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Quantitative combination load forecasting model based on forecasting error optimization

文献类型：期刊论文

作者Deng, Song1; Chen, Fulin2; Wu, Di3; He, Yi4; Ge, Hui5; Ge, Yuan6
刊名COMPUTERS & ELECTRICAL ENGINEERING
出版日期2022-07-01
卷号101页码:11
关键词Gene expression programming Data noise reduction Load forecasting error Combination load forecasting
ISSN号0045-7906
DOI10.1016/j.compeleceng.2022.108125
通讯作者Wu, Di(wudi@cigit.ac.cn)
英文摘要Accurate load forecasting is indispensable in various applications of the electric power industry. Although existing load forecasting methods perform well, they cannot handle complicated scenarios where load-related data are highly random and uncertain. To deal with this issue, A Quantitative Combination Load Forecasting model(QCLF) is proposed. Its main idea is to incorporate the load forecasting errors into the forecasting process as an optimization problem, which can significantly reduce the adverse impacts of random and uncertain load-related data. First, we propose an improved K-Means and Least Square-based Load Forecasting Error Model(LFEM-KLS) to improve the availability and effectiveness of load-related data. Second, we employ gene expression programming (GEP) to optimize the proposed LFEM-KLS to achieve highly accurate load forecasting. Experimental results on three load datasets demonstrate that a QCLF model significantly outperforms other related load forecasting models.
资助项目National Natural Science Foundation of P. R. China[51977113] ; National Natural Science Foundation of P. R. China[62176070] ; National Natural Science Foundation of P. R. China[52077106] ; Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education[GDSC202003/TK220008]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000812902600008
出版者PERGAMON-ELSEVIER SCIENCE LTD
源URL[http://119.78.100.138/handle/2HOD01W0/15952]  
专题中国科学院重庆绿色智能技术研究院
通讯作者Wu, Di
作者单位1.Nanjing Univ Post & Telecommun, Inst Adv Technol, Nanjing 210003, Peoples R China
2.SouthEast Univ, Sch Cyber Sci & Engn, Nanjing 211189, Peoples R China
3.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China
4.Old Domin Univ, Norfolk, VA 23462 USA
5.Nanjing Univ Post & Telecommun, Collegetemp Automation & Artificial Intelligence, Nanjing 210003, Peoples R China
6.Anhui Polytech Univ, Key Lab Adv Percept & Intelligent Control High end, Minist Educ, Wuhu 241000, Peoples R China
推荐引用方式
GB/T 7714		 Deng, Song,Chen, Fulin,Wu, Di,et al. Quantitative combination load forecasting model based on forecasting error optimization[J]. COMPUTERS & ELECTRICAL ENGINEERING,2022,101:11.
APA Deng, Song,Chen, Fulin,Wu, Di,He, Yi,Ge, Hui,&Ge, Yuan.(2022).Quantitative combination load forecasting model based on forecasting error optimization.COMPUTERS & ELECTRICAL ENGINEERING,101,11.
MLA Deng, Song,et al."Quantitative combination load forecasting model based on forecasting error optimization".COMPUTERS & ELECTRICAL ENGINEERING 101(2022):11.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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