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About theSpecial IssueEditors Wei-ChiangHong’s research interestsmainly include computational intelligence (neural networks, evolutionary computation) and the application of forecasting technology (ARIMA, Support vector regression,andChaos theory). In May 2012, one of his papers was named the “Top Cited Article 2007–2011” of Applied Mathematical Modelling, Elsevier Publisher. In Aug. 2014, he was nominated for the award “Outstanding Professor Award”, by Far Eastern Y. Z. Hsu Science and Technology Memorial Foundation (Taiwan). In Nov. 2014, he was nominated for the “Taiwan Inaugural Scopus Young ResearcherAward–ComputerScience”, byElsevierPublisher, in thePresidents’ ForumofSoutheast and SouthAsia and TaiwanUniversities. In Jun. 2015, hewas named as one of the “Top 10 Best Reviewers”ofAppliedEnergy in2014. InAug. 2017,hewasnamedasoneof the“BestReviewers” ofAppliedEnergy in2016. Ming-WeiLi receivedhisPh.D.degreeofengineering fromDalianUniversityofTechnology,China, in2013. SinceSeptember2017,heisanassociateprofessor intheCollegeofShipbuildingEngineering ofHarbinEngineeringUniversity.His research interestsare IntelligentForecastingMethods,Hybrid EvolutionaryAlgorithm, IntelligentOcean andWaterConservancyEngineering, KeyTechnologies ofMarineRenewableEnergy. Guo-FengFan receivedhis Ph.D. degree inEngineering from theResearchCenter ofMetallurgical Energy Conservation and Emission Reduction, Ministry of Education, Kunming University of Science and Technology, Kunming, in 2013. His research interests are ferrousmetallurgy, energy forecasting, optimization, system identification. In Jan 2018, his paperwas a “TopCitedArticle” by Nuerocomputing, Elsevier Publisher. In Oct 2018, he won the title of Henan academic and technical leader. vii
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Short-Term Load Forecasting by Artificial Intelligent Technologies
Titel
Short-Term Load Forecasting by Artificial Intelligent Technologies
Autoren
Wei-Chiang Hong
Ming-Wei Li
Guo-Feng Fan
Herausgeber
MDPI
Ort
Basel
Datum
2019
Sprache
englisch
Lizenz
CC BY 4.0
ISBN
978-3-03897-583-0
Abmessungen
17.0 x 24.4 cm
Seiten
448
Schlagwörter
Scheduling Problems in Logistics, Transport, Timetabling, Sports, Healthcare, Engineering, Energy Management
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Short-Term Load Forecasting by Artificial Intelligent Technologies