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Energies2018,11, 1605 28. Ediger,V.S¸.;Tatlıdil,H.ForecastingtheprimaryenergydemandinTurkeyandanalysisofcyclicpatterns. EnergyConvers.Manag. 2002,43, 473–487. [CrossRef] 29. Manera,M.;Marzullo,A.Modelling the loadcurveofaggregateelectricityconsumptionusingprincipal components.Environ.Model. Softw. 2005,20, 1389–1400. [CrossRef] 30. Ediger,V.S¸.;Akar,S.ARIMAforecastingofprimaryenergydemandbyfuel inTurkey.EnergyPolicy2007,35, 1701–1708. [CrossRef] 31. Mishra,P. Forecastingnatural gasprice-time series andnonparametric approach. InProceedingsof the World Congress on Engineering, 2012 Vol IWCE 2012, London, UK, 4–6 July 2012; Available online: http://www.iaeng.org/publication/WCE2012/WCE2012_pp490-497.pdf (accessedon19June2018). 32. Zhang,G.P.Timeseries forecastingusingahybridARIMAandneuralnetworkmodel.Neurocomputing2003, 50, 159–175. [CrossRef] 33. Lai,K.K.;Yu,L.;Wang,S.;Huang,W.Hybridizingexponential smoothingandneuralnetworkforfinancial timeseriespredication. InProceedingsof the InternationalConferenceonComputationalScience,Reading,UK, 28–31May2006; Springer: Berlin/Heidelberg,Germany,2006;pp.493–500. 34. Ma,H.; Zhang, Z. Grey predictionwithMarkov-Chain for Crude oil production and consumption in China. InProceedings of the Sixth International SymposiumonNeuralNetworks (ISNN2009),Wuhan,China, 26–29May2009; Springer: Berlin,Germany,2009;pp.551–561. 35. Niska,H.;Hiltunen,T.;Karppinen,A.;Ruuskanen, J.;Kolehmainen,M.Evolvingtheneuralnetworkmodel for forecastingairpollutiontimeseries.Eng.Appl.Artif. Intell. 2004,17, 159–167. [CrossRef] 36. Turanoglu, E.; Senvar, O.; Kahraman, C.Oil consumption forecasting in Turkey using artificial neural network. Int. J.EnergyOptim. Eng. 2012,1, 89–105. [CrossRef] 37. Ekonomou,L.Greek long-termenergyconsumptionpredictionusingartificialneuralnetworks.Energy2010, 35, 512–517. [CrossRef] 38. Buhari,M.;Adamu,S.S.Short-termloadforecastingusingartificialneuralnetwork. InProceedingsof the InternationalMulti-ConferenceofEngineersandComputerScientist,Goa, India,19–22 January2012. 39. Nochai, R.;Nochai, T.ARIMAmodel for forecastingoil palmprice. InProceedingsof the 2nd IMT-GT Regional Conference onMathematics, Statistics andApplications, Penang,Malaysia, 13–15 June 2006; pp.13–15. 40. Chiroma,H.;Abdulkareem,S.;Muaz,S.A.;Abubakar,A.I.; Sutoyo,E.;Mungad,M.; Saadi,Y.; Sari,E.N.; Herawan,T.An intelligentmodelingofoil consumption. InAdvances in Intelligent Informatics; Springer: Berlin,Germany,2015;pp.557–568. 41. Chiroma,H.;Khan,A.;Abubakar,A.I.;Muaz,S.A.;Gital,A.Y.U.;Shuib,L.M.EstimationofMiddle-EastOil ConsumptionUsingHybridMeta-HeuristicAlgorithms. Presentedat theSecondInternationalConference onAdvancedDataandInformationEngineering,Bali, Indonesia,25–26April2015. 42. Xia,Y.; Liu,C.;Da,B.; Xie, F.Anovelheterogeneousensemble credit scoringmodelbasedonbstacking approach.ExpertSyst.Appl. 2018,93, 182–199. [CrossRef] 43. Hansen,B.E. Interval forecastsandparameteruncertainty. J.Econom. 2006,135, 377–398. [CrossRef] 44. Rubinstein,S.;Goor,A.;Rotshtein,A.Timeseries forecastingofcrudeoil consumptionusingneuro-fuzzy inference. J. Ind. Intell. Inf. 2015,3, 84–90. [CrossRef] 45. Efendi, R.; Deris,M.M. Forecasting ofmalaysian oil production andoil consumptionusing fuzzy time series. InProceedingsof the InternationalConferenceonSoftComputingandDataMining,SanDiego,CA,USA, 11–14September2016; Springer: Berlin,Germany,2016;pp.31–40. 46. Efendi,R.;Deris,M.M.PredictionofMalaysian–Indonesianoilproductionandconsumptionusingfuzzy timeseriesmodel.Adv.DataSci. Adapt.Anal. 2017,9, 1750001. [CrossRef] 47. Aho,T.;Enko,B.;Eroski,S.;Elomaa,T.Multi-target regressionwithruleensembles. J.Mach. Learn. Res. 2012, 13, 2367–2407. 48. Xu,M.;Golay,M.Surveyofmodelselectionandmodelcombination.SSRNElectron. J.2008. [CrossRef] 49. Wang, J.-Z.;Wang, J.-J.; Zhang,Z.-G.;Guo, S.-P. Forecasting stock indiceswithbackpropagationneural network.ExpertSyst.Appl. 2011,38, 14346–14355. [CrossRef] 50. Ebrahimpour,R.;Nikoo,H.;Masoudnia,S.;Yousefi,M.R.;Ghaemi,M.S.MixtureofMLP-experts for trend forecastingof time series: Acase studyof theTehran stock exchange. Int. J. Forecast. 2011, 27, 804–816. [CrossRef] 286
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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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