Empirical mode decomposition based adaboost-backpropagation neural network method for wind speed forecasting

Abstract

Wind speed forecasting is a popular research direction in renewable energy and computational intelligence. Ensemble forecasting and hybrid forecasting models are widely used in wind speed forecasting. This paper proposes a novel ensemble forecasting model by combining Empirical mode decomposition (EMD), Adaptive boosting (AdaBoost) and Backpropagation Neural Network (BPNN) together. The proposed model is compared with six benchmark models: persistent, AdaBoost with regression tree, BPNN, AdaBoost-BPNN, EMD-BPNN and EMD-AdaBoost with regression tree. The comparisons undergoes several statistical tests and the tests show that the proposed EMD-AdaBoost- BPNN model outperformed the other models significantly. The forecasting error of the proposed model also shows significant randomness.