Research on Electric Power Supply Forecasting and Generation Planning Based on LSTM Recurrent Neural Network and Particle Swarm Optimization Algorithm

This paper employs correlation analysis, regression analysis, and LSTM recurrent neural networks to forecast the future trends in electric power supply, providing vital insights for future energy policies. Furthermore, the study investigates the challenges and potential of photovoltaic (PV) power generation in China, focusing on enhancing the efficiency of PV generation, regional disparities in generation capacity, and technological challenges. By establishing a Net Present Value (NPV) calculation model and a dynamic programming model based on the Particle Swarm Optimization (PSO) algorithm, the economic feasibility of PV stations in Xinjiang and the maximum potential of PV power generation in China are thoroughly examined.