Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula theory

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• 作者：Yaoyao He^a ; ^b ; ^c ; ^{hy-342501y@163.com} ; Rui Liu^a ; ^b ; ^{lr_0329@163.com} ; Haiyan Li^a ; ^b ; Shuo Wang^c ; Xiaofen Lu^c
• 关键词：Short-term power load probability density forecasting ; Support vector quantile regression ; PI coverage probability ; PI normalized average width ; Copula theory ; Real-time price
• 刊名：Applied Energy
• 出版年：2017
• 出版时间：1 January 2017
• 年：2017
• 卷：185
• 期：part_P1
• 页码：254-266
• 全文大小：2484 K
• 卷排序：185

文摘

Propose a kernel-based support vector quantile regression (SVQR) model. The proposed SVQR can implement power load probability density forecasting. Analyze the diagram of power load and real-time price by Copula theory. PICP and PINAW are adopted to assess the SVQR. The power load forecast accuracy are assessed by three cases of Singapore.