Particle swarm algorithm-based power sale quantity accurate prediction method for power distribution area
The invention discloses a particle swarm algorithm-based power distribution area power sale quantity accurate prediction method. The method comprises the following steps of S1, obtaining and processing historical power sale data of an area; s2, dividing the historical data into training group data a...
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| creator | GE YUDA LI DAREN ZHOU TAIBIN ZHOU YANG SHEN JIE HUANG GUANGQUN GAN ZEHONG CHEN MAOJIA |
| description | The invention discloses a particle swarm algorithm-based power distribution area power sale quantity accurate prediction method. The method comprises the following steps of S1, obtaining and processing historical power sale data of an area; s2, dividing the historical data into training group data and test group data, classifying the historical data, and sorting the classified data according to dates; s3, selecting corresponding independent classification items through a particle swarm algorithm, and establishing a prediction model according to the classification items and the training group data; and S4, the prediction model is tested through the test group data, and calculation deviation is known and corrected. According to the method, the previous electricity sale quantity of the transformer area is acquired and processed, so that the influence of each independent item on the electricity sale quantity is conveniently and accurately calculated, meanwhile, the condition of each independent item in the predic |
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The method comprises the following steps of S1, obtaining and processing historical power sale data of an area; s2, dividing the historical data into training group data and test group data, classifying the historical data, and sorting the classified data according to dates; s3, selecting corresponding independent classification items through a particle swarm algorithm, and establishing a prediction model according to the classification items and the training group data; and S4, the prediction model is tested through the test group data, and calculation deviation is known and corrected. 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The method comprises the following steps of S1, obtaining and processing historical power sale data of an area; s2, dividing the historical data into training group data and test group data, classifying the historical data, and sorting the classified data according to dates; s3, selecting corresponding independent classification items through a particle swarm algorithm, and establishing a prediction model according to the classification items and the training group data; and S4, the prediction model is tested through the test group data, and calculation deviation is known and corrected. According to the method, the previous electricity sale quantity of the transformer area is acquired and processed, so that the influence of each independent item on the electricity sale quantity is conveniently and accurately calculated, meanwhile, the condition of each independent item in the predic</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES HANDLING RECORD CARRIERS PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
| title | Particle swarm algorithm-based power sale quantity accurate prediction method for power distribution area |
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