The Short-Term Load Forecasting Method Based on Particle Swarm Optimization Support Vector Machine

With the rapid development of power systems, short-term load forecasting is playing an increasingly important role in power scheduling and management. Traditional load forecasting methods, such as time series and regression analysis, have certain limitations when dealing with complex and nonlinear data. This paper proposes a short-term load forecasting method based on Particle Swarm Optimization Support Vector Machine (PSO-SVM).By performing granular analysis on historical load data and leveraging the superior performance of support vector machines, a novel load forecasting model is constructed. The model is evaluated using metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R²) based on training and testing subsets. Comparisons are made with thirteen popular machine learning forecasting algorithms. The experimental results demonstrate that the PSO-SVM model achieves higher accuracy and stability in short-term load forecasting, providing effective support for the optimal scheduling of power systems.