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Highly accurate different horizon-based wind speed forecasting facilitates abetter modern power system. This paper proposed a novel astute hybrid windspeed forecasting model and applied it to different horizons. The proposedhybrid forecasting model decomposes the original wind speed data into IMFs(Intrinsic Mode Function) using Improved Complete Ensemble Empirical ModeDecomposition with Adaptive Noise (ICEEMDAN). We fed the obtained subseriesfrom ICEEMDAN to the transformer network. Each transformer network computes theforecast subseries and then passes to the fusion phase. Get the primary windspeed forecasting from the fusion of individual transformer network forecastsubseries. Estimate the residual error values and predict errors using amultilayer perceptron neural network. The forecast error is added to theprimary forecast wind speed to leverage the high accuracy of wind speedforecasting. Comparative analysis with real-time Kethanur, India wind farmdataset results reveals the proposed ICEEMDAN-TNF-MLPN-RECS hybrid model'ssuperior performance with MAE=1.7096*10^-07, MAPE=2.8416*10^-06,MRE=2.8416*10^-08, MSE=5.0206*10^-14, and RMSE=2.2407*10^-07 for case study 1and MAE=6.1565*10^-07, MAPE=9.5005*10^-06, MRE=9.5005*10^-08,MSE=8.9289*10^-13, and RMSE=9.4493*10^-07 for case study 2 enriched wind speedforecasting than state-of-the-art methods and reduces the burden on the powersystem engineer.

Hybrid Transformer Network for Different Horizons-based Enriched Wind Speed Forecasting