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Energies2018,11, 1605 51. Miranian,A.;Abdollahzade,M.Developingalocal least-squaressupportvectormachines-basedneuro-fuzzy model for nonlinear and chaotic time series prediction. IEEETrans. NeuralNetw. Learn. Syst. 2013, 24, 207–218. [CrossRef] [PubMed] 52. Kao,L.-J.;Chiu,C.-C.;Lu,C.-J.;Yang, J.-L. Integrationofnonlinear independentcomponentanalysisand supportvector regressionforstockprice forecasting.Neurocomputing2013,99, 534–542. [CrossRef] 53. Tofallis,C.Least squarespercentageregression. J.Mod.Appl. Stat.Methods2009,7, 526–534. [CrossRef] 54. Kianimajd,A.;Ruano,M.G.;Carvalho,P.;Henriques, J.; Rocha,T.; Paredes, S.;Ruano,A.E.Comparison of different methods of measuring similarity in physiologic time series. IFAC-PapersOnLine 2017, 50, 11005–11010. [CrossRef] Β©2018bytheauthors. LicenseeMDPI,Basel,Switzerland. Thisarticle isanopenaccess articledistributedunder the termsandconditionsof theCreativeCommonsAttribution (CCBY) license (http://creativecommons.org/licenses/by/4.0/). 287
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Short-Term Load Forecasting by Artificial Intelligent Technologies
Title
Short-Term Load Forecasting by Artificial Intelligent Technologies
Authors
Wei-Chiang Hong
Ming-Wei Li
Guo-Feng Fan
Editor
MDPI
Location
Basel
Date
2019
Language
English
License
CC BY 4.0
ISBN
978-3-03897-583-0
Size
17.0 x 24.4 cm
Pages
448
Keywords
Scheduling Problems in Logistics, Transport, Timetabling, Sports, Healthcare, Engineering, Energy Management
Category
Informatik
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Short-Term Load Forecasting by Artificial Intelligent Technologies