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Energies 2018,11, 242 References 1. Ahmad,M.W.;Mourshed,M.;Rezgui,Y.TreesvsNeurons:ComparisonbetweenrandomforestandANN forhigh-resolutionpredictionofbuildingenergyconsumption. EnergyBuild. 2017,147, 77–89. 2. Banihashemi,S.;Ding,G.;Wang, J. DevelopingahybridmodelofpredictionandclassiïŹcationalgorithms forbuildingenergyconsumption. EnergyProcedia2017,110, 371–376. 3. Naji, S.; Keivani,A.; Shamshirband, S.; Alengaram,U.J.; Jumaat,M.Z.;Mansor, Z.; Lee,M. Estimating buildingenergyconsumptionusingextremelearningmachinemethod. Energy2016,97, 506–516. 4. Hsu,D. Comparisonof integratedclusteringmethods foraccurateandstablepredictionofbuildingenergy consumptiondata. Appl. Energy2015,160, 153–163. 5. Dong,B.;Cao,C.;Lee,S.E. Applyingsupportvectormachines topredictbuildingenergyconsumption in tropical region. EnergyBuild. 2005,37, 545–553. 6. Jung,H.C.;Kim, J.S.;Heo,H. Predictionofbuildingenergyconsumptionusingan improvedreal coded geneticalgorithmbasedleast squaressupportvectormachineapproach. EnergyBuild. 2015,90, 76–84. 7. Hong,W.C.;Dong,Y.;Zhang,W.Y.;Chen,L.Y.;Panigrahi,B.K. Cyclicelectric loadforecastingbyseasonal SVRwithchaoticgeneticalgorithm. Int. J.Electr. PowerEnergySyst. 2013,44, 604–614. 8. Fan,G.F.; Peng, L.L.; Hong,W.C.; Sun, F. Electric load forecasting by the SVRmodelwithdifferential empiricalmodedecompositionandautoregression. Neurocomputing2016,173, 958–970. 9. Hong,W.C. Chaoticparticle swarmoptimizationalgorithm ina support vector regressionelectric load forecastingmodel. EnergyConvers.Manag. 2009,50, 105–117. 10. Robinson,C.;Dilkina,B.;Hubbs, J.;Zhang,W.;Guhathakurta,S.;Brown,M.A.;Pendyala,R.M.Machinelearning approachesforestimatingcommercialbuildingenergyconsumption.Appl. Energy2017,208, 889–904. 11. Hinton,G.E.;Osindero, S.; Teh,Y.W.Afast learningalgorithmfordeepbeliefnets.NeuralComput. 2006, 18, 1527–1554. 12. Lv,Y.;Duan,Y.;Kang,W.;Li,Z.;Wang,F.Y. TrafïŹcïŹ‚owpredictionwithbigdata:Adeep learningapproach. IEEETrans. Intell. Transp. Syst. 2015,16, 865–873. 13. Yang,H.F.;Dillon,T.S.;Chen,Y.P.P. Optimizedstructureof the trafïŹcïŹ‚owforecastingmodelwithadeep learningapproach. IEEETrans.NeuralNetw. Learn. Syst. 2017,28, 2371–2381. 14. Li, C.; Ding, Z.; Zhao,D.; Yi, J.; Zhang,G. Building energy consumptionprediction: Anextremedeep learningapproach. Energies2017,10, 1525. 15. Xiao,Y.;Wu, J.;Lin,Z.;Zhao,X. Adeep learning-basedmulti-modelensemblemethodforcancerprediction. Comput.MethodsProgramsBiomed. 2018,153, 1–9. 16. Galea,C.;Farrugia,R.A. Forensic facephoto-sketchrecognitionusingadeeplearning-basedarchitecture. IEEESignalProcess. Lett. 2017,24, 1586–1590. 17. Masoumi,M.; Hamza,A.B. Spectral shape classiïŹcation: Adeep learning approach. J. Vis. Commun. ImageRepresent. 2017,43, 198–211. 18. Sarikaya, R.; Hinton, G.E.; Deoras, A. Application of deep belief networks for natural language understanding. IEEE/ACMTrans.AudioSpeechLang. Process. (TASLP)2014,22, 778–784. 19. Zhang,X.L.;Wu,J.Deepbeliefnetworksbasedvoiceactivitydetection. IEEETrans.AudioSpeechLang.Process. 2013,21, 697–710. 20. Chen, C.C.; Li, S.T. Credit rating with a monotonicity-constrained support vector machine model. ExpertSyst.Appl. 2014,41, 7235–7247. 21. Wang,L.;Xue,P.;Chan,K.L. Incorporatingpriorknowledge intoSVMfor imageretrieval. InProceedingsof the InternationalConferenceonPatternRecognition,Cambridge,UK,23–26August2004;pp. 981–984. 22. Wu, X.; Srihari, R. Incorporating prior knowledge with weighted margin support vector machines. InProceedingsof theTenthACMSIGKDDInternationalConferenceonKnowledgeDiscoveryandData Mining,Seattle,WA,USA,22–25August2004;ACM:NewYork,NY,USA,2004;pp. 326–333. 23. Li,C.;Zhang,G.;Yi, J.;Wang,M.Uncertaintydegreeandmodelingof interval type-2 fuzzysets: deïŹnition, methodandapplicationComput.Math.Appl. 2013,66, 1822–1835. 24. Abonyi, J.; Babuska, R.; Verbruggen, H.B.; Szeifert, F. Incorporating prior knowledge in fuzzymodel identiïŹcation. Int. J.Syst. Sci. 2000,31, 657–667. 25. Li,C.;Yi, J.;Zhang,G.Onthemonotonicityof interval type-2 fuzzy logicsystems. IEEETrans. FuzzySyst. 2014,22, 1197–1212. 415
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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
Kategorie
Informatik
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Short-Term Load Forecasting by Artificial Intelligent Technologies