A Novel Hybrid Machine Learning Model for Wind Speed Probabilistic Forecasting

• Published in: Energies (Basel) Vol. 15; no. 19; p. 6942
• Main Authors: Liu, Guanjun, Wang, Chao, Qin, Hui, Fu, Jialong, Shen, Qin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022
• Subjects: Accuracy; Alternative energy sources; Buildings and facilities; Comparative analysis; Decision making; Decision trees; Electricity; Gaussian process; hybrid model; Learning algorithms; Machine learning; Predictions; probabilistic forecasting; Reliability; Statistical analysis; Statistical methods; Sustainable development; uncertainty quantification; Weather forecasting; Wind; Wind power; Wind speed
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en15196942

Accurately capturing wind speed fluctuations and quantifying the uncertainties has important implications for energy planning and management. This paper proposes a novel hybrid machine learning model to solve the problem of probabilistic prediction of wind speed. The model couples the light gradient boosting machine (LGB) model with the Gaussian process regression (GPR) model, where the LGB model can provide high-precision deterministic wind speed prediction results, and the GPR model can provide reliable probabilistic prediction results. The proposed model was applied to predict wind speeds for a real wind farm in the United States. The eight contrasting models are compared in terms of deterministic prediction and probabilistic prediction, respectively. The experimental results show that the LGB-GPR model improves the point forecast accuracy (RMSE) by up to 20.0% and improves the probabilistic forecast reliability (CRPS) by up to 21.5% compared to a single GPR model. This research is of great significance for improving the reliability of wind speed, probabilistic predictions, and the sustainable development of new energy.