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Photovoltaic power generation is one of the most promising forms of renewable energy generation. The forecasting of photovoltaic power is of great significance for ensuring the stability of the power grid system. In this article, a combined forecasting model based on maximum overlapping discrete wavelet transform, particle swarm optimization and bidirectional long short-term memory network is proposed to forecast photovoltaic power. Firstly, the signal component analysis of photovoltaic data is carried out using MODWT, and the frequency principal component is extracted as BiLSTM training data through signal reconstruction. Secondly, considering the fact that the number of network hidden layers and the learning rate affect the network performance, in order to obtain the most suitable network model parameters for the case data, PSO optimization is used to procure the optimal number of network hidden layers and the optimal learning rate. Finally, the parameters are assigned to BiLSTM which is then retrained to get the final forecasting results. Taking the real photovoltaic data of a certain area as an example, compared with the other eight forecasting models, it is verified that the proposed model has the best forecasting performance.
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