COMBINATED WIND SPEED PREDICTION WITHOUT NEGATIVE CONSTRAINT BASED ON MACHINE LEARNING COUPLED HEURISTIC ALGORITHM AND DATA PREPROCESSING

Fu Tonglin

doi:10.19912/j.0254-0096.tynxb.2024-0194

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (6) : 659-666. DOI: 10.19912/j.0254-0096.tynxb.2024-0194

COMBINATED WIND SPEED PREDICTION WITHOUT NEGATIVE CONSTRAINT BASED ON MACHINE LEARNING COUPLED HEURISTIC ALGORITHM AND DATA PREPROCESSING

Fu Tonglin

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Abstract

In this study, three machine learning models, including artificial neural network （ANN）, support vector machine （SVM）, and extreme learning machine （ELM）, were coupled with ensemble empirical mode decomposition （EEMD） and Grey wolf algorithm （GWO） to construct hybrid models to predict wind speed of Huan County wind farm in Longdong area of Loess Plateau in China. And then the forecasting results of each hybrid model were taken as input variables, and the minimum sum of squares of prediction errors was regarded as the objective function, the combined model without negative constraint theory （NNCT） was proposed to realize the accurate prediction of wind speed in the study area, while the weight of the combined model was optimized by using GWO algorithm. Numerical simulation results show that the GWO-NNCT model can effectively reduce the risk of model selection and has higher prediction accuracy than that of the hybrid models and single machine learning model.

Key words

wind speed / forecasting / machine learning / grey wolf algorithm / ensemble empirical mode decomposition / combination model

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Fu Tonglin. COMBINATED WIND SPEED PREDICTION WITHOUT NEGATIVE CONSTRAINT BASED ON MACHINE LEARNING COUPLED HEURISTIC ALGORITHM AND DATA PREPROCESSING[J]. Acta Energiae Solaris Sinica. 2025, 46(6): 659-666 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0194

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