An Adaptive Deep Learning Based Short-Term Wind Speed Forecasting Model for Variable Terrain Conditions

Wind flow can be highly unpredictable suffer substantial fluctuations in speed and direction due to the shape and height of hills, mountains, and valleys, making accurate wind speed (WS) forecasting essential in complex terrain. Hourly WS data at 50 meters above ground, from MERRA-2, NASA (2015–2021), collected from five Indian wind stations for plain and complex terrain. This article presents a novel and adaptive model for short-term WS forecasting. The article's key contributions are as follows. (a) the partial auto correlation function (PACF) is utilized to minimize the dimension of the set of intrinsic mode functions (IMF), hence reducing training time; (b) The sample entropy (SampEn) was used to calculate the complexity of the reduced set of IMFs. Since a particular deep learning (DL) model-feature-combination was selected based on complexity, the proposed method is adaptive; (c) a novel bidirectional feature-LSTM framework for complicated IMFs has been suggested, resulting in improved forecasting accuracy; (d) the proposed model shows 55.94% superior forecasting performance compared to the persistence, hybrid, ensemble empirical mode decomposition (EEMD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and variational mode decomposition (VMD)-based DL models. It has achieved the lowest prediction variance between simple and complex terrain at 0.70%, ensuring robust forecasting performance. Dimension reduction of IMF's and complexity-based model-feature selection helps reduce the training time by 68.77%, additionally forecasting quality is improved by 58.58% on average. These benefits highlight the model's adaptability, effectiveness, and resilience in addressing WS forecasting challenges on complex terrain.