A novel hybrid approach based on dynamic adaptive variable-weight optimization for short-term wind speed prediction

One of the most important preconditions for guaranteeing a smooth link between wind farms and the power system is to develop an accurate model for forecasting the wind speed. This paper describes a novel wind speed prediction model based on dynamic adaptive variable-weight optimization theory that considers the relevance of historical observations. The model applies signal preprocessing to recorded wind speed observations using ensemble empirical mode decomposition. The decomposed signals are then subjected to a random noise reduction procedure, which improves the robustness of the prediction model. An autoregressive integrated moving average model, general regression neural network, and long short-term memory are used to recognize the different features of each decomposed subsequence. Brain storm optimization is then applied to further promote the forecasting performance by integrating different forecasting models with dynamically adapted variable weights. To evaluate the prediction capacity of the proposed method, three case studies are conducted. The experimental outcomes reveal that the method presented in this paper provides more satisfactory prediction ability and robustness than other models.