基于集合经验模式分解和遗传-高斯过程回归的短期风速概率预测

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Abstract Short-term wind speed probabilistic forecasting is quite significant for grid integration of large wind energy.By now the wind speed forecasting methods are mostly point predictions, whose results cannot describe the randomness of wind energy.A hybrid probabilistic forecasting method based on ensemble empirical mode decomposition (EEMD) and genetic algorithm-Gaussian process regression (GA-GPR) is proposed.Firstly, the EEMD is used to decompose the selected and normalized wind speed time series.Then, the GPR models of each component are established, in which the conjugate gradient algorithm is replaced by GA to optimize the hyper-parameters of covariance functions.Finally, the wind speed probabilistic forecasting results are obtained via superimposing the results of each component, which are compared with the quantile regression algorithm.The simulation results show that the proposed model can enhance the prediction precision, which can be served as a reference for similar engineering projects.

Key words： Ensemble empirical mode decomposition Gaussian process regression genetic algorithm wind speed probabilistic forecasting

Received: 15 November 2014 Published: 29 June 2015

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TM315

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	Gan Di
	Ke Deping
	Sun Yuanzhang
	Cui Mingjian

Cite this article:

Gan Di,Ke Deping,Sun Yuanzhang等. Short-term Wind Speed Probabilistic Forecasting Based on EEMD and Coupling GA-GPR[J]. Transactions of China Electrotechnical Society, 2015, 30(11): 138-147.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2015/V30/I11/138

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