Photovoltaic Power Prediction Fusion Algorithm Based on Improved Feature Selection

To improve the accuracy of photovoltaic power prediction, a fusion prediction model based on improved feature selection was proposed. Firstly, the Pearson correlation coefficient and the information gain method were combined to select characteristic parameters. Then, the dataset was classified to construct the single model of XGBoost, LightGBM and multilayer perceptron (MLP). Finally, a MLP with two hidden layers was used to build a fusion model. The results show that the fusion prediction model has higher prediction accuracy and stronger generalization ability than the single model, and can better meet the needs of short-term photovoltaic power prediction.