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Energies2018,11, 2008 8. Hornik,K.; Stinchcombe,M.;White,H.Multilayer feedforwardnetworks areuniversal approximators. NeuralNetw. 1989,2, 359–366. [CrossRef] 9. Park,D.C.; El-Sharkawi,M.A.;Marks,R.J.;Atlas, L.E.;Damborg,M.J. Electric load forecastingusingan artificialneuralnetwork. IEEETrans. PowerSyst. 1991,6, 442–449. [CrossRef] 10. Längkvist,M.;Karlsson,L.;Loutfi,A.Areviewofunsupervisedfeature learningfor timeseriesmodeling. PatternRecognit. Lett. 2014,42, 11–24. [CrossRef] 11. Szegedy,C.;Liu,W.; Jia,Y.;Sermanet,P.;Reed,S.;Anguelov,D.;Erhan,D.;Vanhoucke,V.;Rabinovich,A. Goingdeeperwith convolutions. InProceedingsof the 2015 IEEEConferenceonComputerVisionand PatternRecognition(CVPR),Boston,MA,USA,7–12 June2015;pp.1–9. 12. Hinton,G.E.;Hinton,G.E.;Osindero,S.;Osindero,S.;Teh,Y.W.;Teh,Y.W.Afast learningalgorithmfordeep beliefnets.NeuralComput. 2006,18, 1527–1554. [CrossRef] [PubMed] 13. Qiu,X.;Zhang,L.;Ren,Y.;Suganthan,P.N.;Amaratunga,G.Ensembledeeplearningforregressionandtime series forecasting. InProceedingsof the2014IEEESymposiumonComputational Intelligence inEnsemble Learning,Orlando,FL,USA,9–12December2014;pp.1–6. [CrossRef] 14. Busseti,E.;Osband, I.;Wong,S.DeepLearning forTimeSeriesModeling; StanfordUniversity: Stanford,CA, USA,2012. 15. Dalto,M.;Matusko, J.; Vasak,M.Deepneural networks for time series predictionwith applications in ultra-short-termwind forecasting. In Proceedings of the IEEE International Conference on Industrial Technology(ICIT),Seville,Spain,17–19March2015;pp.1657–1663. 16. Kuremoto,T.;Kimura,S.;Kobayashi,K.;Obayashi,M.Timeseries forecastingusingadeepbeliefnetwork withrestrictedBoltzmannmachines.Neurocomputing2014,137, 47–56. [CrossRef] 17. Glorot, X.; Bengio, Y. Understanding the difficulty of training deep feedforward neural networks. InProceedingsof theThirteenthInternationalConferenceonArtificial IntelligenceandStatistics,Sardinia, Italy,13–15May2010;Teh,Y.W.,Titterington,M.,Eds.;PMLR:London,UK,2010;pp.249–256. 18. Kennedy, J.;Eberhart,R.Particle swarmoptimization. InProceedingsof the IEEEInternationalConference onNeuralNetworks,Perth,Australia, 27November–1December1995;Volume4,pp.1942–1948. 19. Ryu,S.;Noh, J.;Kim,H.Deepneuralnetworkbaseddemandsideshort termloadforecasting.Energies2017, 10, 3. [CrossRef] 20. Kuo, P.-H.; Huang, C.-J. A high precision artificial neural networksmodel for short-term energy load forecasting.Energies2018,11, 213. [CrossRef] 21. Li, C.; Ding, Z.; Yi, J.; Lv, Y.; Zhang, G.Deep belief network based hybridmodel for building energy consumptionprediction.Energies2018,11, 242. [CrossRef] 22. Chen,K.;Chen,K.;Wang,Q.;He,Z.;Hu, J.;He, J. Short-termloadforecastingwithdeepresidualnetworks. IEEETrans. SmartGrid2018. [CrossRef] 23. Hosein,S.;Hosein,P.Loadforecastingusingdeepneuralnetworks. InProceedingsof theIEEEPower&Energy SocietyInnovativeSmartGridTechnologiesConference(ISGT),Washington,DC,USA,23–26April2017;pp.1–5. 24. Lin,C.-T.;Lee,C.S.G.NeuralFuzzySystems—ANeuro-FuzzySynergismto IntelligentSystems;Prentice-Hall: UpperSaddleRiver,NJ,USA,1996. 25. Bengio,Y.Learningdeeparchitectures forAI.Found. TrendsMach. Learn. 2009,2, 1–127. [CrossRef] 26. Hinton,G.Apractical guide to training restrictedBoltzmannmachines. InNeuralNetworks: Tricks of the Trade; Springer: Berlin/Heidelberg,Germany,2012. 27. Hansen, L.K.; Salamon, P.Neural network ensembles. IEEETrans. PatternAnal. Mach. Intell. 1990, 12, 993–1001. [CrossRef] 28. Brown,R.H.;Vitullo,S.R.;Corliss,G.F.;Adya,M.;Kaefer,P.E.;Povinelli,R.J.Detrendingdailynaturalgas consumptionseries to improveshort-termforecasts. InProceedingsof the IEEEPowerandEnergySociety GeneralMeeting,Denver,CO,USA,26–30 July2015. 29. Ruchti, T.L.; Brown, R.H.; Garside, J.J. Kalman based artificial neural network training algorithms for nonlinearsystemidentification. InProceedingsof the IEEEInternationalSymposiumonIntelligentControl, Chicago, IL,USA,25–27August1993;pp.582–587. ©2018bytheauthors. LicenseeMDPI,Basel,Switzerland. Thisarticle isanopenaccess articledistributedunder the termsandconditionsof theCreativeCommonsAttribution (CCBY) license (http://creativecommons.org/licenses/by/4.0/). 191
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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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