A hybrid VMD based contextual feature representation approach for wind speed forecasting

Accurate wind speed prediction is critical for efficient power system operation, regulation, security analysis, and energy trading. However, the stochastic nature of the wind makes wind speed forecasting (WSF) difficult. Thus, a novel hybrid WSF approach termed VMD-Ts2Vec-SVR comprising variational mode decomposition (VMD), contextual time series representation (Ts2Vec) model, and support vector regression (SVR) is proposed. In the proposed approach, VMD is used to decompose the raw input wind speed for denoising, and extracting the main features of the original series, Ts2Vec model is used to learn the sequential contextual representations in all semantic levels from the denoised series, and SVR is used to predict the future wind speed from the contextual representation. Two experiments are performed for testing the proposed approach using wind speed dataset collected from Leicester, and Portland wind farms. For validation of the proposed approach for different time intervals, it is tested for 5-min, 10-min, 15-min, 30-min, 1-h, and 2-h ahead WSF. The performance of the proposed approach is compared with seven individual models, seven hybrid VMD based models for better validation. Two experiments demonstrated both the proposed approach's superior performance across all time horizons and its viability for the WSF.