Short-Term Forecasting of Photovoltaic Power Generation Based on Feature Selection and Bias Compensation-LSTM Network

In this paper, a hybrid model that considers both accuracy and efficiency is proposed to predict photovoltaic (PV) power generation. To achieve this, improved forward feature selection is applied to obtain the optimal feature set, which aims to remove redundant information and obtain related features, resulting in a significant improvement in forecasting accuracy and efficiency. The prediction error is irregularly distributed. Thus, a bias compensation-long short-term memory (BC-LSTM) network is proposed to minimize the prediction error. The experimental results show that the new feature selection method can improve the prediction accuracy by 0.6% and the calculation efficiency by 20% compared to using feature importance identification based on LightGBM. The BC-LSTM network can improve accuracy by 0.3% using about twice the time compared with the LSTM network, and the hybrid model can further improve prediction accuracy and efficiency based on the BC-LSTM network.

Automated summary

In this paper, a hybrid model that considers both accuracy and efficiency is proposed for predicting photovoltaic power generation. Improved feature selection and a bias compensation-long short-term memory network are used to enhance prediction accuracy and efficiency. Experimental results show significant improvements compared to traditional methods.