Highly accurate different horizon-based wind speed forecasting facilitates a
better modern power system. This paper proposed a novel astute hybrid wind
speed forecasting model and applied it to different horizons. The proposed
hybrid forecasting model decomposes the original wind speed data into IMFs
(Intrinsic Mode Function) using Improved Complete Ensemble Empirical Mode
Decomposition with Adaptive Noise (ICEEMDAN). We fed the obtained subseries
from ICEEMDAN to the transformer network. Each transformer network computes the
forecast subseries and then passes to the fusion phase. Get the primary wind
speed forecasting from the fusion of individual transformer network forecast
subseries. Estimate the residual error values and predict errors using a
multilayer perceptron neural network. The forecast error is added to the
primary forecast wind speed to leverage the high accuracy of wind speed
forecasting. Comparative analysis with real-time Kethanur, India wind farm
dataset results reveals the proposed ICEEMDAN-TNF-MLPN-RECS hybrid model's
superior 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 1
and 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 speed
forecasting than state-of-the-art methods and reduces the burden on the power
system engineer.

本文提出了一种新颖的智能混合风速预测模型（ICEEMDAN-TNF-MLPN-RECS），相较于现有技术在不同的时间范围内实现了更高的精确度，降低了电力系统工程师的工作量。