Photovoltaic Power Prediction Based on NRS-PCC Feature Selection and Multi-Scale CNN-LSTM Network

Abstract

To improve the quality of photovoltaic (PV) data and power prediction accuracy, a PV power prediction method based on neighborhood rough set and Pearson correlation coefficient (NRS-PCC) feature selection and multi-scale convolutional neural networks and long short-term memory (CNN-LSTM) network is proposed. We first calculate the correlation between different PV features based on PCC and select strongly correlated features to cross-multiply to get the fusion features to enrich the data source. Then, dimensionality reduction of the fusion features by NRS. Finally, correlation analysis based on PCC on the dimensionality reduction of the fusion features to screen out the effective features. Furthermore, a multi-scale CNN-LSTM is designed to predict PV power. The output vectors of different convolutional layers are first fused to extract multi-scale features, then the features of different scales are spliced as the input of the LSTM network, and finally, the LSTM network performs regression prediction. The effectiveness of the proposed method is verified on a real PV power dataset.