Applying green learning to regional wind power prediction and fluctuation risk assessment

Abstract

Deep Learning (DL) models, such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), have been widely used to predict the intermittency of wind power; however, the non-linear activation functions and backpropagation mechanisms in DL models increase computational complexity and energy consumption. This paper proposes a prediction model based on Green Learning (GL) to reduce energy consumption. The proposed GL model replaces the feature extraction of activation functions with a hybrid feature extraction approach combining categorical and numerical features. We also employ cluster centroids and quantile regression forest for classification/regression to eliminate the need for backpropagation in optimizing hyperparameters. Using Taiwan as a case study, this paper evaluates the risk of fluctuations in regional wind power generation in 2030. In simulations, the proposed GL model achieved excellent accuracy with energy consumption significantly lower than that of DL models. Our analysis also revealed that by 2030, fluctuations in wind power generation during the winter will exceed 40% of the peak supply capacity in the central region, indicating the need to enhance the resilience of regional power systems.