Multi-layer BiLSTM Photovoltaic Prediction Study Based on Improved PSO

In order to improve prediction accuracy of photovoltaic (PV) power output, this paper proposes a multilayer bi-directional long-and short-term memory (BiLSTM) based on improved particle swarm (PSO) for PV power prediction. Firstly, according to timing-related characteristics of PV power generation, a multilayer BiLSTM model is constructed to learn hidden PV power data information. Secondly, in order to improve efficiency of model training, improved PSO algorithm is constructed. Particle information is initialized using inverse learning and dynamic inertia weighting strategy is utilized to improve performance of algorithm parameter tuning. Finally, multilayer BiLSTM model based on improved PSO predicts PV power. Simulation example analysis shows that the proposed method can significantly improve prediction accuracy of PV power.