CN110930051A - Big data analysis-based resident demand response potential analysis system and method - Google Patents
Big data analysis-based resident demand response potential analysis system and method Download PDFInfo
- Publication number
- CN110930051A CN110930051A CN201911214069.3A CN201911214069A CN110930051A CN 110930051 A CN110930051 A CN 110930051A CN 201911214069 A CN201911214069 A CN 201911214069A CN 110930051 A CN110930051 A CN 110930051A
- Authority
- CN
- China
- Prior art keywords
- data
- analysis
- regional
- acquisition system
- user
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000007405 data analysis Methods 0.000 title claims abstract description 36
- 238000004458 analytical method Methods 0.000 title claims abstract description 31
- 230000004044 response Effects 0.000 title claims abstract description 22
- 238000000034 method Methods 0.000 title claims abstract description 10
- 230000005540 biological transmission Effects 0.000 claims abstract description 28
- 238000012545 processing Methods 0.000 claims abstract description 25
- 238000013528 artificial neural network Methods 0.000 claims abstract description 17
- 230000005611 electricity Effects 0.000 claims abstract description 15
- 239000000284 extract Substances 0.000 claims abstract description 8
- 238000012544 monitoring process Methods 0.000 claims description 11
- 238000013075 data extraction Methods 0.000 claims description 8
- 238000002372 labelling Methods 0.000 claims description 4
- 238000012795 verification Methods 0.000 claims description 3
- 238000012360 testing method Methods 0.000 abstract description 7
- 238000007726 management method Methods 0.000 abstract description 3
- 230000006399 behavior Effects 0.000 description 6
- 238000012549 training Methods 0.000 description 6
- 239000011159 matrix material Substances 0.000 description 4
- 230000004913 activation Effects 0.000 description 3
- 238000013480 data collection Methods 0.000 description 3
- 238000000354 decomposition reaction Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 210000002569 neuron Anatomy 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 230000003014 reinforcing effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Images
Classifications
-
- 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
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2477—Temporal data queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/211—Selection of the most significant subset of features
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/602—Providing cryptographic facilities or services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/082—Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
-
- 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
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Mathematical Physics (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Probability & Statistics with Applications (AREA)
- Molecular Biology (AREA)
- Biomedical Technology (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Entrepreneurship & Innovation (AREA)
- Databases & Information Systems (AREA)
- Computing Systems (AREA)
- Marketing (AREA)
- Fuzzy Systems (AREA)
- Biophysics (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- Bioethics (AREA)
- Computer Hardware Design (AREA)
- Computer Security & Cryptography (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
Abstract
The invention discloses a resident demand response potential analysis system and method based on big data analysis. The regional data acquisition system extracts power utilization data of the household electric meters of the users in the region and transmits the selected data to a next-stage data encryption transmission station; modeling by using a neural network system ELM in the data analysis processing station to obtain extracted data records; after multiple tests, the resident demand response potential can be analyzed, the analysis on the electricity utilization behavior of the user is completed, the refined management can be helped to be developed, the power supply company is helped to carry out peak staggering scheduling in the electricity utilization peak period, auxiliary decision information is provided for power grid planning, the time required by analysis is shortened, and the overall efficiency is improved.
Description
Technical Field
The invention relates to the technical field of electric power system analysis, in particular to a resident demand response potential analysis system and method based on big data analysis.
Background
With the development of economy in China, the electric energy consumption is increased rapidly, the peak-valley difference of a power grid is increased gradually, the load peak frequency is increased rapidly, and the problems of light abandonment, wind abandonment and the like caused by the randomness and the intermittent characteristics of new energy power generation bring challenges to the scheduling work of the power grid.
And at the resident user side, because the improvement of living standard, the reinforcing of environmental protection consciousness, a large amount of intelligent flexible equipment and novel energy storage equipment are popularized and used at resident family. In summer peak time, the air conditioner load ratio of a center city at the front line exceeds 40%, if the peak-off scheduling of the electricity peak time cannot be carried out according to the demands of residents, the normal electricity utilization demand of the whole city can be influenced, the electricity consumption capacity and the load regulation and control capacity of the residents are obviously improved, and the research on emerging demand response services at the power grid side and the resident user side has important significance.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a resident demand response potential analysis system and method based on big data analysis, which helps a power supply company to carry out peak-to-peak scheduling in a power utilization peak period, reduces the time required by analysis and improves the overall power utilization efficiency of a power grid.
In order to achieve the purpose, the invention adopts the following technical scheme:
a resident demand response potential analysis system based on big data analysis comprises a regional data acquisition system and is characterized in that the input end of the regional data acquisition system is connected with the output end of a user ammeter, the output end of the regional data acquisition system is connected with the input end of a data analysis processing station through a data encryption transmission station, a neural network system ELM is arranged in the data analysis processing station, and the outer side of the data analysis processing station is respectively connected with a control end and a monitoring end through transmission lines;
the regional data acquisition system is internally provided with a data extraction module, a data transmission module, a data analysis module and a data marking module, and is used for extracting user data within set time, marking according to the extracted user data and facilitating data extraction and comparison again;
the regional data acquisition system and the data encryption transmission station are arranged according to the number of users in a region and the area of the region, and a data encryption module is arranged in the data encryption transmission station;
the neural network system ELM can be used for classifying and fitting data and carrying out classification labeling on the extracted data;
the control end is provided with a password verification end and is used for controlling the whole system to work;
and the monitoring end is used for checking the working state and feedback information of the system.
A resident demand response potential analysis method based on big data analysis is characterized by comprising the following analysis steps:
1) the regional data acquisition system extracts power utilization data of user household electric meters in a region, selects an optimal feature set from the data features according to the power utilization requirements of users, and transmits the selected data to a next-stage data encryption transmission station;
2) the data encryption transmission station receives data transmitted by a regional data acquisition system in a region, classifies the data, and uniformly encrypts and transmits the data to the data analysis processing station;
3) modeling is carried out in the data analysis processing station by using a neural network system ELM, so that the characteristics of the electricity load curve of the residential user are extracted, the ELM network is trained, the load curve is classified, and the extracted data record is obtained;
4) and (3) the staff repeats the steps 1) to 3) through the control end control system, extracts and records at different time points according to the requirements, and checks the resident demand response potential by the monitoring end after comparing and analyzing the time data.
According to the invention, a neural network system ELM is adopted for modeling, the extracted feature set is analyzed and processed, different feature sets are extracted at different time points, input parameters are changed, the influence of different parameters on the performance of classification results is compared, the resident demand response potential can be obtained through analysis after multiple tests, the analysis on the power consumption behavior of a user is completed, the analysis result of the user behavior can help to develop refined management, a power supply company is helped to carry out peak-to-peak scheduling in the power consumption peak period, and auxiliary decision information is provided for power grid planning; the regional data acquisition system is adopted to extract different user data, the optimal feature set can be selected from the data features according to the power consumption requirements of users, meanwhile, the selected user marks the selected user, data extraction and comparison are convenient to carry out again, and the data encryption transmission station can transmit the data acquired by the regional data acquisition system in the region to the data analysis processing station for analysis and processing after unified integration and encryption, so that the time required by analysis is shortened, and the overall efficiency is improved.
Drawings
FIG. 1 is a schematic diagram of a resident demand response potential analysis system based on big data analysis according to the present invention;
FIG. 2 is a flow chart of the analysis steps of the present invention;
in the figure: 1. the system comprises a regional data acquisition system, 2 a user ammeter, 3 a data encryption transmission station, 4 a data analysis processing station, 5 a control end and 6 a monitoring end.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Referring to fig. 1 and 2, a resident demand response potential analysis system based on big data analysis comprises a regional data acquisition system 1, wherein the input end of the regional data acquisition system 1 is connected with the output end of a user ammeter 2, the output end of the regional data acquisition system 1 is connected with the input end of a data analysis processing station 4 through a data encryption transmission station 3, a neural network system ELM is arranged in the data analysis processing station 4, and the outer side of the data analysis processing station 4 is respectively connected with a control end 5 and a monitoring end 6 through transmission lines; the regional data acquisition system 1 is internally provided with a data extraction module, a data transmission module, a data analysis module and a data marking module, and the regional data acquisition system 1 is used for extracting user data within a set time, marking according to the extracted user data, and performing data extraction comparison again.
The number of the regional data acquisition system 1 and the number of the data encryption transmission station 3 are both set to be multiple and are set according to the number of users in a region and the area of the region, and a data encryption module is arranged inside the data encryption transmission station 3. The control end 5 adopts a PLC controller and is provided with a password verification end, the control end 5 is used for controlling the work permission of the whole system, and the monitoring end 6 is used for checking the work state and feedback information of the system.
When the regional data collection system is used, the regional data collection system 1 is adopted to begin 101 to extract different user electricity consumption data collection 102, the optimal characteristic set 103 can be selected according to the electricity consumption requirements of users according to data characteristics, meanwhile, the selected user marks the selected user, data extraction and comparison are facilitated again, the data encryption transmission station 3 normalizes characteristic values in different characteristics 104, collected data are uniformly integrated and encrypted and then are transmitted to the data analysis processing station 4 for analysis processing, the time required by analysis can be reduced, and the overall efficiency is improved.
A resident demand response potential analysis method based on big data analysis comprises the following analysis steps:
1) the regional data acquisition system 1 extracts power consumption data of household user electric meters 2 in a region, selects an optimal feature set from data features according to the power consumption requirements of users, and transmits the selected data to a next-stage data encryption transmission station 3;
2) the data encryption transmission station 3 receives data transmitted by a regional data acquisition system in a region, classifies the data, and uniformly encrypts and transmits the data to the data analysis processing station 4;
3) modeling is carried out in the data analysis processing station 4 by using a neural network system ELM, and the neural network system ELM can be used for classifying and fitting data and carrying out classification labeling on the extracted data;
the output function in the processing station 4 is expressed as follows:
wherein x is the input quantity of the neural network, i.e. the output quantity of the transmission station 3, f (-) is the activation function of the neural network, w is the weight between the input layer and the hidden layer, b is the node threshold of the hidden layer, β is the output quantity of the hidden layer, i.e. the connection weight between the hidden layer and the output layer, l is the number of the neurons, and y is the output quantity of the neural network, i.e. the output quantity of the processing station 4.
Specifically, a function f (-) meeting infinitesimal conditions is selected as an activation function of the hidden layer node, and a weight w between the input layer and the hidden layer and a threshold b of the hidden layer node are randomly set; determining the number of nodes of the hidden layer to obtain an output matrix H of the hidden layer; calculating the Moor Penrose generalized inverse of the hidden layer output matrix H by using a singular value decomposition method, and then calculating the output layer weight y to complete network training; the trained network is used for realizing classification of the electricity utilization behaviors of the users, if the output result of the ELM network is the same as the original label of the group of data, classification is considered to be correct, then the accuracy of classification of the training set and the test set is respectively calculated, the influence of different parameters on the performance of the classification result is compared by changing the input parameters of the algorithm, the resident demand response potential can be obtained through analysis after multiple tests, the feature extraction of the electricity utilization load curve of the resident users, the ELM network training and the classification of the load curve are realized, and the extracted data record is obtained.
4) The staff repeats the steps 1) to 3) through the control end 5 control system, extracts and records at different time points according to the requirements, compares and analyzes the time data, and then checks the resident demand response potential through the monitoring end 6.
Example (b): the neural network system ELM can be used for classifying and fitting data and carrying out classification labeling on extracted data.
Selecting a function f (-) meeting infinite and differentiable as an activation function of the hidden layer node, and randomly setting a weight w between an input layer and the hidden layer and a threshold b of the hidden layer node;
determining the number of nodes of the hidden layer, and combining the output quantity of the hidden layer to obtain a hidden layer output matrix H;
calculating the Moor Penrose generalized inverse of the output matrix H by using a singular value decomposition method, and then calculating the weight of an output layer to complete network training;
the trained network is used for realizing classification of the electricity utilization behaviors of the user, if the output result of the ELM network is the same as the original label of the group of data, the classification is considered to be correct, then the accuracy of classification of the training set and the test set is calculated respectively, the influence of different parameters on the performance of the classification result is compared by changing the input parameters of the algorithm, and the resident demand response potential can be obtained through analysis after multiple tests.
The specific analysis method steps of the system are shown in FIG. 2:
1. the method comprises the following steps that firstly 101, a regional data acquisition system 1 is adopted to extract electricity consumption data 102 of household electric meters of different users in a region, an optimal feature set 103 is selected from data features according to electricity consumption requirements of different users, and the selected data are transmitted to a next-stage data encryption transmission station 3;
2. the data encryption transmission station 3 receives data transmitted by the data acquisition systems 1 in a plurality of areas in the area, classifies different data and then uniformly encrypts the data, normalizes characteristic values in different characteristic sets 104, and transmits the characteristic values to the data analysis processing station 4;
3. modeling 105 by using a neural network system ELM in the data analysis processing station 4, realizing feature extraction of the electricity load curve of the residential user, ELM network training and load curve classification, and recording according to the extracted data;
4. the staff repeats the steps 1) to 3) through the PLC controller control system of the control end 5), and can extract records at different time points according to the demands, and after comparing and analyzing a plurality of time data 106, the monitoring end 6 can check the resident demand response potential, and the system analysis is finished 107. The invention adopts a neural network system ELM to carry out modeling, analyzes and processes the extracted feature set, changes input parameters by extracting different feature sets at different time points, compares the influence of different parameters on the performance of classification results, can analyze the resident demand response potential after a plurality of tests, completes the analysis of the power utilization behavior of a user, can help to develop refined management by the user behavior analysis result, can help a power supply company to carry out peak-to-peak scheduling in the power utilization peak period, and can provide auxiliary decision information for power grid planning for a long time.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (2)
1. A resident demand response potential analysis system based on big data analysis comprises a regional data acquisition system and is characterized in that the input end of the regional data acquisition system is connected with the output end of a user ammeter, the output end of the regional data acquisition system is connected with the input end of a data analysis processing station through a data encryption transmission station, a neural network system ELM is arranged in the data analysis processing station, and the outer side of the data analysis processing station is respectively connected with a control end and a monitoring end through transmission lines;
the regional data acquisition system is internally provided with a data extraction module, a data transmission module, a data analysis module and a data marking module, and is used for extracting user data within set time, marking according to the extracted user data and facilitating data extraction and comparison again;
the regional data acquisition system and the data encryption transmission station are arranged according to the number of users in a region and the area of the region, and a data encryption module is arranged in the data encryption transmission station;
the neural network system ELM can be used for classifying and fitting data and carrying out classification labeling on the extracted data;
the control end is provided with a password verification end and is used for controlling the whole system to work;
and the monitoring end is used for checking the working state and feedback information of the system.
2. The method for analyzing the response potential of the demands of the residents based on the big data analysis as claimed in claim 1, wherein the analyzing steps are as follows:
1) the regional data acquisition system extracts power utilization data of user household electric meters in a region, selects an optimal feature set from the data features according to the power utilization requirements of users, and transmits the selected data to a next-stage data encryption transmission station;
2) the data encryption transmission station receives data transmitted by a regional data acquisition system in a region, classifies the data, and uniformly encrypts and transmits the data to the data analysis processing station;
3) modeling is carried out in the data analysis processing station by using a neural network system ELM, so that the characteristics of the electricity load curve of the residential user are extracted, the ELM network is trained, the load curve is classified, and the extracted data record is obtained;
4) and (3) the staff repeats the steps 1) to 3) through the control end control system, extracts and records at different time points according to the requirements, and checks the resident demand response potential by the monitoring end after comparing and analyzing the time data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911214069.3A CN110930051A (en) | 2019-12-02 | 2019-12-02 | Big data analysis-based resident demand response potential analysis system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911214069.3A CN110930051A (en) | 2019-12-02 | 2019-12-02 | Big data analysis-based resident demand response potential analysis system and method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110930051A true CN110930051A (en) | 2020-03-27 |
Family
ID=69847175
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911214069.3A Pending CN110930051A (en) | 2019-12-02 | 2019-12-02 | Big data analysis-based resident demand response potential analysis system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110930051A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112036934A (en) * | 2020-08-14 | 2020-12-04 | 南方电网能源发展研究院有限责任公司 | Quotation method for participation of load aggregators in demand response considering thermoelectric coordinated operation |
| CN113036763A (en) * | 2021-05-24 | 2021-06-25 | 广东电网有限责任公司佛山供电局 | Power demand response power supply method and device, electronic equipment and readable storage medium |
| CN113919594A (en) * | 2021-11-18 | 2022-01-11 | 贵州电网有限责任公司 | Demand response potential evaluation method based on deep forest |
| CN114069644A (en) * | 2021-12-06 | 2022-02-18 | 国网山东省电力公司汶上县供电公司 | Power demand response method, system, medium and equipment based on data matching algorithm |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN205194065U (en) * | 2015-10-30 | 2016-04-27 | 重庆金鑫智慧科技有限公司 | Intelligent residential district electric quantity collection device based on thing networking |
| CN106204329A (en) * | 2016-07-13 | 2016-12-07 | 张志华 | A kind of intelligent grid load management system |
| CN106650797A (en) * | 2016-12-07 | 2017-05-10 | 广东电网有限责任公司江门供电局 | Distribution network electricity stealing suspected user intelligent recognition method based on integrated ELM (Extreme Learning Machine) |
| CN110119755A (en) * | 2019-03-22 | 2019-08-13 | 国网浙江省电力有限公司信息通信分公司 | Electricity method for detecting abnormality based on Ensemble learning model |
| CN110135761A (en) * | 2019-05-27 | 2019-08-16 | 国网河北省电力有限公司沧州供电分公司 | For power demand side response Load Regulation method of commerce, system and terminal device |
-
2019
- 2019-12-02 CN CN201911214069.3A patent/CN110930051A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN205194065U (en) * | 2015-10-30 | 2016-04-27 | 重庆金鑫智慧科技有限公司 | Intelligent residential district electric quantity collection device based on thing networking |
| CN106204329A (en) * | 2016-07-13 | 2016-12-07 | 张志华 | A kind of intelligent grid load management system |
| CN106650797A (en) * | 2016-12-07 | 2017-05-10 | 广东电网有限责任公司江门供电局 | Distribution network electricity stealing suspected user intelligent recognition method based on integrated ELM (Extreme Learning Machine) |
| CN110119755A (en) * | 2019-03-22 | 2019-08-13 | 国网浙江省电力有限公司信息通信分公司 | Electricity method for detecting abnormality based on Ensemble learning model |
| CN110135761A (en) * | 2019-05-27 | 2019-08-16 | 国网河北省电力有限公司沧州供电分公司 | For power demand side response Load Regulation method of commerce, system and terminal device |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112036934A (en) * | 2020-08-14 | 2020-12-04 | 南方电网能源发展研究院有限责任公司 | Quotation method for participation of load aggregators in demand response considering thermoelectric coordinated operation |
| CN113036763A (en) * | 2021-05-24 | 2021-06-25 | 广东电网有限责任公司佛山供电局 | Power demand response power supply method and device, electronic equipment and readable storage medium |
| CN113919594A (en) * | 2021-11-18 | 2022-01-11 | 贵州电网有限责任公司 | Demand response potential evaluation method based on deep forest |
| CN114069644A (en) * | 2021-12-06 | 2022-02-18 | 国网山东省电力公司汶上县供电公司 | Power demand response method, system, medium and equipment based on data matching algorithm |
| CN114069644B (en) * | 2021-12-06 | 2023-08-15 | 国网山东省电力公司汶上县供电公司 | Power demand response method, system, medium and equipment based on data matching algorithm |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110930051A (en) | Big data analysis-based resident demand response potential analysis system and method | |
| Hino et al. | A versatile clustering method for electricity consumption pattern analysis in households | |
| CN109634940A (en) | A kind of typical low pressure platform area's electricity consumption model building method based on magnanimity low-voltage platform area electricity consumption data | |
| CN109902340A (en) | It is a kind of meter and complicated meteorology coupled characteristic multi-source-lotus combine scene generating method | |
| CN111242161A (en) | Non-invasive non-resident user load identification method based on intelligent learning | |
| CN106294738A (en) | A kind of Intelligent household scene collocation method | |
| CN117559443A (en) | Ordered power utilization control method for large industrial user cluster under peak load | |
| CN116467648A (en) | Early monitoring method for nonlinear platform power failure based on Internet of things table | |
| Gurbuz et al. | Comprehensive non-intrusive load monitoring process: Device event detection, device feature extraction and device identification using KNN, random forest and decision tree | |
| CN110909786A (en) | New user load identification method based on characteristic index and decision tree model | |
| CN112001578A (en) | Generalized energy storage resource optimization scheduling method and system | |
| CN102930007A (en) | User power supply recovery emergency degree classification method in large area power failure emergency processing | |
| CN116777153A (en) | Distribution network flexibility attribution analysis method considering distributed energy access | |
| CN105354761B (en) | Safety and efficiency evaluation method and system for accessing wind power into power grid | |
| CN114662922B (en) | Resident demand response potential evaluation method and system considering photovoltaic uncertainty | |
| CN111061152A (en) | Attack recognition method based on deep neural network and intelligent energy power control device | |
| CN206820478U (en) | Based on the uncertain wind-powered electricity generation regulator control system contributed of wind-powered electricity generation | |
| CN115034472A (en) | Distributed photovoltaic operation intelligent prediction management system | |
| CN114662576A (en) | Non-invasive storage battery car charging detection method and system based on supervised classification | |
| Feng et al. | Design of Comprehensive Energy System Considering Anomaly Detection of Multi-Energy Data | |
| CN112307671A (en) | Method for self-adapting to different large-scale equipment instrument state threshold values | |
| Femin et al. | Modeling the temporal variations in the output of large solar PV power plants | |
| CN117910626B (en) | Intelligent household energy management method based on Internet of things | |
| Cheng et al. | A GRU based Flexible Resource Identification Approach with Small Dataset | |
| Yin et al. | Energy consumption parameter detection of green energy saving building based on artificial fish swarm algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200327 |