New energy power prediction method based on improved wavelet transform and neural network

• Main Authors: LI ZHUOHUAN, MA XIYUAN, LI PENG, CHEN YANSEN, ZHOU CHANGCHENG, ZHANG ZIHAO, CHENG KAI, ZHOU YUE, YAO SENJING, CHEN YUANFENG, BAO TAO
• Format: Patent
• Language: Chinese; English
• Published: 08.07.2022
• 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; ELECTRIC DIGITAL DATA PROCESSING; 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

The invention relates to a new energy power prediction method based on improved wavelet transform and a neural network. The method comprises the steps of obtaining multiple groups of meteorological data samples and historical generated power, determining relevancy between a meteorological numerical value sample under each variable type and the historical generated power, obtaining an initial variable type corresponding to each relevancy threshold value, and according to first training precision corresponding to the initial variable type, obtaining a first training result; determining a target threshold and a target variable type, carrying out clustering processing on multiple groups of meteorological data samples to obtain a first target category to which each group of meteorological data samples belong, carrying out wavelet decomposition on historical power generation power, training multiple initial power prediction models by adopting the target meteorological samples and the power signal samples under each