Photovoltaic power prediction of LSTM model based on Pearson feature selection

Accurate photovoltaic power prediction is the basis for realizing high-efficiency utilization of new energy in large-scale regional power grids. In order to deal with the influence and restriction of many factors such as ambient temperature, relative temperature and solar irradiance in the prediction of photovoltaic power generation, a photovoltaic power prediction method based on Pearson coefficient is proposed in this paper. In the prediction model, Pearson coefficients were used for correlation tests to remove irrelevant features. The remaining features were modeled using a long short-term memory network for regression prediction, and the final conclusions were drawn. The results of the algorithm show that the modified long short-term memory network has improved the mean absolute error and mean squared error of the predicted values. The prediction method, which can achieve short-term prediction of PV power and can reduce the impact of noise on PV power prediction. This research provides important support for the engineering application of energy internet related technologies to guarantee the stable operation of the power grid as well as to arrange reasonable dispatch. (C) 2021 Published by Elsevier Ltd.

Automated summary

Accurate photovoltaic power prediction is essential for efficient utilization of new energy in large-scale regional power grids. This study proposes a prediction method based on Pearson coefficient, which effectively improves prediction accuracy and reduces the impact of noise. The modified long short-term memory network shows better performance in predicting PV power, providing important support for energy internet technologies in ensuring stable grid operation.