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Energies 2018,11, 213 21. Veit, A.; Goebel, C.; Tidke, R.; Doblander, C.; Jacobsen, H.Household electricity demand forecasting: Benchmarkingstate-of-the-artmethod. InProceedingsof the5thInternationalConfrerenceFutureEnergy Systems,Cambridge,UK,11–13 June2014;pp.233–234. [CrossRef] 22. Jetcheva, J.G.;Majidpour,M.;Chen,W.Neuralnetworkmodelensembles forbuilding-levelelectricity load forecasts.EnergyBuild. 2014,84, 214–223. [CrossRef] 23. Kardakos,E.G.;Alexiadis,M.C.;Vagropoulos,S.I.; Simoglou,C.K.;Biskas,P.N.;Bakirtzis,A.G.Application of time series and artificial neural networkmodels in short-term forecasting of PV power generation. InProceedingsof the201348th InternationalUniversities’PowerEngineeringConference,Dublin, Ireland, 2–5September2013;pp.1–6. [CrossRef] 24. Fujimoto,Y.;Hayashi,Y.Patternsequence-basedenergydemandforecastusingphotovoltaicenergyrecords. In Proceedings of the 2012 International Conference onRenewable EnergyResearch andApplications, Nagasaki, Japan,11–14November2012. 25. Chaouch,M.Clustering-basedimprovementofnonparametricfunctionaltimeseriesforecasting:Application to intra-dayhousehold-level loadcurves. IEEETrans. SmartGrid2014,5, 411–419. [CrossRef] 26. Niu,D.;Dai,S.Ashort-termloadforecastingmodelwithamodifiedparticle swarmoptimizationalgorithm andleast squaressupportvectormachinebasedonthedenoisingmethodofempiricalmodedecomposition andgreyrelationalanalysis.Energies2017,10, 408. [CrossRef] 27. Deo,R.C.;Wen,X.;Qi,F.Awavelet-coupledsupportvectormachinemodel for forecastingglobal incident solar radiationusing limitedmeteorologicaldataset.Appl. Energy2016,168, 568–593. [CrossRef] 28. Soubdhan,T.;Ndong, J.;Ould-Baba,H.;Do,M.T.Arobust forecasting frameworkbasedon theKalman filtering approachwith a twofold parameter tuning procedure: Application to solar and photovoltaic prediction.Sol. Energy2016,131, 246–259. [CrossRef] 29. Hahn,H.;Meyer-Nieberg,S.;Pickl,S.Electric loadforecastingmethods: Tools fordecisionmaking.Eur. J. Oper. Res. 2009,199, 902–907. [CrossRef] 30. Zhang,G.;Patuwo,B.E.;Hu,M.Y.Forecastingwithartificialneuralnetworks: Thestateof theart. Int. J.Forecast. 1998,14, 35–62. [CrossRef] 31. Pappas,S.S.;Ekonomou,L.;Moussas,V.C.;Karampelas,P.;Katsikas,S.K.Adaptive loadforecastingof the Hellenicelectricgrid. J.ZhejiangUniv.A2008,9, 1724–1730. [CrossRef] 32. White,B.W.;Rosenblatt,F.Principlesofneurodynamics: Perceptronsandthetheoryofbrainmechanisms. Am. J.Psychol. 1963,76, 705. [CrossRef] 33. Hochreiter, S.; Urgen Schmidhuber, J. Long short-termmemory. Neural Comput. 1997, 9, 1735–1780. [CrossRef] [PubMed] 34. Srivastava,N.;Hinton,G.;Krizhevsky,A.;Sutskever, I.;Salakhutdinov,R.Dropout:ASimpleWaytoPrevent NeuralNetworks fromOverfitting. J.Mach. Learn.Res. 2014,15, 1929–1958. [CrossRef] 35. Suykens, J.A.K.;Vandewalle, J.Least squaressupportvectormachineclassifiers.NeuralProcess. Lett. 1999,9, 293–300. [CrossRef] 36. Liaw,A.;Wiener,M.ClassificationandRegressionbyrandomForest.RNews2002,2, 18–22. [CrossRef] 37. Safavian,S.R.;Landgrebe,D.ASurveyofDecisionTreeClassifierMethodology. IEEETrans. Syst.ManCybern. 1991,21, 660–674. [CrossRef] ©2018bytheauthors. LicenseeMDPI,Basel,Switzerland. Thisarticle isanopenaccess articledistributedunder the termsandconditionsof theCreativeCommonsAttribution (CCBY) license (http://creativecommons.org/licenses/by/4.0/). 429
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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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