Photovoltaic output prediction method based on PCA-SFFS-BiGRU

• Main Authors: WO JIANDONG, QU DIQING, HOU JIANSHENG, YE GUOQING, YE HONG, WANG NING, LI YU, SHENG CHEN, HUANG HONGHUI, XU HAOHUA, HE YAN, ZHANG WENJIE, WU FENG, JI KEQIN, LI FULIN, ZHU JUNXING, WANG KE
• Format: Patent
• Language: Chinese; English
• Published: 19.09.2023
• 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; PHYSICS; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR

The invention discloses a photovoltaic output prediction method based on PCA-SFFS-BiGRU, and the method comprises the steps: obtaining meteorological data of weather forecast, and carrying out the preprocessing; performing principal component analysis on each meteorological parameter to obtain dimensionality-reduced meteorological parameters, and forming a first feature set; further extracting a principal component by using a sequence forward feature selection algorithm to obtain a second feature set; calculating the distance correlation between each reserved principal component and the observed photovoltaic output, and restarting the sequence forward feature selection algorithm by using a plurality of principal components with the highest correlation value to obtain a third feature set; and constructing a bidirectional gating neural network, and optimizing the bidirectional gating neural network by using a particle swarm optimization algorithm to obtain a photovoltaic output prediction model. According to th