Particle-swarm optimization of ensemble neural networks with negative correlation learning for forecasting short-term wind speed of wind farms in western China

Abstract In many countries, wind power is being developed as a primary source of renewable energy. However, it is difficult to describe and forecast wind speed features, which are stochastic and intermittently complex. Moreover, it is important both to obtain precise forecasts of wind speed for wind power generation and to determine their contribution to regional economies. In this paper, we propose a novel approach for wind speed forecasting. The proposed model is a hybrid that uses the wavelet analysis technique (WAT) for denoising, a negative correlation learning neural network (NCL-NN) ensemble, and an ensemble structure optimized using particle-swarm optimization (PSO). We name the approach WAT-NCL-PSO. We define a novel fitness function to optimize the performance of the NCL-NN ensemble. Then, we rebuild the new NCL-NN ensemble using the contribution rates (CRs) by applying the PSO algorithm. We compiled wind speed datasets from six wind power generator sites in western China and used them to test the performance of the proposed model. Further, we analyzed the model’s performance in terms of its robustness and time complexity. Finally, to illustrate the effectiveness of the proposed approach, we compared its performance with that of the back-propagation neural network (BPNN), support vector machine (SVM), bagging, AdaBoost, random forest (RF), long short-term memory (LSTM), seasonal autoregressive integrated moving average (SARIMA), SVM with ensemble empirical mode decomposition (EEMD-SVM), and NCL-NN with wavelet denoising (WAT-NCL) models. The simulation results demonstrate that the performance of the WAT-NCL-PSO model is superior to other methods in terms of forecasting accuracy for short-term wind speeds.