K-nearest neighbors and a kernel density estimator for GEFCom2014 probabilistic wind power forecasting

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• 作者：Yao Zhang ^{zy.3110161019@stu.xjtu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Jianxue Wang ; ^{jxwang@mail.xjtu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Wind power ; Probabilistic forecasting ; Point forecasting ; kk-nearest neighbors ; Kernel density estimator ; Coordinate descent algorithm
• 刊名：International Journal of Forecasting
• 出版年：2016
• 出版时间：July-September 2016
• 年：2016
• 卷：32
• 期：3
• 页码：1074-1080
• 全文大小：1080 K

文摘

Probabilistic forecasts provide quantitative information in relation to energy uncertainty, which is essential for making better decisions on the operation of power systems with an increasing penetration of wind power. On the basis of the class="mathmlsrc">class="formulatext stixSupport mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0169207015001417&_mathId=si1.gif&_user=111111111&_pii=S0169207015001417&_rdoc=1&_issn=01692070&md5=e84979c997254a9effc4fde981bfe9c3" title="Click to view the MathML source">kclass="mathContainer hidden">class="mathCode">$k$-nearest neighbors algorithm and a kernel density estimator method, this paper presents a general framework for the probabilistic forecasting of renewable energy generation, especially for wind power generation. It is a direct and non-parametric approach. Firstly, the class="mathmlsrc">class="formulatext stixSupport mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0169207015001417&_mathId=si1.gif&_user=111111111&_pii=S0169207015001417&_rdoc=1&_issn=01692070&md5=e84979c997254a9effc4fde981bfe9c3" title="Click to view the MathML source">kclass="mathContainer hidden">class="mathCode">$k$-nearest neighbors algorithm is used to find the class="mathmlsrc">class="formulatext stixSupport mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0169207015001417&_mathId=si1.gif&_user=111111111&_pii=S0169207015001417&_rdoc=1&_issn=01692070&md5=e84979c997254a9effc4fde981bfe9c3" title="Click to view the MathML source">kclass="mathContainer hidden">class="mathCode">$k$ closest historical examples with characteristics similar to the future weather condition of wind power generation. Secondly, a novel kernel density estimator based on a logarithmic transformation and a boundary kernel is used to construct wind power predictive density based on the class="mathmlsrc">class="formulatext stixSupport mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0169207015001417&_mathId=si1.gif&_user=111111111&_pii=S0169207015001417&_rdoc=1&_issn=01692070&md5=e84979c997254a9effc4fde981bfe9c3" title="Click to view the MathML source">kclass="mathContainer hidden">class="mathCode">$k$ closest historical examples. The effectiveness of this approach has been confirmed on the real data provided for GEFCom2014. The evaluation results show that the proposed approach can provide good quality, reliable probabilistic wind power forecasts.