CN113538165A - Resident electricity consumption behavior perception analysis method serving for energy conservation and emission reduction of users - Google Patents
Resident electricity consumption behavior perception analysis method serving for energy conservation and emission reduction of users Download PDFInfo
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- CN113538165A CN113538165A CN202110590787.1A CN202110590787A CN113538165A CN 113538165 A CN113538165 A CN 113538165A CN 202110590787 A CN202110590787 A CN 202110590787A CN 113538165 A CN113538165 A CN 113538165A
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
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- G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
Abstract
The invention discloses a resident electricity consumption behavior perception analysis method serving for energy conservation and emission reduction of users, which comprises the following steps: step 1, constructing a residential electricity consumption behavior electricity consumption index; step 2, classifying the users based on the hourly power utilization rule, the power utilization scale, the power utilization condition of the week period, the holiday power utilization rule and the power utilization habit data of the users; and 3, comparing the non-invasive daily electric quantity data with the daily electric quantity of the marketing system in an error manner so as to test the accuracy of the level grade of the non-invasive daily electric quantity data. The invention can classify the electricity consumption habits of the users, thereby constructing the classified user portrait and analyzing the electricity consumption laws.
Description
Technical Field
The invention relates to a resident electricity consumption behavior perception analysis method for serving energy conservation and emission reduction of users in the field of electricity consumption data analysis.
Background
Conventionally, a traditional user electricity consumption behavior analysis method directly analyzes total electricity consumption of a user in different time periods (hours, peak and valley, daily electricity and monthly electricity), analyzes electricity consumption change trends, and summarizes electricity consumption rules of different users. Therefore, in the process of analyzing the characteristics of the electricity consumption behaviors of the residential users, the analysis concept of the electricity consumption behaviors is diluted continuously and is only limited on the change trend of the electric quantity in unit time, the analysis of the characteristics of the electricity consumption of the residential users on the use condition of various daily electric appliances of the residential users cannot be really realized, and the targeted service strategy support for energy conservation and emission reduction of the residential users is lacked.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a resident electricity consumption behavior perception analysis method serving for energy conservation and emission reduction of users, which can classify the electricity consumption habits of the users, thereby constructing a classified user figure and analyzing the electricity consumption rules.
One technical scheme for achieving the above purpose is as follows: a resident electricity consumption behavior perception analysis method serving for energy conservation and emission reduction of users comprises the following steps:
step 1, constructing a power utilization index of residential electricity consumption behaviors, collecting a non-invasive daily electricity quantity data level grade of residents and a time curve relation of the level grade within 24 hours and a week, and then dividing the data through Pearson correlation analysis;
step 2, dividing users into five categories, namely night cat type users, conventional office worker non-family users, family type users, old users and the like, based on the hourly power utilization rules, the power utilization scale, the power utilization condition of a week period, the holiday power utilization rules and the power utilization habit data of the users;
and 3, comparing the non-invasive daily electric quantity data with the daily electric quantity of the marketing system in an error mode, wherein the calculation formula of the daily electric quantity error rate is as follows: and the daily electricity error rate is (non-invasive daily electricity median-daily electricity median of the marketing system)/daily electricity median of the marketing system, and is used for checking the accuracy of the level grade of the non-invasive daily electricity data.
According to the resident electricity consumption behavior perception analysis method serving for energy conservation and emission reduction of the user, the electricity consumption portrait of the user can be obtained, the electricity consumption type of the user can be known, the electricity consumption habits of different types of users can be mined, the abnormal electricity consumption condition can be found, the information of high-energy-consumption electric appliances and high-energy-consumption habits can be timely notified to the user, the user can conveniently adjust the electricity consumption behavior in time, and energy conservation and emission reduction can be effectively achieved. Compared with the prior method, the analysis method has the following advantages:
1) by using the non-invasive load monitoring and analyzing result, the electric quantity and load conditions of various household electric appliances can be collected, and the electricity utilization dimension is refined to the last section;
2) various behavior analysis indexes and big data analysis methods are adopted, so that the analysis result is more accurate;
3) multiple dimensions such as time, place, electric appliance category, use duration, use frequency and the like are selected for analysis, and the electricity utilization behaviors of the user are observed in all directions, so that the analysis result is continuously close to the actual situation
Detailed Description
In order to better understand the technical solution of the present invention, the following detailed description is made by specific examples:
the invention discloses a resident electricity consumption behavior perception analysis method serving for energy conservation and emission reduction of a user, which is characterized by comprising the following steps of:
step 1, constructing a power utilization index of the electricity utilization behavior of residents, collecting the level grade of non-invasive daily electricity quantity data of the residents and the time curve relation of the level grade within 24 hours and a week, and then dividing the data through Pearson correlation analysis.
Firstly, creating a user electricity utilization characteristic analysis index, referring to and using the TGI index to reflect the strength of a target group in a specific research range, and dividing the electricity utilization level grade by using a big data analysis method, thereby being convenient for labeling the user. Meanwhile, the correlation among all indexes is analyzed by adopting a Pearson method, so that an index relation is defined, and a foundation is laid for analyzing the power utilization behavior characteristics of a user. The contents of the index method and the index principle are specifically adopted as follows:
(1) TGI index
The tgi (target Group index) index, is an index that reflects the strength or weakness of a target population within a particular study.
TGI index [ proportion of population having a certain characteristic in the target population/proportion of population having the same characteristic in the population ]. times.100.
(2) Electricity usage level rating
The electricity consumption is divided into three categories of high/medium/low by adopting various types such as clustering, box type diagrams and the like, and corresponding labels are marked.
(3) Pearson correlation
The correlation coefficient is the statistical indicator originally designed by the statistician karl pearson and is a measure of the degree of linear correlation between the study variables. The correlation coefficient is used as a statistical index for reflecting the degree of closeness of correlation between variables.
And 2, dividing users into five categories, namely night cat type users, conventional office worker (non-family) users, family type users, old users and the like, by combining algorithms such as clustering, curve similarity, factor analysis and the like based on dimensions such as hourly power utilization rules, power utilization scales, different date type power utilization conditions, holiday power utilization rules, power utilization habits, kitchen appliance use conditions, personal care appliance use conditions, clothes care appliance use conditions, novel appliance use conditions, television watching conditions, air temperature sensitivity and the like of the users. The algorithm specifically adopted comprises a clustering algorithm, a DTW similarity algorithm and an objective assignment algorithm.
And 3, comparing the non-invasive daily electric quantity data with the daily electric quantity of the marketing system in an error mode, wherein the calculation formula of the daily electric quantity error rate is as follows: and the daily electricity error rate is (non-invasive daily electricity median-daily electricity median of the marketing system)/daily electricity median of the marketing system, and is used for checking the accuracy of the level grade of the non-invasive daily electricity data.
Through the analysis operation, the user power consumption portrait can be finally acquired, the user power consumption types can be known, the power consumption habits of different types of users can be mined, the abnormal power consumption condition can be found out, the information of high-energy-consumption electric appliances and high-energy-consumption habits of the users can be timely informed, the users can conveniently and timely adjust the power consumption portrait, and the energy conservation and emission reduction can be effectively realized.
The analysis method is implemented in non-invasive load monitoring and installation test point analysis, and the observation of the analysis result shows that the effect is good and is matched with the actual situation. The technology accords with the design concept of accurate analysis, labels can be printed on current resident users for classification in an analysis result, electricity utilization habits of different types of users are distinguished, abnormal electricity utilization conditions can be rapidly identified through monitoring the electricity utilization conditions of daily electric appliances, a user is timely reminded to close the electric appliances in abnormal use, potential safety hazards of electricity utilization are prevented, and electricity utilization waste conditions are reduced; in addition, the comparison of the power utilization conditions of the electric appliances among the same type of users can better provide energy-saving electric appliance recommendation for the users under the condition that the users have the requirement of replacing the electric appliances, continuously and iteratively optimize the electric appliances of user groups, improve the energy consumption efficiency and greatly reduce the use of daily electric energy.
It should be understood by those skilled in the art that the above embodiments are only for illustrating the present invention and are not to be used as a limitation of the present invention, and that changes and modifications to the above described embodiments are within the scope of the claims of the present invention as long as they are within the spirit and scope of the present invention.
Claims (1)
1. A resident electricity consumption behavior perception analysis method serving for energy conservation and emission reduction of users is characterized by comprising the following steps:
step 1, constructing a power utilization index of residential electricity consumption behaviors, collecting a non-invasive daily electricity quantity data level grade of residents and a time curve relation of the level grade within 24 hours and a week, and then dividing the data through Pearson correlation analysis;
step 2, dividing users into five categories, namely night cat type users, conventional office worker non-family users, family type users, old users and the like, based on the hourly power utilization rules, the power utilization scale, the power utilization condition of a week period, the holiday power utilization rules and the power utilization habit data of the users;
and 3, comparing the non-invasive daily electric quantity data with the daily electric quantity of the marketing system in an error mode, wherein the calculation formula of the daily electric quantity error rate is as follows: and the daily electricity error rate is (non-invasive daily electricity median-daily electricity median of the marketing system)/daily electricity median of the marketing system, and is used for checking the accuracy of the level grade of the non-invasive daily electricity data.
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| CN108776939A (en) * | 2018-06-07 | 2018-11-09 | 上海电气分布式能源科技有限公司 | The analysis method and system of user power utilization behavior |
| CN110727662A (en) * | 2019-09-10 | 2020-01-24 | 国网浙江省电力有限公司电力科学研究院 | Low-voltage transformer area user phase identification method and system based on correlation analysis |
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| CN111461761A (en) * | 2020-02-29 | 2020-07-28 | 国网江苏省电力有限公司苏州供电分公司 | Resident user portrait method based on multi-dimensional fine-grained behavior data |
| CN111832861A (en) * | 2019-04-19 | 2020-10-27 | 广州供电局有限公司 | Resident load variable-scale portrait method and system based on big data platform |
| CN112307003A (en) * | 2020-11-02 | 2021-02-02 | 合肥优尔电子科技有限公司 | Power grid data multidimensional auxiliary analysis method, system, terminal and readable storage medium |
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2021
- 2021-05-28 CN CN202110590787.1A patent/CN113538165A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108776939A (en) * | 2018-06-07 | 2018-11-09 | 上海电气分布式能源科技有限公司 | The analysis method and system of user power utilization behavior |
| CN111832861A (en) * | 2019-04-19 | 2020-10-27 | 广州供电局有限公司 | Resident load variable-scale portrait method and system based on big data platform |
| CN110727662A (en) * | 2019-09-10 | 2020-01-24 | 国网浙江省电力有限公司电力科学研究院 | Low-voltage transformer area user phase identification method and system based on correlation analysis |
| CN110907884A (en) * | 2019-12-06 | 2020-03-24 | 国网天津市电力公司电力科学研究院 | Electric energy meter error diagnosis and analysis method based on non-invasive measurement |
| CN111461761A (en) * | 2020-02-29 | 2020-07-28 | 国网江苏省电力有限公司苏州供电分公司 | Resident user portrait method based on multi-dimensional fine-grained behavior data |
| CN112307003A (en) * | 2020-11-02 | 2021-02-02 | 合肥优尔电子科技有限公司 | Power grid data multidimensional auxiliary analysis method, system, terminal and readable storage medium |
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