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题名: A Combined Model Based on Neural Networks, LSSVM and Weight Coefficients Optimization for Short-Term Electric Load Forecasting
作者: Li, CH; He, ZS; Wang, YC
收录类别: CPCI-S
出版日期: 2016
会议名称: 17th International Conference on Web-Age Information Management (WAIM)
会议日期: JUN 03-05, 2016
会议地点: Nanchang, PEOPLES R CHINA
英文摘要: As an essential energy in the daily life, electricity which is difficult to store has become a hot issue in power system. Short-term electric load forecasting (STLF) which is regarded as a vital tool helps electric power companies make good decisions. It can not only guarantee adequate energy supply but also avoid unnecessary wastes. Although there exists quantity of forecasting methods, most of them are not able to make accurate predictions. Therefore, a forecasting method with high accuracy is particularly important. In this paper, a combined model based on neural networks and least squares support vector machine (LSSVM) is proposed to improve the forecasting accuracy. At first, three forecasting methods named generalized regression neural network (GRNN), Elman, LSSVM are utilized to forecast respectively. Among them, simulate anneal (SA) arithmetic is used to optimize GRNN. Then, SA is employed to determine the weight coefficients of each individual method. At last, multiplying all the three forecasting results with the corresponding weights, the final result of the combined model can be attained. Using the electric load data of Queensland of Australia as experimental simulation, case studies show that the proposed combined model works well for STLF and the results prove more accurate.
关键词: Generalized regression neural network ; Elman ; Least squares support vector machine simulated annealing algorithm ; Short-term electric load forecasting
作者部门: [Li, Caihong ; He, Zhaoshuang ; Wang, Yachen] Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China
通讯作者: Li, CH (reprint author), Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China.
学科分类: Computer Science
会议录: WEB-AGE INFORMATION MANAGEMENT
卷号: 9998
页码: 109-121
出版者: SPRINGER
出版地: CHAM
语种: 英语
DOI: 10.1007/978-3-319-47121-1_10
ISSN号: 0302-9743
WOS记录号: WOS:000389726800010
IR记录号: WOS:000389726800010
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内容类型: 会议论文
URI标识: http://ir.lzu.edu.cn/handle/262010/189716
Appears in Collections:信息科学与工程学院_会议论文

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Recommended Citation:
Li, CH,He, ZS,Wang, YC. A Combined Model Based on Neural Networks, LSSVM and Weight Coefficients Optimization for Short-Term Electric Load Forecasting[C]. 见:17th International Conference on Web-Age Information Management (WAIM). Nanchang, PEOPLES R CHINA. JUN 03-05, 2016.
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