CN114037179A - Power load prediction system and method based on big data - Google Patents
Power load prediction system and method based on big data Download PDFInfo
- Publication number
- CN114037179A CN114037179A CN202111407177.XA CN202111407177A CN114037179A CN 114037179 A CN114037179 A CN 114037179A CN 202111407177 A CN202111407177 A CN 202111407177A CN 114037179 A CN114037179 A CN 114037179A
- Authority
- CN
- China
- Prior art keywords
- data
- analysis
- model
- load
- module
- 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.)
- Granted
Links
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
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- 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
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas 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
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention relates to the technical field of power load prediction and discloses a power load prediction system and method based on big data. The invention has the advantages that: the data model is trained by a training unit analysis deviation, the model and an algorithm are optimized, the data model outputs analysis data closest to real data to a correlation value analysis module, the correlation value analysis module retrieves the real load data of a real load database, compares the analysis data and analyzes big data correlation to obtain data with the highest correlation, an information integration module obtains the analysis value of the correlation value analysis module, information correlation integration is optimized, and then the correlation of the integration data and the power load is further improved during next prediction, so that the analysis reliability of the data model can be gradually improved through data analysis, the prediction deviation is reduced, and meanwhile, the reliability and the accuracy of high data model prediction can be further improved through continuous improvement of the data correlation.
Description
Technical Field
The invention relates to the technical field of power load prediction, in particular to a power load prediction system and method based on big data.
Background
The power load prediction is a key ring of power operation, and in order to ensure stable power supply, reduce waste and perform load analysis, power scheduling and power production can be effectively guided, but the current power load prediction system has the following problems in use:
the current power load prediction system has a single negative analysis mode and lacks automatic optimization, so that the analysis deviation is large, and the power dispatching is influenced to make correct judgment.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a power load prediction system and method based on big data, which have the advantages of self-optimization, small deviation and the like and solve the problems in the background art.
(II) technical scheme
In order to achieve the purpose of self optimization, the invention provides the following technical scheme: a power load prediction system based on big data is characterized by comprising a data model, a data acquisition module, an information integration module, an algorithm library, a correlation value analysis module and a real load database;
the output of the data acquisition module is in data connection with the input of the information integration module, the output of the information integration module is in data connection with the input of the data model, the input of the data model is in information connection with the algorithm library, the output of the data model is in data connection with the input of the associated value analysis module, the output of the real load database is in data connection with the input of the data model and the associated value analysis module respectively, and the output of the associated value analysis module is in data connection with the input of the information integration module.
Preferably, the data acquisition module comprises a real-time news acquisition unit, an electricity consumption data acquisition unit, a weather condition acquisition unit and an electricity consumption load acquisition unit.
Preferably, the data model includes a prediction analysis unit and a training unit.
Another technical problem to be solved by the present invention is to provide a big data based power load prediction method, which includes the following steps:
1) the data acquisition module acquires big data by acquiring a real-time news acquisition unit, an electricity consumption data acquisition unit, a weather condition acquisition unit and an electricity consumption load acquisition unit;
2) the information integration module integrates and extracts effective data according to the information association degree;
3) the data model extracts data integrated by the information integration module and brings the data into the model, and the prediction analysis unit calls a plurality of algorithms and brings the algorithms into the data to obtain a result;
4) the real load database inputs real load data into the data model, the real data is compared with the prediction model, the deviation is analyzed through the training unit, the data model is trained, and the model and the algorithm are optimized;
5) the data model outputs analysis data closest to the real data to the association value analysis module, the association value analysis module calls the real load data of the real load database, compares the analysis data, and analyzes the association degree of the big data to obtain the data with the highest association degree;
6) and the information integration module acquires the analysis value of the association degree of the association value analysis module, optimizes information association degree integration, and further improves the association degree of the integrated data and the power load when predicting next time.
Preferably, the algorithm library is preset with a plurality of load analysis algorithms, different algorithms are respectively adopted in the step 4) to obtain different analysis values, the training unit in the step 4) compares the analysis values obtained by the algorithms with actual load values to obtain deviation values of the algorithms, optimizes each algorithm according to the deviation values, and inputs the deviation values into the model to realize model optimization.
Preferably, the data acquisition module in step 1) acquires news, real-time electricity consumption data, weather temperature data and power grid load data related to electric power through the real-time news acquisition unit, the electricity consumption data acquisition unit, the weather condition acquisition unit and the electricity consumption load acquisition unit.
Preferably, the information integration module optimizes the information integration capability according to the correlation value analysis result provided by the correlation value analysis module, and gradually improves the correlation degree of the integrated big data information on the influence of the power grid load.
Preferably, the data model is brought into historical data by a training unit to be analyzed and compared, building is completed, and the data model is optimized through continuous training of the training unit.
Preferably, an algorithm with the largest analysis deviation is deleted from each predictive analysis round of the algorithm library, and a new algorithm is added to each predictive analysis round of the algorithm library.
(III) advantageous effects
Compared with the prior art, the invention provides a power load prediction system and method based on big data, which have the following beneficial effects:
the big data-based power load prediction system and method are provided with a data model, a data acquisition module, an information integration module, an algorithm library, a correlation value analysis module and a real load database. The output end of the data acquisition module is in data connection with the input end of the information integration module, the output end of the information integration module is in data connection with the input end of the data model, the input end of the data model is in information connection with the algorithm library, the output end of the data model is in data connection with the input end of the associated value analysis module, the output end of the real load database is in data connection with the input ends of the data model and the associated value analysis module respectively, and the output end of the associated value analysis module is in data connection with the input end of the information integration module, so that the data acquisition module can acquire big data through the acquisition of a real-time news acquisition unit, a power utilization data acquisition unit, a weather condition acquisition unit and a power utilization load acquisition unit, the data model extracts data integrated by the information integration module and brings the data into the model, the prediction analysis unit calls a plurality of algorithms and brings the algorithms into the data, obtaining a result, inputting real load data into a data model by a real load database, comparing the real data with a prediction model, analyzing deviation by a training unit, training the data model, optimizing the model and an algorithm, outputting the analysis data closest to the real data to a correlation value analysis module by the data model, calling the real load data of the real load database by the correlation value analysis module, comparing the analysis data, analyzing the correlation degree of big data to obtain the data with the highest correlation degree, acquiring the analysis value of the correlation degree of the correlation value analysis module by an information integration module, optimizing information correlation degree integration, further improving the correlation degree of the integrated data and the power load during next prediction, thereby gradually improving the analysis reliability of the data model by the analysis of the data, reducing the prediction deviation, and simultaneously continuously improving the data correlation degree, the reliability and accuracy of the high data model prediction can be further improved.
Drawings
FIG. 1 is a schematic diagram of the system of the present invention;
FIG. 2 is a flow chart of the method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to 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. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
a power load prediction system based on big data comprises a data model, a data acquisition module, an information integration module, an algorithm library, a correlation value analysis module and a real load database;
the output end of the data acquisition module is in data connection with the input end of the information integration module, the output end of the information integration module is in data connection with the input end of the data model, the input end of the data model is in information connection with the algorithm library, the output end of the data model is in data connection with the input end of the associated value analysis module, the output end of the real load database is in data connection with the input ends of the data model and the associated value analysis module respectively, and the output end of the associated value analysis module is in data connection with the input end of the information integration module.
The data acquisition module comprises a real-time news acquisition unit, an electricity consumption data acquisition unit, a weather condition acquisition unit and an electricity consumption load acquisition unit.
The data model comprises a prediction analysis unit and a training unit.
Another technical problem to be solved by the present invention is to provide a big data based power load prediction method, which includes the following steps:
1) the data acquisition module acquires big data by acquiring a real-time news acquisition unit, an electricity consumption data acquisition unit, a weather condition acquisition unit and an electricity consumption load acquisition unit;
2) the information integration module integrates and extracts effective data according to the information association degree;
3) the data model extracts data integrated by the information integration module and brings the data into the model, and the prediction analysis unit calls a plurality of algorithms and brings the algorithms into the data to obtain a result;
4) the real load database inputs real load data into the data model, the real data is compared with the prediction model, the deviation is analyzed through the training unit, the data model is trained, and the model and the algorithm are optimized;
5) the data model outputs analysis data closest to the real data to the association value analysis module, the association value analysis module calls the real load data of the real load database, compares the analysis data, and analyzes the association degree of the big data to obtain the data with the highest association degree;
6) and the information integration module acquires the analysis value of the association degree of the association value analysis module, optimizes information association degree integration, and further improves the association degree of the integrated data and the power load when predicting next time.
A plurality of load analysis algorithms are preset in the algorithm library, different algorithms are respectively adopted in the step 4) to obtain different analysis values, the training unit in the step 4) compares the analysis values obtained by the algorithms with actual load values to obtain deviation values of the algorithms, the algorithms are optimized according to the deviation values, and the deviation values are input into a model to realize model optimization.
The data acquisition module in the step 1) acquires news, real-time electricity consumption data, weather and temperature data and power grid load data related to electric power through the real-time news acquisition unit, the electricity consumption data acquisition unit, the weather condition acquisition unit and the electricity consumption load acquisition unit.
The information integration module optimizes the information integration capability according to the correlation value analysis result provided by the correlation value analysis module, and gradually improves the correlation degree of the integrated big data information on the influence of the power grid load.
The data model is brought into historical data by the training unit to be analyzed and compared, the building is completed, and the data model is optimized through continuous training of the training unit.
And deleting an algorithm with the largest analysis deviation in each prediction analysis round of the algorithm library, and supplementing a new algorithm in each prediction analysis round of the algorithm library.
The embodiment has the advantages that the analysis reliability of the data model can be gradually improved through the analysis of the data, so that the prediction deviation is reduced, meanwhile, the reliability and the accuracy of the prediction of the high-data model can be further improved through the continuous improvement of the data association degree, news reference can be introduced through the collection of real-time news information, and the judgment capability of the policy on the influence of the power consumption load is improved.
Example two:
a power load prediction system based on big data comprises a data model, a data acquisition module, an information integration module, an algorithm library, a correlation value analysis module and a real load database;
the output end of the data acquisition module is in data connection with the input end of the information integration module, the output end of the information integration module is in data connection with the input end of the data model, the input end of the data model is in information connection with the algorithm library, the output end of the data model is in data connection with the input end of the associated value analysis module, the output end of the real load database is in data connection with the input ends of the data model and the associated value analysis module respectively, and the output end of the associated value analysis module is in data connection with the input end of the information integration module.
The data acquisition module comprises an electricity price data acquisition unit, an electricity consumption data acquisition unit, a weather condition acquisition unit and an electricity consumption load acquisition unit.
The data model comprises a prediction analysis unit and a training unit.
Another technical problem to be solved by the present invention is to provide a big data based power load prediction method, which includes the following steps:
1) the data acquisition module acquires big data through an acquisition electricity price data acquisition unit, an electricity consumption data acquisition unit, a weather condition acquisition unit and an electricity consumption load acquisition unit;
2) the information integration module integrates and extracts effective data according to the information association degree;
3) the data model extracts data integrated by the information integration module and brings the data into the model, and the prediction analysis unit calls a plurality of algorithms and brings the algorithms into the data to obtain a result;
4) the real load database inputs real load data into the data model, the real data is compared with the prediction model, the deviation is analyzed through the training unit, the data model is trained, and the model and the algorithm are optimized;
5) the data model outputs analysis data closest to the real data to the association value analysis module, the association value analysis module calls the real load data of the real load database, compares the analysis data, and analyzes the association degree of the big data to obtain the data with the highest association degree;
6) and the information integration module acquires the analysis value of the association degree of the association value analysis module, optimizes information association degree integration, and further improves the association degree of the integrated data and the power load when predicting next time.
A plurality of load analysis algorithms are preset in the algorithm library, different algorithms are respectively adopted in the step 4) to obtain different analysis values, the training unit in the step 4) compares the analysis values obtained by the algorithms with actual load values to obtain deviation values of the algorithms, the algorithms are optimized according to the deviation values, and the deviation values are input into a model to realize model optimization.
The data acquisition module in the step 1) acquires news, real-time electricity consumption data, weather and temperature data and power grid load data related to electric power through the electricity price data acquisition unit, the electricity consumption data acquisition unit, the weather condition acquisition unit and the electricity consumption load acquisition unit.
The information integration module optimizes the information integration capability according to the correlation value analysis result provided by the correlation value analysis module, and gradually improves the correlation degree of the integrated big data information on the influence of the power grid load.
The data model is brought into historical data by the training unit to be analyzed and compared, the building is completed, and the data model is optimized through continuous training of the training unit.
And deleting an algorithm with the largest analysis deviation in each prediction analysis round of the algorithm library, and supplementing a new algorithm in each prediction analysis round of the algorithm library.
The advantage of this embodiment is, gather the price of electricity data, can analyze out the relevance degree that the price of electricity influences the power consumption load, through the analysis, can provide the reference to power consumption load and price of electricity adjustment, improve the practicality.
The invention has the beneficial effects that: the system is provided with a data model, a data acquisition module, an information integration module, an algorithm library, an association value analysis module and a real load database. The output end of the data acquisition module is in data connection with the input end of the information integration module, the output end of the information integration module is in data connection with the input end of the data model, the input end of the data model is in information connection with the algorithm library, the output end of the data model is in data connection with the input end of the associated value analysis module, the output end of the real load database is in data connection with the input ends of the data model and the associated value analysis module respectively, and the output end of the associated value analysis module is in data connection with the input end of the information integration module, so that the data acquisition module can acquire big data through the acquisition of a real-time news acquisition unit, a power utilization data acquisition unit, a weather condition acquisition unit and a power utilization load acquisition unit, the data model extracts data integrated by the information integration module and brings the data into the model, the prediction analysis unit calls a plurality of algorithms and brings the algorithms into the data, obtaining a result, inputting real load data into a data model by a real load database, comparing the real data with a prediction model, analyzing deviation by a training unit, training the data model, optimizing the model and an algorithm, outputting the analysis data closest to the real data to a correlation value analysis module by the data model, calling the real load data of the real load database by the correlation value analysis module, comparing the analysis data, analyzing the correlation degree of big data to obtain the data with the highest correlation degree, acquiring the analysis value of the correlation degree of the correlation value analysis module by an information integration module, optimizing information correlation degree integration, further improving the correlation degree of the integrated data and the power load during next prediction, thereby gradually improving the analysis reliability of the data model by the analysis of the data, reducing the prediction deviation, and simultaneously continuously improving the data correlation degree, the reliability and accuracy of the high data model prediction can be further improved.
Typical cases are as follows: a power load prediction system based on big data comprises a data model, a data acquisition module, an information integration module, an algorithm library, a correlation value analysis module and a real load database;
the output end of the data acquisition module is in data connection with the input end of the information integration module, the output end of the information integration module is in data connection with the input end of the data model, the input end of the data model is in information connection with the algorithm library, the output end of the data model is in data connection with the input end of the associated value analysis module, the output end of the real load database is in data connection with the input ends of the data model and the associated value analysis module respectively, and the output end of the associated value analysis module is in data connection with the input end of the information integration module.
The data acquisition module comprises a real-time news acquisition unit, an electricity consumption data acquisition unit, a weather condition acquisition unit and an electricity consumption load acquisition unit.
The data model comprises a prediction analysis unit and a training unit.
Another technical problem to be solved by the present invention is to provide a big data based power load prediction method, which includes the following steps:
1) the data acquisition module acquires big data by acquiring a real-time news acquisition unit, an electricity consumption data acquisition unit, a weather condition acquisition unit and an electricity consumption load acquisition unit;
2) the information integration module integrates and extracts effective data according to the information association degree;
3) the data model extracts data integrated by the information integration module and brings the data into the model, and the prediction analysis unit calls a plurality of algorithms and brings the algorithms into the data to obtain a result;
4) the real load database inputs real load data into the data model, the real data is compared with the prediction model, the deviation is analyzed through the training unit, the data model is trained, and the model and the algorithm are optimized;
5) the data model outputs analysis data closest to the real data to the association value analysis module, the association value analysis module calls the real load data of the real load database, compares the analysis data, and analyzes the association degree of the big data to obtain the data with the highest association degree;
6) and the information integration module acquires the analysis value of the association degree of the association value analysis module, optimizes information association degree integration, and further improves the association degree of the integrated data and the power load when predicting next time.
A plurality of load analysis algorithms are preset in the algorithm library, different algorithms are respectively adopted in the step 4) to obtain different analysis values, the training unit in the step 4) compares the analysis values obtained by the algorithms with actual load values to obtain deviation values of the algorithms, the algorithms are optimized according to the deviation values, and the deviation values are input into a model to realize model optimization.
The data acquisition module in the step 1) acquires news, real-time electricity consumption data, weather and temperature data and power grid load data related to electric power through the real-time news acquisition unit, the electricity consumption data acquisition unit, the weather condition acquisition unit and the electricity consumption load acquisition unit.
The information integration module optimizes the information integration capability according to the correlation value analysis result provided by the correlation value analysis module, and gradually improves the correlation degree of the integrated big data information on the influence of the power grid load.
The data model is brought into historical data by the training unit to be analyzed and compared, the building is completed, and the data model is optimized through continuous training of the training unit.
And deleting an algorithm with the largest analysis deviation in each prediction analysis round of the algorithm library, and supplementing a new algorithm in each prediction analysis round of the algorithm library.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (9)
1. A power load prediction system based on big data is characterized by comprising a data model, a data acquisition module, an information integration module, an algorithm library, a correlation value analysis module and a real load database;
the output of the data acquisition module is in data connection with the input of the information integration module, the output of the information integration module is in data connection with the input of the data model, the input of the data model is in information connection with the algorithm library, the output of the data model is in data connection with the input of the associated value analysis module, the output of the real load database is in data connection with the input of the data model and the associated value analysis module respectively, and the output of the associated value analysis module is in data connection with the input of the information integration module.
2. The big data based power load forecasting system and method according to claim 1, wherein the data collection module comprises a real-time news collection unit, a power consumption data collection unit, a weather condition collection unit and a power consumption load collection unit.
3. The big data based power load forecasting system and method according to claim 1, wherein the data model includes a predictive analysis unit and a training unit.
4. A method for predicting a power load based on big data, which uses the prediction system of the above claims 1-3, characterized by comprising the steps of:
1) the data acquisition module acquires big data by acquiring a real-time news acquisition unit, an electricity consumption data acquisition unit, a weather condition acquisition unit and an electricity consumption load acquisition unit;
2) the information integration module integrates and extracts effective data according to the information association degree;
3) the data model extracts data integrated by the information integration module and brings the data into the model, and the prediction analysis unit calls a plurality of algorithms and brings the algorithms into the data to obtain a result;
4) the real load database inputs real load data into the data model, the real data is compared with the prediction model, the deviation is analyzed through the training unit, the data model is trained, and the model and the algorithm are optimized;
5) the data model outputs analysis data closest to the real data to the association value analysis module, the association value analysis module calls the real load data of the real load database, compares the analysis data, and analyzes the association degree of the big data to obtain the data with the highest association degree;
6) and the information integration module acquires the analysis value of the association degree of the association value analysis module, optimizes information association degree integration, and further improves the association degree of the integrated data and the power load when predicting next time.
5. The big data-based power load forecasting method as claimed in claim 4, wherein a plurality of load analysis algorithms are preset in the algorithm library, different algorithms are respectively adopted in the step 4) to obtain different analysis values, the training unit in the step 4) compares the analysis values obtained by the algorithms with actual load values to obtain deviation values of the algorithms, optimizes the algorithms according to the deviation values, and inputs the deviation values into the model to realize model optimization.
6. The big data-based power load forecasting method according to claim 4, wherein the data collecting module in step 1) collects news, real-time electricity consumption data, weather temperature data and power grid load data related to power through the real-time news collecting unit, the electricity consumption data collecting unit, the weather condition collecting unit and the electricity consumption load collecting unit.
7. The big data-based power load forecasting method according to claim 4, wherein the information integration module optimizes information integration capability according to the correlation value analysis result provided by the correlation value analysis module, and gradually improves the correlation degree of the integrated big data information on the influence of the power grid load.
8. The big data-based power load prediction method according to claim 4, wherein the data model is built by introducing historical data into the training unit for analysis and comparison, and the data model is optimized through continuous training of the training unit.
9. The big data based power load forecasting method according to claim 4, wherein an algorithm with the largest analysis deviation is deleted from each predictive analysis round of the algorithm library, and a new algorithm is added to each predictive analysis round of the algorithm library.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111407177.XA CN114037179B (en) | 2021-11-24 | 2021-11-24 | Big data-based power load prediction system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111407177.XA CN114037179B (en) | 2021-11-24 | 2021-11-24 | Big data-based power load prediction system and method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114037179A true CN114037179A (en) | 2022-02-11 |
| CN114037179B CN114037179B (en) | 2023-04-28 |
Family
ID=80138637
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111407177.XA Active CN114037179B (en) | 2021-11-24 | 2021-11-24 | Big data-based power load prediction system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114037179B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107239854A (en) * | 2017-05-22 | 2017-10-10 | 华北电力大学 | Load forecasting method based on EMD GRA MPSO LSSVM models |
| CN111091243A (en) * | 2019-12-13 | 2020-05-01 | 南京工程学院 | PCA-GM-based power load prediction method, system, computer-readable storage medium, and computing device |
| CN111144650A (en) * | 2019-12-26 | 2020-05-12 | 南京工程学院 | Power load prediction method, device, computer readable storage medium and equipment |
| CN111382906A (en) * | 2020-03-06 | 2020-07-07 | 南京工程学院 | Power load prediction method, system, equipment and computer readable storage medium |
| CN111652420A (en) * | 2020-05-26 | 2020-09-11 | 张志远 | Real-time load prediction system |
| AU2020104000A4 (en) * | 2020-12-10 | 2021-02-18 | Guangxi University | Short-term Load Forecasting Method Based on TCN and IPSO-LSSVM Combined Model |
-
2021
- 2021-11-24 CN CN202111407177.XA patent/CN114037179B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107239854A (en) * | 2017-05-22 | 2017-10-10 | 华北电力大学 | Load forecasting method based on EMD GRA MPSO LSSVM models |
| CN111091243A (en) * | 2019-12-13 | 2020-05-01 | 南京工程学院 | PCA-GM-based power load prediction method, system, computer-readable storage medium, and computing device |
| CN111144650A (en) * | 2019-12-26 | 2020-05-12 | 南京工程学院 | Power load prediction method, device, computer readable storage medium and equipment |
| CN111382906A (en) * | 2020-03-06 | 2020-07-07 | 南京工程学院 | Power load prediction method, system, equipment and computer readable storage medium |
| CN111652420A (en) * | 2020-05-26 | 2020-09-11 | 张志远 | Real-time load prediction system |
| AU2020104000A4 (en) * | 2020-12-10 | 2021-02-18 | Guangxi University | Short-term Load Forecasting Method Based on TCN and IPSO-LSSVM Combined Model |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114037179B (en) | 2023-04-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110288136B (en) | Wind power multi-step prediction model establishment method | |
| CN109345409B (en) | Comprehensive energy efficiency management method for residential users based on broadband carrier | |
| CN115796393B (en) | Energy management optimization method, system and storage medium based on multi-energy interaction | |
| CN114330097A (en) | Heterogeneous data and deep learning wind power cluster power prediction method and device | |
| CN117013527A (en) | Distributed photovoltaic power generation power prediction method | |
| CN116151464A (en) | Photovoltaic power generation power prediction method, system and storable medium | |
| CN114037179A (en) | Power load prediction system and method based on big data | |
| CN116131256A (en) | Method and system for calculating openable capacity of power distribution network based on equipment synchronization rate | |
| CN115238854A (en) | Short-term load prediction method based on TCN-LSTM-AM | |
| CN111898812B (en) | Distributed photovoltaic data virtual acquisition method | |
| CN113888231A (en) | Marketing data screening analysis system based on artificial intelligence | |
| Sicheng et al. | Abnormal line loss data detection and correction method | |
| CN116599036A (en) | Two-stage non-invasive load decomposition method based on TCN and Informar | |
| Yu et al. | A regression prediction model based on incremental iteration for big industrial data | |
| Xia et al. | Research on short-term load forecasting of power system based on gradient lifting tree | |
| CN117543589B (en) | Scheduling method of cascade hydropower safety centralized control system | |
| Cai et al. | A hybrid short-term load forecasting model based on sparse attention mechanism | |
| CN117172391B (en) | Line loss reasonable interval prediction method, device and medium based on multiple regression | |
| CN117407681B (en) | Time sequence data prediction model establishment method based on vector clustering | |
| Li et al. | The construction of" multi-source sharing" power big data intelligent management system driven by the goal of improving quality and efficiency | |
| CN112966868A (en) | Building load day-ahead prediction method and system | |
| CN117439057A (en) | Short-term electric load prediction method | |
| Wang et al. | An evaluation method for power grid development morphology considering regional characteristics | |
| Li et al. | Design of short term prediction system for interactive potential of power users based on artificial intelligence | |
| Wu et al. | Short-term wind power forecast based on Long Short Term Memory |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |