Load Forecasting Method Based on Stacking Ensemble Learning Under Multiple Meteorological Factors

The ensemble learning method based on the stacking algorithm has shown significant advantages in power system load forecasting under the influence of multiple meteorological factors, solving the problem of weak generalization ability caused by a single model in short-term load forecasting methods. This method combines multiple basic prediction models to improve the accuracy of prediction. This paper first introduces the existing load forecasting methods, then analyzes the principles and advantages of the stacking ensemble learning method, and finally constructs a predictive model for performance evaluation. The research results show that the prediction results obtained by the stacking algorithm are close to the actual load curve and have higher prediction accuracy compared to other models. This method provides a reliable solution for short-term load forecasting of power systems under complex meteorological conditions, which is helpful for better energy management and planning.