Ensemble deep learning model for wind speed prediction

Wind prediction is a significant prerequisite for look-ahead economic load dispatch. Wind speed data is normally exhibiting wide uncertainty nature. Evaluation of this wind data must be accurate to reduce the dangers of system operations. To address this problem, the hybrid approach is developed using long short-term memory (LSTM) network and ensemble empirical mode decomposition (EEMD). This decomposition technique is utilized to divide training data into distinct subseries. The features of uncertainty involved in each sub-series are extracted and utilized to enhance forecasting accuracy by LSTM to learn the characteristics of the decomposed signals. This developed hybrid deep learning model is comprehensively validated with real-time data. The performance validation analysis using statistical error values shows that the developed approach gives superior performance to the existing benchmark approaches.