Solar irradiance prediction model based on CNN-Bi-LSTM-Attention

Abstract

The instability of solar irradiance can lead to power fluctuations in photovoltaic power generation systems, which can have a negative impact on the stability of the power system network. To address this problem, a new solar irradiance prediction model is proposed in this article, aiming to improve the stability of photovoltaic power generation systems and ensure the normal operation of the power grid network. Firstly, data pre-processing is performed to select input data, and then the data is fed into a convolutional neural network for fusion and feature extraction of multiple variables. The input of a bidirectional long short-term memory network model is set as the extracted results. Finally, the output of the network model is integrated into an attention mechanism to allocate weights and obtain the final prediction of solar irradiance. The predictive performance of the proposed model was validated using all-weather observation data from the publicly available online dataset in 2020. Compared with the control model, the proposed model showed significant improvements in average absolute error and other indicators.