SHORT-TERM WIND POWER INTERVAL PREDICTION BASED ON MIXED QUANTILE REGRESSION LONG AND SHORT-TERM MEMORYNEURAL NETWORK

Yang Mao; Zhang Shutian; Wang Bo; Yu Xinnan

doi:10.19912/j.0254-0096.tynxb.2023-1676

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (2) : 582-590. DOI: 10.19912/j.0254-0096.tynxb.2023-1676

SHORT-TERM WIND POWER INTERVAL PREDICTION BASED ON MIXED QUANTILE REGRESSION LONG AND SHORT-TERM MEMORYNEURAL NETWORK

Yang Mao¹, Zhang Shutian¹, Wang Bo², Yu Xinnan¹

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Abstract

In order to further improve the accuracy of wind power interval prediction, a short-term interval prediction method of wind power based on mixed quantile regression long-term and short-term memory neural network is proposed. The traditional quantile regression model is improved by considering the three characteristics of compound, smoothing and non-crossing. Firstly, the smoothing function is used instead of the pinball loss function, which makes it easier for long-term and short-term memory neural networks to fit the quantile regression model. Then, a composite objective function is constructed so that it does not train multiple independent models repeatedly under the condition of giving multiple quantiles. thirdly, ReLU penalty function is used for non-cross constraint to avoid the occurrence of quantile crossing. Finally, the improved quantile regression is combined with the long-term and short-term memory neural network and applied to a wind farm in Gansu Province, China. The operation results show that the PICP and PIAW corresponding to thg proposed model increases the PICP increase by 4.17 percentage points and decrease by 2.31 MW respectively at different confidence levels, which verifies the effectiveness of the method.

Key words

wind power / deep learning / interval prediction / compound non-crossing / quantile regression / ReLU penalty function

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Yang Mao, Zhang Shutian, Wang Bo, Yu Xinnan. SHORT-TERM WIND POWER INTERVAL PREDICTION BASED ON MIXED QUANTILE REGRESSION LONG AND SHORT-TERM MEMORYNEURAL NETWORK[J]. Acta Energiae Solaris Sinica. 2025, 46(2): 582-590 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1676

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