Wind-speed forecasting model based on DBN-Elman combined with improved PSO-HHT

Abstract

Random and fluctuating wind speeds make it difficult to stabilize the wind-power output, which complicates the execution of wind-farm control systems and increases the response frequency. In this study, a novel prediction model for ultrashort-term wind-speed prediction in wind farms is developed by combining a deep belief network, the Elman neural network, and the Hilbert-Huang transform modified using an improved particle swarm optimization algorithm. The experimental results show that the prediction results of the proposed deep neural network is better than that of shallow neural networks. Although the complexity of the model is high, the accuracy of wind-speed prediction and stability are also high. The proposed model effectively improves the accuracy of ultrashort-term wind-speed forecasting in wind farms.