A composite framework coupling multiple feature selection, compound prediction models and novel hybrid swarm optimizer-based synchronization optimization strategy for multi-step ahead short-term wind speed forecasting

Abstract Accurate wind speed prediction plays a vital role in power system in terms of rational dispatching and safe operation. For this purpose, a novel composite framework integrating time varying filter-based empirical mode decomposition (TVF-EMD), fuzzy entropy (FE) theory, singular spectrum analysis (SSA), phase space reconstruction (PSR), compound prediction models adopting kernel-based extreme learning machine (KELM) and convolutional long short-term memory network (ConvLSTM) as well as mutation and hierarchy-based hybrid optimization algorithm, is proposed in this paper. Among the supplementary strategies, TVF-EMD, FE and SSA are employed to achieve non-stationary raw series attenuation, aggregation for approximate IMFs as well as separation of dominant and residuary ingredients from the aggregated IMFs, respectively. Besides, parameters of PSR and KELM as well as wrapper method-based feature selection (FS) for input combination are synchronously optimized by the newly developed swarm optimizer integrating Harris hawks optimization (HHO) and grey wolf optimizer (GWO) with mutation operator and hierarchy strategy. Meanwhile, such hybrid structure is adopted to predict the preprocessed high-frequency components, while the remaining component is predicted by ConvLSTM cells-based deep learning network. Subsequently, the ultimate forecasting results of the raw wind speed are calculated by superimposing the predicted values of all components. Four datasets collected from various sites with two different time intervals and nine relevant contrastive models are carried out to evaluate the proposed approach, where the corresponding results demonstrate that: (1) data preprocessing strategy applying TVF-EMD and FE theory can significantly reduce the time consumption of the entire model without decreasing forecasting performance; (2) SSA-based dominant ingredients extraction can further improve the forecasting capability of combined model; (3) the proposed MHHOGWO can synchronously accomplish parameters optimization and FS effectively, thus improving the forecasting effectiveness of the entire model significantly; (4) the proposed compound prediction models based on KELM and ConvLSTM can exert the capabilities of each model adequately as well as ulteriorly reducing computational requirements.