兰州大学机构库 >信息科学与工程学院
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 PublicationNEURAL COMPUTING & APPLICATIONS
ISSN0941-0643
AbstractElectricity, 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.
KeywordPower load and price forecasting Jordan neural network ESN network LSSVM Singular spectrum analysis
DOI10.1007/s00521-020-05113-0
Indexed BySCIE
Language英语
Funding ProjectNatural 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 AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000546885500001
PublisherSPRINGER LONDON LTD
Original Document TypeArticle ; Early Access
Citation statistics
Document Type期刊论文
Identifierhttp://ir.lzu.edu.cn/handle/262010/422029
Collection信息科学与工程学院
Corresponding AuthorZhang, Hairui
AffiliationLanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China
First Author AffilicationSchool of Information Science and Engineering
Corresponding Author AffilicationSchool 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).
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