Maximum Incremental Load Recursive Model Based on LS-SVM Considering Accumulated Temperature Effect

Ti Guan; Zheng Xu; Lin Lin; Guilin Zhang; Yujian Jia; Yawen Shi

doi:10.1109/Cybermatics_2018.2018.00143

2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)

Maximum Incremental Load Recursive Model Based on LS-SVM Considering Accumulated Temperature Effect

Year: 2018, Pages: 716-719

DOI Bookmark: 10.1109/Cybermatics_2018.2018.00143

Authors

Ti Guan, Shandong Provincial Electric Power Company, Jinan, 250001, China
Zheng Xu, Shandong Provincial Electric Power Company, Jinan, 250001, China
Lin Lin, Shandong Provincial Electric Power Company, Jinan, 250001, China
Guilin Zhang, Shandong University of Science and Technology, Qingdao, 266590, China
Yujian Jia, Jinan Power Supply Company, Jinan, 250012, China
Yawen Shi, Jinan Power Supply Company, Jinan, 250012, China

Abstract

It is of vital significance to forecast maximum daily load in power system dispatching. First, electric load characteristics in summer are analysed in this paper. Second, maximum incremental load forecasting recursive model is proposed to describe the characteristics of the incremental load, and the mapping relationship between input and output is established by least squares support vector machine (LS-SVM). Moreover, the effect of accumulated temperature is considered under the condition of maximum temperature above 34°C lasting more than two days. Finally, simulation results demonstrates the efficiency of the proposed recursive model.

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Maximum Incremental Load Recursive Model Based on LS-SVM Considering Accumulated Temperature Effect

Authors

Abstract

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