Day-ahead load probability density forecasting using monotone composite quantile regression neural network and kernel density estimation

Zhang Wanying; He Yaoyao; Yang Shanlin

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Day-ahead load probability density forecasting using monotone composite quantile regression neural network and kernel density estimation

机译：Day-ahead load probability density forecasting using monotone composite quantile regression neural network and kernel density estimation

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摘要

Due to the aggregation of renewable resources and the fluctuation of demand side, exploring more advanced approaches to maintain the accuracy of short-term load forecasting has become a momentous task. The neural network-based quantile regression probability density forecasting can reveal the prediction uncertainty, which is critical in building energy-saving and reliable power grid. However, quantile curves estimated by previous quantile regression probability density forecasting model may cross each other, violating the basic properties of quantile and jeopardizing the flexibility of modeling. For counteracting this disadvantage, a probability density prediction method based on monotone composite quantile regression neural network and Gaussian kernel function (MCQRNNG) for day-ahead load is proposed. The proposed method guarantees non-crossing by simultaneously estimating multiple nonlinear quantile regression functions to obtain better estimation accuracy. Besides, three-dimensional grid search is adopted to adaptively determine the optimal network structure. Taking real load data carrying quantile crossing from Ottawa in Canada, Baden-Wurttemberg in Germany and a certain region in China, the performance of the MCQRNNG model is verified from three aspects of point, interval and probability prediction. Results show that the proposed model significantly outperforms the comparison model on the basis of effectively extricating the quantile crossing for power load forecasting.

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来源
《Electric Power Systems Research》 |2021年第12期|107551.1-107551.15|共15页
作者
Zhang Wanying; He Yaoyao; Yang Shanlin;
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作者单位

Hefei Univ Technol, Sch Management, Hefei 230009, Peoples R China|Hefei Univ Technol, Minist Educ, Key Lab Proc Optimizat & Intelligent Decis Making, Hefei 230009, Peoples R China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
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关键词
Probability density forecasting; Multiple quantiles regression; Non-crossing curves; Monotone composite quantile regression neural network (MCQRNN);

Day-ahead load probability density forecasting using monotone composite quantile regression neural network and kernel density estimation

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