Short-term photovoltaic power forecasting based on multiple mode decomposition and parallel bidirectional long short term combined with convolutional neural networks

Photovoltaic (PV) power generation exhibits significant variability due to the unpredictable nature of solar energy and volatile weather conditions. This paper introduces a novel integrated model that combines parallel Bi-directional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN), utilizing multimodal decomposition. The proposed model provides precise photovoltaic (PV) forecasts, essential for optimizing short-term dispatches and scheduling in PV power stations. Firstly, Pearson correlation coefficient is employed to assess the correlation between meteorological data and PV power. Variational mode decomposition (VMD), complementary ensemble empirical mode decomposition (CEEMD), and singular spectrum analysis (SSA) are utilized to decompose the highly correlated features including global radiation and radiation global title. Secondly, employing PV power as output, this study introduces sequences from decomposition methods, temperature, humidity, diffuse radiation, wind direction, and tilted diffuse radiation into the training of the Parallel BiLSTM-CNN (PBiLSTM-CNN) network. Finally, the feasibility of the proposed method is demonstrated by example verification and comparative analysis with alternative methodologies. By employing multiple decomposition methods to extract features, the PBiLSTM-CNN model achieves an average accuracy improvement of approximately 19 % and 37 % in different weather conditions and seasons. Moreover, the implementation of PBiLSTM-CNN results in an enhanced forecasting accuracy of about 48 % and 23 %.