missing data is also included: according to the time series characteristics of the data or other relevant factors, an interpolation algorithm is used to estimate and fill the missing data.

[0029] In a possible implementation, step three specifically includes the following steps:

[0030] Step a: receiving pre-processed residential electricity consumption data, which has been cleaned, denoised and formatted;

[0031] Step b: Use a machine learning algorithm to train the preprocessed electricity data and build a residential electricity consumption behavior model that can reflect the time series characteristics, periodic laws, and correlation with external environmental factors of residential electricity consumption;

[0032] Step c: Use data mining technology to conduct in-depth analysis of the residential electricity consumption behavior model to find out the peak and valley hours of residential electricity consumption and the changing trend of electricity consumption;

[0033] Step d: Combine the distributed photovoltaic power generation and low-carbon electricity consumption data of residents to analyze the behavioral characteristics and potential trends of residents in terms of renewable energy utilization and low-carbon living;

[0034] Step e: Generate an analysis report to visualize the mined patterns and trends of residents’ electricity consumption behavior.

[0035] In a possible implementation, step 4 specifically includes the following steps:

[0036] Step a: Obtain electricity data of residential users in emission reduction scenarios such as distributed photovoltaic power generation and low-carbon electricity consumption;

[0037] Step b: Determine the carbon emission factors and emission reduction coefficients corresponding to various types of electricity data according to the preset carbon credit methodology;

[0038] Step c: Using the acquired power data and the corresponding carbon emission factors and emission reduction coefficients, calculate the actual carbon emissions and potential carbon emission reductions of residential users under each emission reduction scenario;

[0039] Step d: Summarize and count the calculated carbon emission reductions to generate a carbon emission reduction report for residential users;

[0040] Step e: Output the carbon emission reduction report to the carbon credit platform or related management system.

[0041] In a possible implementation, step five specifically includes the following steps:

[0042] Step a: receiving verified residential user carbon emission reduction data;

[0043] Step b: According to the preset carbon credit conversion rules, the carbon emission reduction data is converted into corresponding carbon credit values, and the number of carbon credits corresponding to the unit carbon emission reduction is clarified;

[0044] Step c: Establish or update the carbon credit account of the residential user, and record the converted carbon credit value into the corresponding account;

[0045] Step d: Provide a carbon credit query interface to allow residential users to query their carbon credit balance and credit details in real time;

[0046] Step e: Trigger the settlement and redemption process of carbon credits regularly or according to specific conditions. Residential users can use the accumulated carbon credits to redeem rewards or participate in related low-carbon activities.

[0047] In a possible implementation, the carbon credit application system of the present invention includes the following functional modules:

[0048] Module 1: Carbon credit management module, used to record, update and query the carbon credit balance and credit details of residential users;

[0049] Module 2: Commercial discount exchange module, which connects with the systems of commercial partners and allows residential users to use carbon credits to exchange for various commercial discounts;

[0050] Module 3: Public service exchange module, which connects with the system of public service providers and allows residents to use carbon credits to exchange public services;

[0051] Module 4: Carbon offset and trading module, which connects with the carbon trading market and the emission-controlled enterprises' systems, allowing residential users to use carbon credits for carbon offset or enter the carbon trading market to offset the carbon emission quotas of emission-controlled enterprises;

[0052] Module 5: User interaction interface, providing graphical operation interface and mobile application support.

[0053] In one possible implementation, the system also includes a data analysis and optimization module for collecting and analyzing the redemption behavior data of resident users, evaluating the operating effects of each functional module, and providing optimization suggestions to improve system performance and user experience.

[0054] In a possible implementation, a data quality assessment step is also included, that is, quality assessment is performed on the collected original power data and the pre-processed power data.

[0055] In a possible implementation, the data quality assessment step includes data integrity checking, data outlier detection, and data consistency verification.

[0056] One or more technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:

[0057] The embodiment of the present invention provides a method for processing electricity data of residential users for carbon credit, which has the following technical effects or advantages: Accurate carbon emission accounting: This patent adopts an advanced carbon emission accounting model, which can accurately calculate the carbon emissions of residential users according to the electricity consumption characteristics and carbon emission factors of different regions and different users. Flexible carbon credit assignment mechanism: Based on the calculated carbon emissions, this patent provides a set of dynamic carbon credit assignment schemes, which can flexibly adjust the carbon credit conversion rules according to policy orientation and market demand, so as to more effectively encourage residential users to participate in low-carbon emission reduction. Efficient electricity data processing: This patent optimizes the electricity data processing process, improves data processing efficiency and security, and can support real-time processing and analysis of large-scale user data. Comprehensive carbon credit application support: In addition to providing the function of exchanging carbon credits for commercial discounts and public services, this patent also supports the application of carbon credits in the carbon trading market, providing residential users with more diverse low-carbon life reward methods. Improve the accuracy of carbon emission reduction accounting: Through advanced accounting models, accurate measurement of carbon emissions of residential users is achieved, providing a scientific basis for the implementation of the carbon credit system. Enhance the flexibility of the carbon credit assignment mechanism: Dynamic carbon credit conversion rules can better adapt to policy changes and market demand, and improve the pertinence and effectiveness of the carbon credit reward mechanism. Improve the efficiency and security of electricity data processing: The optimized data processing process ensures the fast and accurate processing of large-scale data while ensuring the security of user data. Enrich the application scenarios of carbon credits: By supporting the application of carbon credits in multiple fields, it increases the enthusiasm and sense of gain of residential users in participating in low-carbon emission reduction, and promotes the widespread implementation of the carbon credit system. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present application, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0059] Figure 1A schematic flow chart of a method for processing residential electricity consumption data for carbon inclusiveness provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0060] 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 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.

[0061] In the description of the embodiments of the present invention, it should be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, rather than indicating or implying that the devices or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limitations on the present invention. The terms "first", "second", and "third" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance. In addition, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present invention can be understood according to the specific circumstances.

[0062] like Figure 1 As shown, the method for processing residential user electricity data for carbon inclusiveness provided by an embodiment of the present invention includes the following steps:

[0063] Step 1: Data collection: collect the original electricity data of residential users, including distributed photovoltaic power generation, low-carbon electricity consumption and other related electricity data;

[0064] Step 2: Data preprocessing: cleaning, denoising and formatting the collected raw power data to ensure data accuracy and consistency;

[0065] Step 3: Data mining and analysis: Use machine learning, data mining and other technologies to analyze the pre-processed electricity data to discover the patterns and trends of residents' electricity consumption behavior;

[0066] Step 4: Calculate carbon emission reductions. Based on the preset carbon credit methodology and electricity data, calculate the carbon emission reductions of residential users under emission reduction scenarios such as distributed photovoltaic power generation and low-carbon electricity consumption;

[0067] Step 5: Carbon credits are assigned. The calculated carbon emission reduction is converted into carbon credits according to certain rules and recorded in the corresponding resident user accounts;

[0068] Step 6: Carbon credit application, providing carbon credit exchange services, allowing residential users to use carbon credits to exchange for commercial discounts, exchange for public services, or conduct carbon offsets or enter the carbon trading market to offset the carbon emission quotas of controlled enterprises.

[0069] Step 1 specifically includes the following steps:

[0070] Step a: Clarify the scope of data collection, including distributed photovoltaic power generation, low-carbon electricity consumption and other data related to electricity consumption of residential users;

[0071] Step b: Build a data collection system that can automatically obtain data from smart meters of residential users, distributed photovoltaic power generation monitoring equipment and other related power monitoring equipment in real time or periodically;

[0072] Step c: Establish a data sharing mechanism with power companies or energy service providers to ensure that the original electricity data of residential users can be obtained safely and legally;

[0073] Step d: Use the Internet of Things technology to remotely monitor and collect data on residential users’ electrical equipment to ensure the real-time and accuracy of the data;

[0074] Step e: Perform preliminary data cleaning and formatting on the collected raw power data to meet the requirements of subsequent data processing and analysis;

[0075] Step f: Store the cleaned data in a secure database and take encryption measures to ensure data security.

[0076] The data collection system is compatible with various types of smart meters and power monitoring equipment to achieve automatic data collection and transmission. The data sharing mechanism complies with relevant data privacy protection laws and regulations to ensure that the personal information of residential users is not leaked.

[0077] The method also includes a step of quality assessment of the collected data to ensure the accuracy and completeness of the data. The quality assessment step includes outlier detection, missing value processing, and data consistency check. The method can be applied to the residential user electricity data processing process for carbon inclusiveness, providing an accurate data basis for subsequent carbon emission reduction calculations and carbon credit assignments.

[0078] Through the data collection method, the original electricity data of residential users can be collected comprehensively and accurately, including key information such as distributed photovoltaic power generation and low-carbon electricity consumption. These data will provide solid data support for subsequent carbon inclusive calculation and analysis, help promote the development of renewable energy and encourage residents to actively participate in low-carbon actions.

[0079] Step 2 specifically includes the following steps:

[0080] Step a: receiving and storing the original electricity data of residential users obtained from the data collection system, including distributed photovoltaic power generation, low-carbon electricity consumption and other related electricity data;

[0081] Step b: Clean the original electricity data to remove duplicate data, invalid data and data irrelevant to carbon credit calculation;

[0082] Step c: De-noising the cleaned data by using filtering algorithms or data smoothing techniques to eliminate or reduce noise and abnormal fluctuations in the data to improve the accuracy of the data;

[0083] Step d: Format the denoised data and convert the data into a unified format and unit to ensure data consistency and comparability;

[0084] Step e: Verify the formatted data, check the completeness and accuracy of the data through the set data quality assessment standards, and further process or eliminate the data that does not meet the standards;

[0085] Step f: Output the verified data to the subsequent data processing and analysis stage.

[0086] The cleaning process includes identifying and removing abnormal data caused by equipment failure, transmission error or human error. The denoising process uses an adaptive filtering algorithm, which can dynamically adjust the filtering parameters according to the characteristics of the actual data to achieve the best denoising effect. The method also includes a step of filling missing data, and an interpolation algorithm is used to estimate and fill missing data according to the time series characteristics of the data or other relevant factors. The interpolation algorithm includes but is not limited to linear interpolation, polynomial interpolation or interpolation methods based on machine learning. This method can be applied to residential electricity data processing systems for carbon inclusive finance, providing a high-quality data basis for subsequent carbon emission reduction calculations, carbon credit assignments and policy formulation.

[0087] Through the data preprocessing method described in this embodiment, the collected original electricity data of residential users can be processed comprehensively and carefully to ensure the accuracy and consistency of the data. This not only helps to improve the efficiency and accuracy of subsequent data processing and analysis, but also provides solid data support for the implementation of carbon credit policies and the incentives for residents' low-carbon behavior.

[0088] Step three specifically includes the following steps:

[0089] Step a: receiving pre-processed residential electricity consumption data, which has been cleaned, denoised and formatted to ensure data accuracy and consistency;

[0090] Step b: Use a machine learning algorithm to train the preprocessed electricity data and build a residential electricity consumption behavior model that can reflect the time series characteristics, periodic laws, and correlation with external environmental factors of residential electricity consumption;

[0091] Step c: Use data mining technology to conduct in-depth analysis of the residential electricity consumption behavior model to find out the peak and valley hours of residential electricity consumption and the changing trend of electricity consumption;

[0092] Step d: Combine the distributed photovoltaic power generation and low-carbon electricity consumption data of residents to analyze the behavioral characteristics and potential trends of residents in terms of renewable energy utilization and low-carbon living;

[0093] Step e: Generate an analysis report to visualize the patterns and trends of residents’ electricity consumption behaviors, and provide decision support and reference for power departments, policy makers, and residents.

[0094] The machine learning algorithms include but are not limited to support vector machines, random forests, neural networks, etc., which are used to build a high-precision model of residents' electricity consumption behavior. The data mining techniques include cluster analysis, association rule mining, time series analysis, etc., which are used to deeply explore the inherent laws and influencing factors of residents' electricity consumption behavior from multiple dimensions. The method also includes the step of verifying the analysis results, and evaluating the accuracy and reliability of the analysis results by comparing with the actual electricity consumption data. The verification step includes error analysis, sensitivity testing and robustness evaluation of the model prediction results. The method can be applied to smart grid systems, energy management systems or carbon inclusive platforms to provide data support and scientific basis for optimizing power resource allocation, formulating energy-saving and emission reduction policies, and enhancing residents' low-carbon awareness.

[0095] Through the data mining and analysis method described in this embodiment, the inherent laws and trends of residents' electricity consumption behavior can be deeply analyzed, providing strong support for the intelligent management of the power industry, energy conservation and emission reduction, and the implementation of carbon credit policies. At the same time, the method can also help residents better understand their electricity consumption habits and guide them to adopt a more energy-saving and low-carbon lifestyle.

[0096] Step 4 specifically includes the following steps:

[0097] Step a: Obtain the electricity data of residential users in emission reduction scenarios such as distributed photovoltaic power generation and low-carbon electricity consumption. The data has been pre-processed and data mined and analyzed to ensure its accuracy and effectiveness;

[0098] Step b: According to the preset carbon credit methodology, determine the carbon emission factors and emission reduction coefficients corresponding to various types of electricity data. These factors and coefficients reflect the impact of different electricity activities on carbon emissions;

[0099] Step c: Using the acquired power data and the corresponding carbon emission factors and emission reduction coefficients, calculate the actual carbon emissions and potential carbon emission reductions of residential users under each emission reduction scenario;

[0100] Step d: Summarize and count the calculated carbon emission reductions to generate a carbon emission reduction report for residential users, which lists in detail the carbon emission reduction results of the users under various emission reduction scenarios;

[0101] Step e: Export the carbon emission reduction report to the carbon credit platform or related management system to provide a basis for subsequent carbon credit assignment and policy rewards.

[0102] The preset carbon inclusive methodology includes national or regionally recognized carbon emission calculation standards and methods to ensure the compliance and authority of the calculation results. The power data includes, but is not limited to, distributed photovoltaic power generation, low-carbon electricity consumption, electricity consumption time periods, power equipment and other information, which is used to comprehensively reflect residents' electricity consumption behavior and emission reduction potential. The method also includes a step of verifying the calculation results to ensure the accuracy and reliability of the calculation results by comparing with other calculation methods or actual monitoring data. The verification step includes regular updating and calibration of the data, carbon emission factors and emission reduction coefficients used in the calculation process to adapt to changes in the power industry and carbon emission policies. This method can be applied to carbon account management, carbon credit trading or government carbon emission reduction subsidy policies for residential users, providing data support and policy basis for encouraging residents to participate in low-carbon life and promote the development of renewable energy.

[0103] Through the carbon emission reduction calculation method described in this embodiment, the carbon emission reduction results of residential users in emission reduction scenarios such as distributed photovoltaic power generation and low-carbon electricity consumption can be accurately quantified, providing a solid data foundation for the implementation of carbon preferential policies and the incentives for residents' low-carbon behavior. At the same time, this method also helps to improve residents' awareness and participation in low-carbon life, and promote the overall development of society in a greener and more sustainable direction.

[0104] Step 5 specifically includes the following steps:

[0105] Step a: Receive verified residential carbon emission reduction data and ensure the authenticity and validity of the data;

[0106] Step b: Convert the carbon emission reduction data into corresponding carbon credit values ​​according to the preset carbon credit conversion rules, which specify the number of carbon credits corresponding to a unit of carbon emission reduction;

[0107] Step c: Establish or update the carbon credit account of the resident user, record the converted carbon credit value into the corresponding account, and realize the accumulation and management of carbon credits;

[0108] Step d: Provide a carbon credit query interface to allow residential users to query their carbon credit balance and credit details in real time, thereby enhancing users’ trust in and participation in the carbon credit system;

[0109] Step e: Trigger the settlement and redemption process of carbon credits regularly or according to specific conditions. Residential users can use the accumulated carbon credits to redeem rewards or participate in related low-carbon activities.

[0110] The preset carbon credit conversion rules can be dynamically adjusted according to policy orientation, market demand or user behavior to maintain the flexibility and incentive effect of the carbon credit system. The carbon credit account is associated with the identity information of the resident user to ensure the uniqueness and security of the account. The method also includes a monitoring and analysis step of carbon credit usage, and evaluates the operating effect and user participation of the carbon credit system by collecting and analyzing the carbon credit exchange data of resident users. The monitoring and analysis steps include detection and processing of abnormal exchange behaviors to prevent abuse and fraud of the carbon credit system. The method can be applied to smart grid systems, carbon inclusive platforms or government carbon emission reduction management systems to provide an effective credit incentive mechanism for encouraging residents to participate in low-carbon life and promote the development of renewable energy.

[0111] Through the carbon credit assignment method described in this embodiment, the carbon emission reduction results of residential users can be quantified into specific carbon credit values ​​and recorded in the corresponding accounts. This not only provides a visual display of low-carbon life results for residential users, but also stimulates the enthusiasm and sustainability of users to participate in low-carbon activities through the credit incentive mechanism. At the same time, the method also provides an effective carbon emission reduction management method for governments and enterprises, which helps promote the green and low-carbon transformation of the whole society.

[0112] The carbon credit application system of the present invention includes the following functional modules:

[0113] Module 1: Carbon credit management module, used to record, update and query the carbon credit balance and credit details of residential users, to ensure the accuracy and real-time nature of carbon credit data;

[0114] Module 2: Commercial discount exchange module, which connects with the systems of commercial partners and allows residential users to use carbon credits to exchange for various commercial discounts, including but not limited to commodity discounts, service discounts, membership benefits, etc.;

[0115] Module 3: Public Service Exchange Module, which connects with the system of public service providers and allows residents to use carbon credits to exchange for public services, including but not limited to public transportation tickets, park tickets, cultural activities participation qualifications, etc.;

[0116] Module 4: Carbon offset and trading module, which connects with the carbon trading market and the emission-controlled enterprises' systems, allowing residential users to use carbon credits for carbon offset or enter the carbon trading market to offset the carbon emission quotas of emission-controlled enterprises;

[0117] Module 5: User interaction interface, which provides a graphical operation interface and mobile application support to facilitate residents to check carbon credit balances, browse redeemable items, initiate redemption requests and manage personal accounts.

[0118] The redeemable items in the commercial preferential exchange module and the public service exchange module are dynamically updated according to market demand and user preferences to maintain the attractiveness of the system and user participation. The carbon offset and trading module includes a conversion mechanism for carbon credits and carbon emission quotas to ensure that the carbon credits of resident users can be effectively circulated and offset in the carbon trading market. The system also includes a data analysis and optimization module for collecting and analyzing the redemption behavior data of resident users, evaluating the operating effects of each functional module, and providing optimization suggestions to improve system performance and user experience. The data analysis and optimization module also includes a risk monitoring mechanism for identifying and preventing potential fraud, market manipulation and other risks to ensure the robust operation of carbon credit exchange services. The system can be integrated with other low-carbon life service platforms, smart city management systems, etc. to jointly build a comprehensive green and low-carbon life ecosystem.

[0119] Through the carbon credit exchange service system described in this embodiment, residential users can make full use of their accumulated carbon credits to exchange for a variety of commercial discounts and public services, and achieve substantial returns from low-carbon living. At the same time, the system also provides a new carbon offset channel for carbon trading markets and emission-controlled enterprises, which helps promote the prosperity of the carbon market and the realization of carbon reduction targets. Overall, the program promotes the green and low-carbon transformation of the whole society and enhances the public's awareness and participation in low-carbon living.

[0120] The carbon credit methodology in step four includes but is not limited to distributed photovoltaic power generation methodology and residential low-carbon electricity consumption methodology, and the methodology can be updated and adjusted according to actual conditions.

[0121] The carbon credit assignment rules in step five can be dynamically adjusted according to policy orientation, market demand and the actual emission reduction effects of residential users.

[0122] It also includes a data quality assessment step, which is to perform a quality assessment on the collected raw power data and the pre-processed power data to ensure the credibility and availability of the data.

[0123] The data quality assessment step includes multiple links such as data integrity check, data outlier detection and data consistency verification.

[0124] This method can be applied in many fields, such as assisting the government in formulating carbon preferential policies, promoting the development of renewable energy, and motivating residents to actively participate in low-carbon actions.

[0125] This patent brings the following beneficial effects: Accurate carbon emission accounting: This patent adopts an advanced carbon emission accounting model, which can accurately calculate the carbon emissions of residential users according to the electricity consumption characteristics and carbon emission factors of different regions and different users. Flexible carbon credit assignment mechanism: Based on the calculated carbon emissions, this patent provides a set of dynamic carbon credit assignment schemes, which can flexibly adjust the carbon credit conversion rules according to policy orientation and market demand, so as to more effectively encourage residential users to participate in low-carbon emission reduction. Efficient power data processing: This patent optimizes the power data processing process, improves data processing efficiency and security, and can support real-time processing and analysis of large-scale user data. Comprehensive carbon credit application support: In addition to providing the function of carbon credit exchange for commercial discounts and public services, this patent also supports the application of carbon credits in the carbon trading market, providing residential users with more diverse low-carbon life rewards. Improve the accuracy of carbon emission reduction accounting: Through advanced accounting models, accurate measurement of residential users' carbon emissions is achieved, providing a scientific basis for the implementation of the carbon credit system. Enhance the flexibility of the carbon credit assignment mechanism: Dynamic carbon credit conversion rules can better adapt to policy changes and market demand, and improve the pertinence and effectiveness of the carbon credit reward mechanism. Improve the efficiency and security of electricity data processing: The optimized data processing process ensures the fast and accurate processing of large-scale data while ensuring the security of user data. Enrich the application scenarios of carbon credits: By supporting the application of carbon credits in multiple fields, it increases the enthusiasm and sense of gain of residential users in participating in low-carbon emission reduction, and promotes the widespread implementation of the carbon credit system.

[0126] In this embodiment, it is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the present invention is defined by the appended claims rather than the above description, and therefore it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention.

Claims

1. A method for processing residential electricity data for carbon inclusiveness, characterized by: The following steps are involved: Step 1: Data collection, collecting the original electricity data of residential users; Step 2: Data preprocessing: cleaning, denoising and formatting the collected raw power data; Step 3: Data mining and analysis: Analyze the pre-processed electricity data and summarize the patterns and trends of residents’ electricity consumption behavior; Step 4: Calculate carbon emission reductions. Based on the preset carbon credit methodology and electricity data, calculate the carbon emission reductions of residential users in the scenarios of distributed photovoltaic power generation and low-carbon electricity consumption; Step 5: Carbon credit assignment: convert the calculated carbon emission reduction into carbon credits and record them in the corresponding resident user accounts; Step 6: Carbon credit application, providing carbon credit redemption service.

2. A method for processing residential electricity data for carbon inclusiveness according to claim 1, characterized in that: Step 1 specifically includes the following steps: Step a: Clarify the scope of data collection; Step b: Build a data collection system that can automatically obtain data from smart meters of residential users, distributed photovoltaic power generation monitoring equipment and other related power monitoring equipment in real time or periodically; Step c: Establish a data sharing mechanism with power companies or energy service providers; Step d: Use the Internet of Things technology to remotely monitor and collect data on residential users’ electrical equipment; Step e: Perform preliminary data cleaning and formatting on the collected raw power data; Step f: Store the cleaned data in a secure database and take encryption measures to ensure data security.

3. A method for processing residential electricity data for carbon inclusiveness according to claim 2, characterized in that: It also includes a step of quality assessment of the collected data, which includes outlier detection, missing value processing and data consistency check.

4. The method for processing residential electricity data for carbon inclusiveness according to claim 1 is characterized in that: Step 2 specifically includes the following steps: Step a: receiving and storing the original electricity consumption data of residential users obtained from the data collection system; Step b: Clean the original electricity data to remove duplicate data, invalid data and data irrelevant to carbon credit calculation; Step c: De-noising the cleaned data by using filtering algorithms or data smoothing techniques to eliminate or reduce noise and abnormal fluctuations in the data; Step d: Format the denoised data and convert the data into a unified format and unit; Step e: Verify the formatted data, check the completeness and accuracy of the data through the set data quality assessment standards, and further process or eliminate the data that does not meet the standards; Step f: Output the verified data to the subsequent data processing and analysis stage.

5. A method for processing residential electricity data for carbon inclusiveness according to claim 4, characterized in that: It also includes the step of filling in missing data: according to the time series characteristics of the data or other relevant factors, interpolation algorithms are used to estimate and fill in missing data.

6. The method for processing residential electricity data for carbon inclusiveness according to claim 1, characterized in that: Step three specifically includes the following steps: Step a: receiving pre-processed residential electricity consumption data, which has been cleaned, denoised and formatted; Step b: Use a machine learning algorithm to train the preprocessed electricity data and build a residential electricity consumption behavior model that can reflect the time series characteristics, periodic laws, and correlation with external environmental factors of residential electricity consumption; Step c: Use data mining technology to conduct in-depth analysis of the residential electricity consumption behavior model to find out the peak and valley hours of residential electricity consumption and the changing trend of electricity consumption; Step d: Combine the distributed photovoltaic power generation and low-carbon electricity consumption data of residents to analyze the behavioral characteristics and potential trends of residents in terms of renewable energy utilization and low-carbon living; Step e: Generate an analysis report to visualize the mined patterns and trends of residents’ electricity consumption behavior.

7. A method for processing residential electricity data for carbon inclusiveness according to claim 1, characterized in that: Step 4 specifically includes the following steps: Step a: Obtain electricity data of residential users in emission reduction scenarios such as distributed photovoltaic power generation and low-carbon electricity consumption; Step b: Determine the carbon emission factors and emission reduction coefficients corresponding to various types of electricity data according to the preset carbon credit methodology; Step c: Using the acquired power data and the corresponding carbon emission factors and emission reduction coefficients, calculate the actual carbon emissions and potential carbon emission reductions of residential users under each emission reduction scenario; Step d: Summarize and count the calculated carbon emission reductions to generate a carbon emission reduction report for residential users; Step e: Output the carbon emission reduction report to the carbon credit platform or related management system.

8. The method for processing residential electricity data for carbon inclusiveness according to claim 1, characterized in that: Step 5 specifically includes the following steps: Step a: receiving verified residential user carbon emission reduction data; Step b: According to the preset carbon credit conversion rules, the carbon emission reduction data is converted into corresponding carbon credit values, and the number of carbon credits corresponding to the unit carbon emission reduction is clarified; Step c: Establish or update the carbon credit account of the residential user, and record the converted carbon credit value into the corresponding account; Step d: Provide a carbon credit query interface to allow residential users to query their carbon credit balance and credit details in real time; Step e: Trigger the settlement and redemption process of carbon credits regularly or according to specific conditions. Residential users can use the accumulated carbon credits to redeem rewards or participate in related low-carbon activities.

9. The method for processing residential electricity data for carbon inclusiveness according to claim 1, characterized in that: The carbon credit application system of the present invention includes the following functional modules: Module 1: Carbon credit management module, used to record, update and query the carbon credit balance and credit details of residential users; Module 2: Commercial discount exchange module, which connects with the systems of commercial partners and allows residential users to use carbon credits to exchange for various commercial discounts; Module 3: Public service exchange module, which connects with the system of public service providers and allows residents to use carbon credits to exchange public services; Module 4: Carbon offset and trading module, which connects with the carbon trading market and the emission-controlled enterprises' systems, allowing residential users to use carbon credits for carbon offset or enter the carbon trading market to offset the carbon emission quotas of emission-controlled enterprises; Module 5: User interaction interface, providing graphical operation interface and mobile application support.

10. A method for processing residential electricity data for carbon inclusiveness according to claim 9, characterized in that: The system also includes a data analysis and optimization module for collecting and analyzing the redemption behavior data of resident users, evaluating the operating effects of each functional module, and providing optimization suggestions to improve system performance and user experience; It also includes a data quality assessment step, that is, performing quality assessment on the collected raw power data and the pre-processed power data; The data quality assessment step includes data integrity check, data outlier detection and data consistency verification.

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