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Monte Carlo Method and Quantile Regression for Uncertainty Analysis of Wind Power Forecasting Based on Chaos-LS-SVM

Xin Zhao*, Chao Ge, Fangfang Ji, and Yajuan Liu
International Journal of Control, Automation, and Systems, vol. 19, no. 11, pp.3731-3740, 2021

Abstract : In the paper, the chaos least squares support vector machine algorithm (Chaos-LS-SVM) is applied. To conduct uncertainty analysis of wind power forecasting, two forecasting algorithms of the probabilistic uncertainty analysis based on the Monte Carlo method and the quantile regression analysis based on Chaos-LS-SVM are discussed. The effectiveness and superiority of the two uncertainty analysis methods in the confidence level of 95%, 90%, and 85% are discussed by simulation analysis, and the confidence interval is given in the corresponding confidence level. The prediction interval coverage probability (PICP) and the prediction interval normalized average width (PINAW) of the two uncertainty methods are compared. In the time scale of 1h-ahead, 4h-ahead, and 6h-ahead, the probabilistic uncertainty analysis based on the Monte Carlo method is suitable. In the time scale of 24h-ahead, the quantile regression analysis based on Chaos-LS-SVM is superior.

Keyword : Chaos-LS-SVM, Monte Carlo method, quantile regression, uncertainty analysis

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