标题:Short-TermLoad Forecasting Based on Gaussian Process Regression with Density Peak Clustering and Information Sharing Antlion Optimizer
作者:Zhu, Yaling; Zhang, Bo; Dou, Zhenhai; Zou, Hao; Li, Shengtao; Sun, Kai; Liao, Qingling
作者机构:[Zhu, Yaling; Zhang, Bo; Dou, Zhenhai; Zou, Hao; Sun, Kai; Liao, Qingling] Shandong Univ Technol, Sch Elect & Elect Engn, Zibo 255049, Shandong, Peopl 更多
通讯作者:Dou, Zhenhai
通讯作者地址:Dou, ZH (corresponding author), Shandong Univ Technol, Sch Elect & Elect Engn, Zibo 255049, Shandong, Peoples R China.
来源:IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING
出版年:2020
卷:15
期:9
页码:1312-1320
DOI:10.1002/tee.23198
关键词:Gaussian process regression; density peak clustering; antlion optimizer;; short-term load forecasting
摘要:Short-term load forecasting is essential for reliable and economical operation of the power system. Gaussian process regression (GPR), as a novel machine learning prediction algorithm, has become one of the commonly used algorithms in the field of short-term load prediction because it can fully consider the nonlinear and uncertain characteristics of short-term load sequence. However, due to the unreasonable selection of training set and the inaccurate acquisition of hyper parameters, the prediction accuracy of GPR decreases. In order to improve the accuracy of power system load forecasting, a GPR prediction model based on density peak clustering (DPC) and information sharing antlion optimizer (ISALO) is proposed. Firstly, the DPC is used to find similar days from the historical load data to construct a more reasonable training set. Then, the ISALO, an improved ALO by introducing information sharing mechanism, is used to optimize the hyper parameters of the GPR. Experiments show that the DPC-ISALO-GPR model has a 3.33 and 1.22% reduction in mean absolute percentage error compared to Back Propagation Neural Network (BP) and support vector machines, which is suitable for engineering practical applications. (c) 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
收录类别:EI;SCOPUS;SCIE
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088311306&doi=10.1002%2ftee.23198&partnerID=40&md5=c8d7e1dc21090bc8471e095925857974
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