A combined model based on SSA, neural networks, and LSSVM for short-term electric load and price forecasting

doi:10.1007/s00521-020-05113-0

	A combined model based on SSA, neural networks, and LSSVM for short-term electric load and price forecasting
	Zhang, Hairui; Yang, Y(杨裔); Zhang, Yu; He, Zhaoshuang; Yuan, Wei; Yang, Yong; Qiu, Wan; Li, L(李廉)
	2020-07-09
Source Publication	NEURAL COMPUTING & APPLICATIONS
ISSN	0941-0643
Abstract	Electricity, a kind of clean energy, has been widely used in people's production and daily life. However, it is very difficult to estimate the electricity energy production in advance and store the rest of the electric energy due to the climate, environment, population and other factors. Based on data preprocessing and artificial intelligence optimization algorithm, this paper introduces a combined forecasting method. The proposed method contains six individual methods, and each individual method has its own usage. Singular spectrum analysis (SSA) is adopted to reduce noise from the original data; three individual forecasting methods, Jordan neural network, the echo state network, least squares support vector machine, are applied to obtain the intermediate forecasting results; two optimization algorithms, particle swarm optimization and simulated annealing, are used to optimize the parameters of the combined model. This paper not only validates the superiority of the combined model compared to the single predictive model through the simulation experiments of power load data and electricity price data. The mean absolute percent error (MAPE) of the combined power load and electricity price forecast results are 1.14% and 7.58%, respectively, which are higher than the MAPE error of the corresponding single models prediction results. It has also been verified that the process of eliminating noise by the SSA plays a positive role in the accuracy of the combined forecasting model. In addition, two series of experiments on the power load data lead to two very interesting conclusions. One of the conclusions is that as the size of the test data increases, the prediction accuracy of the model decreases; the other is that the predicted result calculated through the optimized combined weight is better than the combined result calculated using the average weight, and the average weight is used. Weighted combination does not improve the prediction accuracy of a single model.
Keyword	Power load and price forecasting Jordan neural network ESN network LSSVM Singular spectrum analysis
DOI	10.1007/s00521-020-05113-0
Indexed By	SCIE
Language	英语
Funding Project	Natural Science Foundation of PR of China[61073193][61300230] ; Key Science and Technology Foundation of Gansu Province[1102FKDA010] ; Natural Science Foundation of Gansu Province[1107RJZA188] ; Science and Technology Support Program of Gansu Province[1104GKCA037]
WOS Research Area	Computer Science
WOS Subject	Computer Science, Artificial Intelligence
WOS ID	WOS:000546885500001
Publisher	SPRINGER LONDON LTD
Original Document Type	Article ; Early Access
Citation statistics	Cited Times:1[WOS] [WOS Record] [Related Records in WOS]
Document Type	期刊论文
Identifier	http://ir.lzu.edu.cn/handle/262010/422029
Collection	信息科学与工程学院
Corresponding Author	Zhang, Hairui
Affiliation	Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China
First Author Affilication	School of Information Science and Engineering
Corresponding Author Affilication	School of Information Science and Engineering
Recommended Citation GB/T 7714	Zhang, Hairui,Yang, Yi,Zhang, Yu,et al. A combined model based on SSA, neural networks, and LSSVM for short-term electric load and price forecasting[J]. NEURAL COMPUTING & APPLICATIONS,2020.
APA	Zhang, Hairui.,Yang, Yi.,Zhang, Yu.,He, Zhaoshuang.,Yuan, Wei.,...&Li, Lian.(2020).A combined model based on SSA, neural networks, and LSSVM for short-term electric load and price forecasting.NEURAL COMPUTING & APPLICATIONS.
MLA	Zhang, Hairui,et al."A combined model based on SSA, neural networks, and LSSVM for short-term electric load and price forecasting".NEURAL COMPUTING & APPLICATIONS (2020).

Files in This Item:
There are no files associated with this item.

If you have any objections to this item, please fill out the form below and the administrator will contact you as soon as possible.
Content:
Email：	*
Institution:
Verification Code:	Refresh

Any comments and suggestions are welcomed.
Title:	*
Content:
Email：	*
Verification Code:	Refresh

信息科学与工程学院知识库