Load Forecasting Method Based on PSO-LSTM

Load forecasting plays a crucial role in fields such as energy management and power system planning. Accurate forecasting can effectively reduce energy costs and enhance the stability and reliability of power systems. Traditional forecasting methods struggle to balance forecasting accuracy and efficiency when dealing with complex and variable load data. Although the Long Short-Term Memory network (LSTM) can address the long-term dependency problem in time series and shows certain advantages in load forecasting, the setting of its initial parameters has a significant impact on the forecasting results, and it is prone to getting trapped in local optima. The Particle Swarm Optimization algorithm (PSO), based on swarm intelligence, has powerful global search capabilities and fast convergence. This paper proposes a load forecasting method based on PSO-LSTM, which uses PSO to optimize the parameters of LSTM, thereby enhancing the generalization ability and forecasting accuracy of the model. Through case-based analysis and comparison, it can be seen that compared with traditional forecasting methods, this method effectively improves the accuracy of load forecasting and provides strong support for the efficient and stable operation of power systems.