Interval deep learning architecture with rough pattern recognition and fuzzy inference for short-term wind speed forecasting

In recent decades, wind power is rapidly becoming a significant energy resource due to environmental considerations. The accuracy of wind energy forecasts is closely dependent on the prediction of wind speed time series. In this paper, a novel solution for ultra-short-term and short-term wind speed forecasting is introduced. The proposed method consists of a novel real-valued Deep Belief Network (DBN) with a new Rough feature extraction layer (RFEL) and a Fuzzy Type II Inference System (FT2IS) for robust supervised regression. To learn meaningful unsupervised features from the underlying wind speed data, real-valued input units are computed to better approximate the wind data distribution compared to the existing deep learning models. The proposed differentiable RFEL can be applied to any neural network to efficiently extract noise invariant features. A Takagi-Sugeno-Kang (TSK) system with interval Gaussian membership functions is employed for the supervised forecasting task. The high generalization capacity of the proposed unsupervised feature learning model incorporated into the robust RFEL and FT2IS leads to accurate predictions for highly varying wind speed time series. Numerical results on the Western Wind Dataset reveal significant performance improvements compared to recently proposed Deep Learning Architectures (DLAs), including the DBN, Stacked Autoencoder (SAE), and hybrid methodologies that leverage backtracking and metaheuristic optimization.