Short-term photovoltaic power forecasting method based on convolutional neural network

This research proposes a hybrid model that combines the convolutional neural network (CNN) and the bidirectional long short-term memory network (BiLSTM) to accurately estimate the energy output of a short-term photovoltaic system. Firstly, Pearson correlation analysis is introduced to screen out meteorological factors with high correlation with photovoltaic (PV) power output. Then, a convolutional neural network-bidirectional long short-term memory network (CNN-BiLSTM) combined algorithm is used to extract the characteristics of influencing factors by CNN, and BiLSTM is used for timing prediction. Last but not least, using simulation analysis of data from a particular region in China over the previous two years, the results show that this model reduces training time, improves prediction accuracy, and outperforms the conventional prediction model in terms of the effectiveness of forecasting results, which could also satisfy the demands of the practical application of PV energy generation prediction. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under theCCBY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This research proposes a hybrid model combining CNN and BiLSTM for accurately estimating the energy output of a short-term photovoltaic system. The model utilizes Pearson correlation analysis to screen meteorological factors highly correlated with PV power output, and employs CNN for feature extraction and BiLSTM for timing prediction. Experimental results based on data from a specific region in China demonstrate that this model reduces training time, improves prediction accuracy, and outperforms conventional models in forecasting effectiveness, meeting the practical demands of PV energy generation prediction.