CN113283679A - AI artificial intelligence based power load prediction system - Google Patents
AI artificial intelligence based power load prediction system Download PDFInfo
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- CN113283679A CN113283679A CN202110739475.2A CN202110739475A CN113283679A CN 113283679 A CN113283679 A CN 113283679A CN 202110739475 A CN202110739475 A CN 202110739475A CN 113283679 A CN113283679 A CN 113283679A
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- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 18
- 238000004458 analytical method Methods 0.000 claims description 26
- 238000013480 data collection Methods 0.000 claims description 19
- 238000001514 detection method Methods 0.000 claims description 14
- 238000012544 monitoring process Methods 0.000 claims description 12
- 238000013500 data storage Methods 0.000 claims description 5
- 238000000556 factor analysis Methods 0.000 claims description 4
- AGVAZMGAQJOSFJ-WZHZPDAFSA-M cobalt(2+);[(2r,3s,4r,5s)-5-(5,6-dimethylbenzimidazol-1-yl)-4-hydroxy-2-(hydroxymethyl)oxolan-3-yl] [(2r)-1-[3-[(1r,2r,3r,4z,7s,9z,12s,13s,14z,17s,18s,19r)-2,13,18-tris(2-amino-2-oxoethyl)-7,12,17-tris(3-amino-3-oxopropyl)-3,5,8,8,13,15,18,19-octamethyl-2 Chemical compound [Co+2].N#[C-].[N-]([C@@H]1[C@H](CC(N)=O)[C@@]2(C)CCC(=O)NC[C@@H](C)OP(O)(=O)O[C@H]3[C@H]([C@H](O[C@@H]3CO)N3C4=CC(C)=C(C)C=C4N=C3)O)\C2=C(C)/C([C@H](C\2(C)C)CCC(N)=O)=N/C/2=C\C([C@H]([C@@]/2(CC(N)=O)C)CCC(N)=O)=N\C\2=C(C)/C2=N[C@]1(C)[C@@](C)(CC(N)=O)[C@@H]2CCC(N)=O AGVAZMGAQJOSFJ-WZHZPDAFSA-M 0.000 claims description 2
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- 238000000034 method Methods 0.000 description 2
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Abstract
The invention discloses an AI artificial intelligence-based power load forecasting system which comprises a power load data collecting module, wherein the connecting end of the power load data collecting module is connected with a data summarizing module, the connecting end of the data summarizing module is connected with an intelligent data forecasting module, the connecting end of the intelligent data forecasting module is connected with a power data structure analyzing module, the connecting end of the power data structure analyzing module is connected with a power load early warning module, and the connecting end of the power load early warning module is connected with a forecasting model detecting module. The invention avoids the damage of the power system and the loss of the power cost, improves the timeliness of data, the reliability of early warning, the accuracy of a prediction result, the utilization rate of power generation equipment and the effectiveness of economic dispatching, ensures the reliability of the operation of the power system and the supply-demand balance of the power market, provides a reliable decision basis for the planning and the operation of the power system, and promotes the economic persistence development of the power market.
Description
Technical Field
The invention belongs to the technical field of power load prediction, and particularly relates to an AI artificial intelligence-based power load prediction system.
Background
The power load prediction is an important component of power system planning, is also the basis of power system economic operation, and is extremely important to power system planning and operation, the power load prediction is a series of prediction work which is carried out by taking a power load as an object, from the viewpoint of the prediction object, the power load prediction comprises the prediction of future power demand, the prediction of future power consumption and the prediction of a load curve, the main work of the prediction is to predict the time distribution and the space distribution of the future power load and provide a reliable decision basis for the power system planning and operation, the higher the accuracy of the load prediction is, the more beneficial to improving the utilization rate of power generation equipment and the effectiveness of economic dispatching, on the contrary, when the load prediction error is larger, not only can a large amount of operation cost and profit loss be caused, but also the reliability of the power system operation and the supply-demand balance of a power market can be influenced, therefore, it is very important to accurately predict the power load.
The existing prediction method of the power load prediction system is laggard, the prediction precision is not high, the requirement of the prediction precision cannot be met, the utilization rate of power generation equipment is low, a large amount of operation cost and profit loss are caused, and even the economic operation of the power system is influenced.
To this end, we propose an AI artificial intelligence based power load prediction system to solve the above problems.
Disclosure of Invention
The present invention is directed to a power load prediction system based on AI artificial intelligence to solve the above problems.
In order to achieve the purpose, the invention adopts the following technical scheme:
an AI artificial intelligence-based power load prediction system comprises a power load data collection module, a power load data analysis module and a power consumption prediction module, wherein the power load data collection module is used for collecting real-time data of a power load and real-time influence factors of power consumption; the connection end of the power load data collection module is connected with a data summarization module, and the data summarization module is used for summarizing and storing the related data collected by the power load data collection module; the intelligent data prediction module is used for establishing a power load prediction model and accurately predicting the power load; the intelligent data prediction module is connected with a power data structure analysis module, and the power data structure analysis module is used for analyzing and calculating external influence factors and obtaining a power utilization structure; the power data structure analysis module is connected with a power load early warning module, and the power load early warning module is used for comparing with previous records and marking a charge peak value; the power load early warning module connecting end is connected with prediction model detection module, prediction model detection module is used for comparing the prediction model and debugging and revising according to the weight value of actual power consumption.
Furthermore, the power load data collection module comprises an influence parameter analysis unit for monitoring influence factors such as temperature, humidity, weather and holidays in real time and a power load real-time monitoring unit for monitoring power consumption in real time.
Furthermore, the data summarization module comprises a single variable storage unit for recording and storing a single influence factor and a comprehensive data storage unit for recording and storing a comprehensive factor.
Furthermore, the intelligent data prediction module comprises a prediction model establishing unit for analyzing the real-time data and the influence factors and establishing a preliminary prediction model and a comprehensive prediction unit for predicting the big data and the short-term data.
Further, the electric power data structure analysis module comprises an external factor analysis unit for analyzing and comparing the influence factors and an electric power utilization structure unit for predicting the compared electric power utilization structure through analysis.
Further, the power load early warning module comprises a peak marking unit for comparing the stored data and marking the peak value of the power consumption and a comprehensive comparison unit for integrally analyzing the big data.
Further, the prediction model detection module comprises a prediction model distribution unit for analyzing the ratio of the prediction model weights and a prediction model debugging unit for performing final debugging on the prediction model.
Compared with the prior art, the invention has the beneficial effects that:
the invention monitors the electricity consumption and the influence factors thereof in real time through the electricity load data collection module and collects the related data to ensure the timeliness and the reliability of the data, the data summarization module stores and records single variable and comprehensive data to avoid data loss or damage, the intelligent data prediction module analyzes the real-time data and the influence factors and establishes a preliminary prediction model and a comprehensive data prediction model to preliminarily predict the electricity consumption, the electricity consumption structure is analyzed and compared by combining with the electricity data structure analysis module to establish an electricity consumption structure diagram, the electricity load early warning module analyzes and compares the big data and marks the peak value, the alarm is notified after an emergency situation is predicted to avoid the damage of an electric power system and the loss of electric power cost, and the prediction model detection module analyzes and debugs the ratio of the weight of the electricity consumption, the method has the advantages that the accuracy of a prediction model is guaranteed, the utilization rate of power generation equipment and the effectiveness of economic dispatching are improved, the operation reliability of a power system and the supply-demand balance of a power market are guaranteed, a reliable decision basis is provided for the planning and operation of the power system, and the economic persistence development of the power market is promoted.
Drawings
FIG. 1 is a block diagram of a power load forecasting system based on AI artificial intelligence according to the present invention;
FIG. 2 is a block diagram of a unit structure of an AI-based artificial intelligence power load prediction system according to the present invention;
fig. 3 is a block diagram of an impact parameter analysis unit in the power load prediction system based on AI artificial intelligence according to the present invention.
In the figure: 1. a power load data collection module; 101. an influence parameter analysis unit; 102. a power load real-time monitoring unit; 2. a data summarization module; 201. a single variable storage unit; 202. a comprehensive data storage unit; 3. an intelligent data prediction module; 301. a prediction model establishing unit; 302. a comprehensive prediction unit; 4. a power data structure analysis module; 401. an extrinsic factor analyzing unit; 402. an electricity-using structural unit; 5. a power load early warning module; 501. a peak marking unit; 502. a comprehensive comparison unit; 6. a prediction model detection module; 601. a prediction model allocation unit; 602. and a prediction model debugging unit.
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. 1-3, an AI artificial intelligence-based power load prediction system includes a power load data collection module 1, where the power load data collection module 1 is configured to collect real-time data of a power load and real-time influence factors of power consumption to ensure timeliness and reliability of the data; the connection end of the power load data collection module 1 is connected with a data summarization module 2, and the data summarization module 2 is used for summarizing and storing the related data collected by the power load data collection module 1, so that data loss or damage is prevented; the connection end of the data summarizing module 2 is connected with an intelligent data prediction module 3, and the intelligent data prediction module 3 is used for establishing a power load prediction model and accurately predicting the power load so as to provide a reliable basis for the operation and planning of a power system; the connection end of the intelligent data prediction module 3 is connected with an electric power data structure analysis module 4, and the electric power data structure analysis module 4 is used for analyzing and calculating external influence factors and obtaining an electricity utilization structure, so that the accuracy and authority of a prediction result are improved; the connection end of the electric power data structure analysis module 4 is connected with an electric power load early warning module 5, and the electric power load early warning module 5 is used for comparing with the previous record and marking a charge peak value so as to avoid the fault and the electric power cost loss of an electric power system; the connection end of the electric power load early warning module 5 is connected with a prediction model detection module 6, and the prediction model detection module 6 is used for debugging and modifying a prediction model according to the comparison of the weight value of the actual power consumption, so that the accuracy of a prediction result is ensured, and the sustainable development of an electric power market is promoted;
the power load data collection module 1 comprises an influence parameter analysis unit 101 for monitoring influence factors such as temperature, humidity, weather, holidays and the like in real time and a power load real-time monitoring unit 102 for monitoring power consumption in real time, so that the timeliness and reliability of monitoring data are improved, the influence factors are accurately monitored and further analyzed and compared, and the accuracy and authority of a detection result are improved;
referring to fig. 1-2, the data summarization module 2 includes a single variable storage unit 201 for recording and storing a single influence factor and a comprehensive data storage unit 202 for recording and storing a comprehensive factor, so that real-time data loss or data damage is effectively avoided, large data can be conveniently consulted, contrasted and analyzed in the later stage, and prediction accuracy is improved;
the intelligent data prediction module 3 comprises a prediction model establishing unit 301 for analyzing real-time data and influencing factors and establishing a preliminary prediction model and an integrated prediction unit 302 for predicting big data and short-term data, wherein the preliminary prediction model is established and the short-term data and the big data are comprehensively predicted by accurately analyzing the big data and the influencing factors;
the electric power data structure analysis module 4 comprises an external factor analysis unit 401 for analyzing and comparing influence factors and an electric power structure unit 402 for predicting a compared electric power structure through analysis, and the electric power structure is predicted through analysis and comparison of big data and influence factors and analysis of a data curve of electric power consumption, so that the accuracy of a prediction result is improved;
the power load early warning module 5 comprises a peak value marking unit 501 for comparing stored data and marking a power consumption peak value and a comprehensive comparison unit 502 for integrally analyzing big data, so that the safety of a prediction system is improved, and the loss of power cost is effectively reduced;
the prediction model detection module 6 comprises a prediction model distribution unit 601 for analyzing the weight ratio of the prediction models and a prediction model debugging unit 602 for finally debugging the prediction models, so that the accuracy of prediction results, the utilization rate of power generation equipment and the effectiveness of economic dispatching are improved, and the operation reliability of a power system and the supply and demand balance of a power market are ensured.
The operating principle of the present invention will now be described as follows:
in the invention, the power consumption and the influence factors thereof are monitored in real time and relevant data are collected through the power load real-time monitoring unit 102 and the influence parameter analysis unit 101 in the power load data collection module 1, so that the timeliness and the reliability of the data are ensured, and the accuracy of a prediction result is improved;
the single variable storage unit 201 and the comprehensive data storage unit 202 in the data summarization module 2 are used for storing and recording the single variable and the comprehensive data of the data collected by the power load data collection module 1, so that the data loss or damage is effectively avoided, and the reliability of the prediction system is improved;
then, real-time data and influence factors are analyzed and a preliminary prediction model and a comprehensive data prediction model are established through a prediction model establishing unit 301 and a comprehensive prediction unit 302 in the intelligent data prediction module 3, so that the electricity consumption is preliminarily predicted;
the external factor analysis unit 401 and the power utilization structure unit 402 in the power data structure analysis module 4 are combined to analyze and compare the influence factors and the power utilization curve graph, so that a power utilization structure chart is created, and the accuracy of the detection result is improved;
the peak marking unit 501 and the comprehensive comparison unit 502 in the power load early warning module 5 analyze and compare the big data and mark the peak, and the alarm is notified immediately after an emergency situation is predicted, so that the damage to the power system and the loss of the power cost are avoided, and the stable and reliable operation of the power system is facilitated;
finally, the ratio of the power consumption weight is analyzed and the prediction model is finally debugged through the prediction model distribution unit 601 and the prediction model debugging unit 602 in the prediction model detection module 6, so that the accuracy of the prediction model, the operation reliability of the power system and the supply and demand balance of the power market are ensured, the utilization rate of the power generation equipment and the effectiveness of economic dispatching are improved, a reliable decision basis is provided for the planning and operation of the power system, and the economic persistence development of the power market is promoted.
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 (7)
1. An AI artificial intelligence-based power load prediction system comprises a power load data collection module (1), and is characterized in that the power load data collection module (1) is used for collecting real-time data of a power load and real-time influence factors of power consumption;
the connection end of the power load data collection module (1) is connected with a data summarization module (2), and the data summarization module (2) is used for summarizing and storing the related data collected by the power load data collection module (1);
the intelligent data prediction module (3) is connected with the connecting end of the data summarization module (2), and the intelligent data prediction module (3) is used for establishing a power load prediction model and accurately predicting the power load;
the intelligent data prediction module (3) is connected with a power data structure analysis module (4) through a connecting end, and the power data structure analysis module (4) is used for analyzing and calculating external influence factors and obtaining a power utilization structure;
the electric power data structure analysis module (4) is connected with an electric power load early warning module (5) at a connecting end, and the electric power load early warning module (5) is used for comparing with the previous record and marking a charge peak value;
the power load early warning module (5) is connected with a prediction model detection module (6) in a connecting end mode, and the prediction model detection module (6) is used for debugging and modifying the prediction model according to the comparison of the weight value of the actual power consumption.
2. The AI artificial intelligence based power load prediction system according to claim 1, wherein the power load data collection module (1) comprises an influence parameter analysis unit (101) for real-time monitoring of influence factors such as temperature, humidity, weather, holidays, etc. and a power load real-time monitoring unit (102) for real-time monitoring of power consumption.
3. The AI artificial intelligence based power load forecasting system of claim 1 wherein the data aggregation module (2) comprises a single variable storage unit (201) for record storage of single influencing factors and an integrated data storage unit (202) for record storage of integrated factors.
4. The AI artificial intelligence based power load prediction system of claim 1 wherein the intelligent data prediction module (3) comprises a prediction model building unit (301) that analyzes real-time data and influencing factors and builds a preliminary prediction model and a comprehensive prediction unit (302) that predicts big data and short-term data.
5. The AI artificial intelligence based power load forecasting system of claim 1, wherein the power data structure analysis module (4) comprises an extrinsic factor analysis unit (401) for analyzing and comparing the influencing factors and a power utilization structure unit (402) for predicting the compared power utilization structure through analysis.
6. The AI artificial intelligence based power load forecasting system of claim 1 wherein the power load warning module (5) comprises a peak marking unit (501) to compare stored data and mark peak power usage and a comprehensive comparison unit (502) to analyze big data as a whole.
7. The AI artificial intelligence based power load prediction system according to claim 1 wherein the prediction model detection module (6) comprises a prediction model assignment unit (601) for analyzing a ratio of prediction model weights and a prediction model debugging unit (602) for final debugging of the prediction model.
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| CN116910681A (en) * | 2023-09-12 | 2023-10-20 | 睿至科技集团有限公司 | Electric power data analysis method and system based on Internet of things |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103295075A (en) * | 2013-04-01 | 2013-09-11 | 沈阳航空航天大学 | Ultra-short-term power load forecasting and early warning method |
| CN104123595A (en) * | 2014-07-22 | 2014-10-29 | 国家电网公司 | Power distribution network load prediction method and system |
| CN105811402A (en) * | 2016-03-22 | 2016-07-27 | 广西电网有限责任公司电力科学研究院 | Electric power load forecasting system and forecasting method |
| CN112561192A (en) * | 2020-12-23 | 2021-03-26 | 上海亿边科技有限公司 | AI artificial intelligence based power load prediction system |
-
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103295075A (en) * | 2013-04-01 | 2013-09-11 | 沈阳航空航天大学 | Ultra-short-term power load forecasting and early warning method |
| CN104123595A (en) * | 2014-07-22 | 2014-10-29 | 国家电网公司 | Power distribution network load prediction method and system |
| CN105811402A (en) * | 2016-03-22 | 2016-07-27 | 广西电网有限责任公司电力科学研究院 | Electric power load forecasting system and forecasting method |
| CN112561192A (en) * | 2020-12-23 | 2021-03-26 | 上海亿边科技有限公司 | AI artificial intelligence based power load prediction system |
Non-Patent Citations (1)
| Title |
|---|
| 闫群民等: "基于大数据的电力系统短期负荷预测", 《电工技术》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116910681A (en) * | 2023-09-12 | 2023-10-20 | 睿至科技集团有限公司 | Electric power data analysis method and system based on Internet of things |
| CN116910681B (en) * | 2023-09-12 | 2024-01-30 | 睿至科技集团有限公司 | Electric power data analysis method and system based on Internet of things |
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