CN112561192A - AI artificial intelligence based power load prediction system - Google Patents
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- CN112561192A CN112561192A CN202011534326.4A CN202011534326A CN112561192A CN 112561192 A CN112561192 A CN 112561192A CN 202011534326 A CN202011534326 A CN 202011534326A CN 112561192 A CN112561192 A CN 112561192A
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Abstract
The invention discloses an AI artificial intelligence-based power load prediction system, which comprises a motor data collection module, wherein the connecting end of the power data collection module is connected with a training set integration module, the connecting end of the training set integration module is connected with an AI data learning module, the connecting end of the AI data learning module is connected with an AI prediction module, the connecting end of the AI prediction module is connected with a prediction result analysis module, the connecting end of the prediction result analysis module is connected with a weight distribution module, and the power data collection module is used for collecting power use data. According to the invention, the model weight unit is used for debugging the weight ratio of the power load prediction model, so that the prediction result of the power load prediction model is closer to the real result, and meanwhile, the comprehensive weight unit is used for debugging the weight ratio calculated by the prediction result of the comprehensive analysis unit, so that the comprehensive data calculated by the comprehensive analysis unit is closer to the real result.
Description
Technical Field
The invention relates to the field of power load prediction, in particular to an AI artificial intelligence-based power load prediction system.
Background
The power load prediction is an important component of power system planning and is also the basis of economic operation of a power system, the power load prediction is extremely important for power system planning and operation, the development change of future loads can be known through the load prediction, power utilization improvement measures on a demand side are put forward in a targeted manner, and a load curve is improved, so that power dispatching is optimized, relevant workers can perform power generation, transportation and power utilization through prediction results, evaluate distribution and establish an effective plan, and the power load prediction method is beneficial to reducing power generation cost and achieving the purposes of energy conservation and emission reduction. Meanwhile, the electric power department can find potential hidden dangers of the electric power system through the load forecasting system, timely eliminate the hidden dangers, output stable electric power for users and ensure the reliable operation of the electric power system.
Most of the traditional prediction means used by the power load prediction system at present have the defects of laggard technology and low prediction precision, and are difficult to meet the requirement of high prediction precision at present.
Therefore, it is necessary to invent an AI artificial intelligence based power load prediction system to solve the above problems.
Disclosure of Invention
The invention aims to provide an AI artificial intelligence-based power load prediction system, which debugs the weight ratio of a power load prediction model through a model weight unit to enable the prediction result of the power load prediction model to be closer to the real result, and simultaneously debugs the weight ratio calculated by the prediction result of an integrated analysis unit through an integrated weight unit to enable the integrated data calculated by the integrated analysis unit to be closer to the real result, thereby improving the accuracy of power load prediction and solving the defects in the technology.
In order to achieve the above purpose, the invention provides the following technical scheme: an AI artificial intelligence-based power load prediction system comprises a motor data collection module, wherein the connecting end of the power data collection module is connected with a training set integration module, the connecting end of the training set integration module is connected with an AI data learning module, the connecting end of the AI data learning module is connected with an AI prediction module, the connecting end of the AI prediction module is connected with a prediction result analysis module, and the connecting end of the prediction result analysis module is connected with a weight distribution module;
the power data collection module is used for collecting power usage data;
the training set integration module is used for comprehensively arranging the electric power use data collected by the electric power data collection module and making sample data by using the electric power use data;
the AI data learning module trains a prediction model through learning sample data;
the AI prediction module predicts the power load through a prediction model;
the prediction result analysis module compares and analyzes the prediction result with the actual power load;
and the weight distribution module finely adjusts the weight ratio of the prediction process according to the analysis of the prediction result analysis module.
Preferably, the power data collection module includes a real-time weather unit and a real-time power load unit, the real-time weather unit is used for collecting and recording real-time weather data, and the real-time power load unit is used for recording real-time power load data.
Preferably, the training set integration module comprises a data storage unit and a sample integration unit, the data storage unit is used for storing meteorological data, power load data and corresponding time information, and the sample integration unit is used for integrating the data stored by the data storage unit and manufacturing training sample data.
Preferably, the AI data learning module includes a prediction model unit and a model learning unit, the prediction model unit is configured to extract data of the data storage unit and establish the power load prediction model according to the data, and the model learning unit is configured to extract training sample data produced by the sample integration unit and train and learn the power load prediction model established by the prediction model unit.
Preferably, the AI prediction module includes a big data prediction unit that predicts the power load through a power load prediction model and meteorological data, and a short term prediction unit that predicts the power load through the power load prediction model and real-time power load data.
Preferably, the prediction result analysis module includes an integrated analysis unit and a result comparison unit, the integrated analysis unit performs integrated analysis on the prediction results of the big data prediction unit and the short term prediction unit, calculates the prediction results, obtains integrated prediction data and outputs the integrated prediction data, and the result comparison unit performs comparison analysis on the prediction results of the big data prediction unit and the short term prediction unit and the integrated prediction data with the actual results respectively, and calculates the difference between the prediction results and the actual results.
Preferably, the weight distribution module includes a model weight unit and an integrated weight unit, the model weight unit performs comparative analysis on the prediction results and the actual results of the big data prediction unit and the short term prediction unit through a result comparison unit, and debugs the weight ratio of the power load prediction model, and the integrated weight unit performs comparative analysis on the integrated prediction data and the actual results through the result comparison unit, and debugs the weight ratio calculated by the prediction results of the integrated analysis unit.
In the technical scheme, the invention provides the following technical effects and advantages:
the power load is predicted through a meteorological data and power load prediction model by a big data prediction unit, the power load is predicted through real-time power load data and a power load prediction model by a short-term prediction unit, then two prediction results are comprehensively analyzed by a comprehensive analysis unit, comprehensive prediction data are calculated and are output, the weight ratio of the power load prediction model is debugged through the comparison analysis result of a result comparison unit by a model weight unit, so that the prediction result of the power load prediction model is closer to a real result, the precision of the power load prediction model is further improved, and the weight ratio calculated by the prediction result of the comprehensive analysis unit is debugged through the comparison analysis result of the result comparison unit by the comprehensive weight unit, so that the comprehensive data calculated by the comprehensive analysis unit is closer to the real result, the accuracy of the power load prediction is further improved, so that the prediction accuracy is improved to the maximum extent.
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In order to more clearly illustrate the embodiments of the present application or technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings can be obtained by those skilled in the art according to the drawings.
FIG. 1 is a system diagram of the present invention;
FIG. 2 is a unit diagram of the present invention.
Description of reference numerals:
the system comprises a power data collection module 1, a training set integration module 2, a 3 AI data learning module, a 4 AI prediction module, a 5 prediction result analysis module, a 6 weight distribution module, a 7 real-time meteorological unit, an 8 real-time power load unit, a 9 data storage unit, a 10 sample integration unit, a 11 prediction model unit, a 12 model learning unit, a 13 big data prediction unit, a 14 short-term prediction unit, a 15 integration analysis unit, a 16 result comparison unit, a 17 model weight unit and an 18 integration weight unit.
Detailed Description
In order to make the technical solutions of the present invention better understood, those skilled in the art will now describe the present invention in further detail with reference to the accompanying drawings.
The invention provides an AI artificial intelligence based power load prediction system as shown in figures 1-2, which comprises a motor data collection module, wherein the connection end of the power data collection module 1 is connected with a training set integration module 2, the connection end of the training set integration module 2 is connected with an AI data learning module 3, the connection end of the AI data learning module 3 is connected with an AI prediction module 4, the connection end of the AI prediction module 4 is connected with a prediction result analysis module 5, and the connection end of the prediction result analysis module 5 is connected with a weight distribution module 6;
the power data collection module 1 is used for collecting power usage data;
the training set integration module 2 is used for comprehensively organizing the electric power use data collected by the electric power data collection module 1 and making sample data by using the electric power use data;
the AI data learning module 3 trains a prediction model through learning sample data;
the AI prediction module 4 predicts the power load through a prediction model;
the prediction result analysis module 5 compares and analyzes the prediction result with the actual power load;
and the weight distribution module 6 is used for finely adjusting the weight ratio of the prediction process according to the analysis of the prediction result analysis module 5.
Further, in the above technical solution, the power data collection module 1 includes a real-time weather unit 7 and a real-time power load unit 8, where the real-time weather unit 7 is configured to collect and record real-time weather data, and the real-time power load unit 8 is configured to record real-time power load data.
Further, in the above technical solution, the training set integration module 2 includes a data storage unit 9 and a sample integration unit 10, the data storage unit 9 is used for storing meteorological data, power load data and corresponding time information, and the sample integration unit 10 is used for integrating data stored in the data storage unit 9 and making training sample data.
Further, in the above technical solution, the AI data learning module 3 includes a prediction model unit 11 and a model learning unit 12, the prediction model unit 11 is configured to extract data of the data storage unit 9 and establish a power load prediction model according to the data, and the model learning unit 12 is configured to extract training sample data produced by the sample integration unit 10 and train and learn the power load prediction model established by the prediction model unit 11.
Further, in the above technical solution, the AI prediction module 4 includes a big data prediction unit 13 and a short term prediction unit 14, the big data prediction unit 13 predicts the power load through a power load prediction model and meteorological data, and the short term prediction unit 14 predicts the power load through the power load prediction model and real-time power load data.
Further, in the above technical solution, the prediction result analysis module 5 includes an integrated analysis unit 15 and a result comparison unit 16, the integrated analysis unit 15 performs integrated analysis on the prediction results of the big data prediction unit 13 and the short term prediction unit 14, calculates the prediction results to obtain integrated prediction data and outputs the integrated prediction data, and the result comparison unit 16 performs comparison analysis on the prediction results of the big data prediction unit 13 and the short term prediction unit 14 and the integrated prediction data with the actual results respectively to calculate the difference between the prediction results and the actual results.
Further, in the above technical solution, the weight distribution module 6 includes a model weight unit 17 and an integrated weight unit 18, the model weight unit 17 debugs the weight ratio of the power load prediction model by comparing and analyzing the prediction results and the actual results of the big data prediction unit 13 and the short term prediction unit 14 by the result comparison unit 16, and the integrated weight unit 18 debugs the weight ratio calculated by the prediction result of the integrated analysis unit 15 by comparing and analyzing the integrated prediction data and the actual results by the result comparison unit 16.
The implementation mode is specifically as follows: firstly, real-time meteorological data and real-time power load data are collected and recorded through a real-time meteorological unit 7 and a real-time power load unit 8, then the real-time meteorological data and the real-time power load data and corresponding time information are stored in a data storage unit 9, the data stored in the data storage unit 9 are analyzed and processed through a sample integration unit 10, the stored data are made into training sample data, the stored data in the data storage unit 9 are extracted through a prediction model unit 11, the stored data are integrated and a power load prediction model is built, then the training sample data made by the sample integration unit 10 are extracted, the training sample data are used for training and learning a power load prediction model, so that the precision of the power load prediction model is improved, and then a big data prediction unit 13 predicts the power load through the meteorological data and the power load prediction model, meanwhile, the short-term prediction unit 14 predicts the power load through the real-time power load data and the power load prediction model, then the comprehensive analysis unit 15 comprehensively analyzes two prediction results, then the comprehensive prediction data is calculated and output, the result comparison unit 16 respectively compares the prediction results of the big data prediction unit 13 and the short-term prediction unit 14 and the comprehensive prediction data with the actual results to comprehensively compare the difference between the prediction results and the actual results, the model weighting unit 17 debugs the weight ratio of the power load prediction model through the comparative analysis results of the result comparison unit 16 to enable the prediction results of the power load prediction model to be closer to the actual results, the precision of the power load prediction model is further improved, and meanwhile, the comprehensive weighting unit 18 compares the analysis results of the result comparison unit 16, the embodiment specifically solves the problems that the conventional prediction means used by most power load prediction systems in the prior art is backward in technology, low in prediction precision and difficult to meet the current high prediction precision requirement.
While certain exemplary embodiments of the present invention have been described above by way of illustration only, it will be apparent to those of ordinary skill in the art that the described embodiments may be modified in various different ways without departing from the spirit and scope of the invention. Accordingly, the drawings and description are illustrative in nature and should not be construed as limiting the scope of the invention.
Claims (7)
1. The utility model provides an electric power load prediction system based on AI artificial intelligence, includes motor data collection module, its characterized in that: the power data collection system is characterized in that a connecting end of the power data collection module (1) is connected with a training set integration module (2), a connecting end of the training set integration module (2) is connected with an AI data learning module (3), a connecting end of the AI data learning module (3) is connected with an AI prediction module (4), a connecting end of the AI prediction module (4) is connected with a prediction result analysis module (5), and a connecting end of the prediction result analysis module (5) is connected with a weight distribution module (6);
the power data collection module (1) is used for collecting power usage data;
the training set integration module (2) is used for comprehensively organizing the electric power use data collected by the electric power data collection module (1) and making sample data by using the electric power use data;
the AI data learning module (3) trains a prediction model through learning sample data;
the AI prediction module (4) predicts the power load through a prediction model;
the prediction result analysis module (5) compares and analyzes the prediction result with the actual power load;
and the weight distribution module (6) finely adjusts the weight ratio of the prediction process according to the analysis of the prediction result analysis module (5).
2. The AI artificial intelligence-based power load prediction system of claim 1, wherein: the electric power data collection module (1) comprises a real-time meteorological unit (7) and a real-time electric power load unit (8), wherein the real-time meteorological unit (7) is used for collecting and recording real-time meteorological data, and the real-time electric power load unit (8) is used for recording real-time electric power load data.
3. The AI artificial intelligence-based power load prediction system of claim 1, wherein: the training set integration module (2) comprises a data storage unit (9) and a sample integration unit (10), wherein the data storage unit (9) is used for storing meteorological data, power load data and corresponding time information, and the sample integration unit (10) is used for integrating the data stored by the data storage unit (9) and making training sample data.
4. The AI artificial intelligence based power load prediction system of claim 3, wherein: the AI data learning module (3) comprises a prediction model unit (11) and a model learning unit (12), wherein the prediction model unit (11) is used for extracting data of the data storage unit (9) and establishing a power load prediction model according to the data, and the model learning unit (12) is used for extracting training sample data made by the sample synthesis unit (10) and training and learning the power load prediction model established by the prediction model unit (11).
5. The AI artificial intelligence-based power load prediction system of claim 1, wherein: the AI prediction module (4) comprises a big data prediction unit (13) and a short-term prediction unit (14), wherein the big data prediction unit (13) predicts the power load through a power load prediction model and meteorological data, and the short-term prediction unit (14) predicts the power load through the power load prediction model and real-time power load data.
6. The AI artificial intelligence based power load prediction system of claim 5, wherein: the prediction result analysis module (5) comprises an integrated analysis unit (15) and a result comparison unit (16), the integrated analysis unit (15) performs integrated analysis on the prediction results of the big data prediction unit (13) and the short-term prediction unit (14) and calculates the prediction results to obtain integrated prediction data and outputs the integrated prediction data, and the result comparison unit (16) performs comparative analysis on the prediction results of the big data prediction unit (13) and the short-term prediction unit (14) and the integrated prediction data with the actual results respectively to calculate the difference between the prediction results and the actual results.
7. The AI artificial intelligence based power load prediction system of claim 6, wherein: the weight distribution module (6) comprises a model weight unit (17) and an integrated weight unit (18), the model weight unit (17) conducts comparative analysis on the prediction results and the actual results of the big data prediction unit (13) and the short-term prediction unit (14) through a result comparison unit (16) to debug the weight ratio of the power load prediction model, and the integrated weight unit (18) conducts comparative analysis on the integrated prediction data and the actual results through the result comparison unit (16) to debug the weight ratio calculated by the prediction results of the integrated analysis unit (15).
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Application publication date: 20210326 |