Short-term forecasting of wind speed using time division ensemble of hierarchical deep neural networks

Wind power in tropical countries like India has great potential as a major source of green energy. However, in order to do proper energy provisioning, there is a need to forecast and estimate the wind speed at windmill locations with an actionable lead-time. Forecasting wind speed at station level is a big challenge using dynamical models as it gives only macro level information. Therefore, use of statistical models is preferably adopted for this purpose. With the recent phenomenal growth of applications in artificial intelligence (AI), it is also possible to use data-driven models based on AI, especially deep learning for short-term forecasting of wind speed. In this paper, we have proposed a novel ensemble forecasting methodology using the long short-term memory (LSTM) model, which is a deep learning approach for time series data analysis. The capability of this approach has been demonstrated using wind speed data obtained from two meteorological stations located at New Delhi in North India and at Bengaluru in South India. We have used the ensemble methodology in two different modes; one is the averaging pooling and other is by using a hierarchical LSTM. The simulations using these models have been validated against the true observations at station scale. The ensemble forecasting method has shown promising results for 3-h early wind speed prediction at both the locations. The results are also compared with two classical statistical methods namely autoregressive and persistence models and two state-of-the-art data-driven models namely support vector machine (SVM) and extreme learning machine (ELM). The capability of the proposed method is demonstrated through various error matrices and found to have better performance. We believe that the proposed method has the potential to improve the short-term wind speed prediction capability at station level. © 2020, Springer Nature Switzerland AG.