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Multi-step least squares support vector machine modeling approach for forecasting short-term electricity demand with application

Electricity demand forecasting plays a crucial role in the operation of electrical power systems because it can provide management decisions related to load switching and power grid. Thus, there have been models developed to estimate the electricity demand. However, inaccurate demand forecasting may raise the operating cost of electric power sector, which means that it would waste considerable money. In this paper, a novel modeling framework was proposed for forecasting electricity demand. Sample entropy was developed to identify the nonlinearity and uncertainty in the original time series, after that redundant noise was removed through a decomposition technique. Besides, the most optimal modes of original series and the optimal input form of the model were determined by the feature selection method. Finally, electricity demand series can be conducted forecasting through least squares support vector machine tuned by multi-objective sine cosine optimization algorithm. The case studies of Australia demonstrated that the proposed framework can ensure high accuracy and strong stability. Thus, it can be considered as a useful tool for electricity demand forecasting.


Keywords: Sample entropy, Multi-objective sine cosine algorithm, Least squares support vector machine, Variational mode decomposition, Multi-step forecasting.


Li, R.;  Chen, X.; Balezentis, T;  Streimikiene , D.; Niu, Z. 2021. Multi-step least squares support vector machine modeling approach for forecasting short-term electricity demand with application. Neural Comput & Applic 33, 301–320; Electronic ISSN 1433-3058; Print ISSN 0941-0643; https://doi.org/10.1007/s00521-020-04996-3; [ACM Digital Library; ANVUR; CNKI; Current Contents/Engineering, Computing and Technology; DBLP; Dimensions; EBSCO Academic Search; EBSCO Advanced Placement Source; EBSCO Applied Science & Technology Source; EBSCO Computer Science Index; EBSCO Computer Source: Consumer Edition; EBSCO Computers & Applied Sciences Complete; EBSCO Discovery Service; EBSCO Engineering Source; EBSCO Military & Government Collection; EBSCO STM Source; EBSCO Science & Technology Collection; EI Compendex; Google Scholar; INSPEC; Institute of Scientific and Technical Information of China; Japanese Science and Technology Agency (JST); Journal Citation Reports/Science Edition; Naver; OCLC WorldCat Discovery Service; ProQuest Advanced Technologies & Aerospace Database; ProQuest Central; ProQuest SciTech Premium Collection; ProQuest Technology Collection; ProQuest-ExLibris Primo; ProQuest-ExLibris Summon; SCImago; SCOPUS; Science Citation Index Expanded (SciSearch); TD Net Discovery Service; UGC-CARE List (India); WTI Frankfurt eG].


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