Enhanced Forecasting of Wind Energy Production: A Hybrid BPNN-SVR Model for Short-Term Wind Power Forecasting

Abstract

In renewable energy management, the precise prediction of wind power generation remains a major challenge. This study proposes an integrated approach employing an artificial neural network (ANN) and a support vector machine (SVM) to construct a robust short-term prediction model for wind energy output. Central to this research is the utilization of a power station as the subject of analysis, wherein historical meteorological data and concurrent power generation figures form the foundational dataset. Employing a backpropagation (BP) neural network and support vector regression (SVR), the model adeptly synthesizes the data, facilitating predictions with satisfactory accuracy. The hybrid model exhibits a root mean square error (RMSE) of 0.18033, slightly higher than the backpropagation neural network (BPNN) model's 0.1796. However, it exhibits significantly enhanced stability under extreme weather conditions, reducing error fluctuation by 14.3% and maximum error by 18.1%. Given that power dispatch systems prioritize prediction stability over absolute accuracy—as sudden fluctuations can cause outages—this model achieves critical reliability by sacrificing only 0.0007 RMSE, thereby aligning with practical engineering requirements.