A combined model based on seasonal autoregressive integrated moving average and modified particle swarm optimization algorithm for electrical load forecasting

doi:10.3233/JIFS-169283

	A combined model based on seasonal autoregressive integrated moving average and modified particle swarm optimization algorithm for electrical load forecasting
	Ma, Tao 1,4; Wang, Fen 1; Wang, Jianzhou 3; Yao, Yukai 2; Chen, Xiaoyun 4
刊名	JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
	2017
卷号	32 期号:5 页码:3447-3459
关键词	Support vector machines seasonal autoregressive integrated moving average combining optimization model electric load forecasting particle swarm optimization algorithm
ISSN号	1064-1246
DOI	10.3233/JIFS-169283
英文摘要	Electric load prediction is an important decision tool in area of electricity economy. Recently researchers have presented innovative models to improve the forecasting accuracy of short-term electricity series, which is valuable in allowing both consumers and electric power sector to make effective planning. This study proposed novel combing optimization model to improve the precision of electric load forecasting and called SSPM. First, taken the advantage of linear prediction for the seasonal autoregressive integrated moving average (SARIMA) model and non-linear prediction for the support vector machines (SVM) model to combine a new model. Next, the produce results by SARIMA model is regarded as linear component and used SVM model for correcting the residual from SARIMA as non-linear component of forecasting results. Third, in order to show the dynamic relationship of linear and non-linear components, the weight variable of alpha(1) and alpha(2) are proposed that optimized by particle swarm optimization (PSO) algorithm with lower error of fitness function, the combining model is applied in the daily electric load data at New South Wales (NSW) in Australia. The experimental results indicate that the proposed optimization model obtains better performance of precise and stability than models of SARIMA and SVM respectively, outperform than conventional artificial neural network (ANN). Although the novel model is applied to electric load forecasting in this paper, it has more scopes for application in a number of areas to gain improvement of forecast accuracy in complex time series.
资助项目	Fundamental Research Fund for Senior School in Ningxia Province[NGY2015124]
WOS研究方向	Computer Science
语种	英语
出版者	IOS PRESS
WOS记录号	WOS:000400023600018
状态	已发表
内容类型	期刊论文
源URL	[http://119.78.100.223/handle/2XXMBERH/33489]
专题	计算机与通信学院
通讯作者	Chen, Xiaoyun
作者单位	1.Ningxia Normal Univ, Sch Math & Comp Sci, Ningxia, Peoples R China 2.Lanzhou Univ Technol, Coll Comp & Commun, Lanzhou, Peoples R China 3.Dongbei Univ Finance & Econ, Sch Stat, Dalian, Peoples R China 4.Lanzhou Univ, Sch Informat Sci & Engn, 222 Tianshui South Rd, Lanzhou 730000, Gansu, Peoples R China
推荐引用方式 GB/T 7714	Ma, Tao,Wang, Fen,Wang, Jianzhou,et al. A combined model based on seasonal autoregressive integrated moving average and modified particle swarm optimization algorithm for electrical load forecasting[J]. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS,2017,32(5):3447-3459.
APA	Ma, Tao,Wang, Fen,Wang, Jianzhou,Yao, Yukai,&Chen, Xiaoyun.(2017).A combined model based on seasonal autoregressive integrated moving average and modified particle swarm optimization algorithm for electrical load forecasting.JOURNAL OF INTELLIGENT & FUZZY SYSTEMS,32(5),3447-3459.
MLA	Ma, Tao,et al."A combined model based on seasonal autoregressive integrated moving average and modified particle swarm optimization algorithm for electrical load forecasting".JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 32.5(2017):3447-3459.