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Energies2018,11, 1138 4. Hong,T.;Fan,S.Probabilisticelectric loadforecasting:Atutorial review. Int. J.Forecast. 2016,32, 914–938. [CrossRef] 5. Taylor, J.W.;McSharry,P.E.Short-termloadforecastingmethods:Anevaluationbasedoneuropeandata. IEEETrans. PowerSyst. 2008,22, 2213–2219. [CrossRef] 6. Chen,H.;Wan,Q.;Zhang,B.;Li,F.;Wang,Y.Short-termloadforecastingbasedonasymmetricARCHmodels. InProceedingsofthePowerandEnergySocietyGeneralMeeting,Providence,RI,USA,25–29July2010;pp.1–6. 7. Quan, H.; Srinivasan, D.; Khosravi, A. Short-term load and wind power forecasting using neural network-basedprediction intervals. IEEETrans. NeuralNetw. Learn. Syst. 2014,22, 303–315. [CrossRef] [PubMed] 8. Ding,N.;Benoit,C.;Foggia,G.;Besanger,Y.;Wurtz,F.Neuralnetwork-basedmodeldesignforshort-term loadforecast indistributionsystems. IEEETrans. PowerSyst. 2016,31, 72–81. [CrossRef] 9. Cevik,H.H.;CunkaS,M.Short-termloadforecastingusingfuzzy logicandANFIS.NeuralComput. Appl. 2015,26, 1355–1367. [CrossRef] 10. Lee,C.W.;Lin,B.Y.Applicationsof thechaoticquantumgeneticalgorithmwithsupportvector regression in loadforecasting.Energies2017,10, 1832. [CrossRef] 11. Hinton,G.E.;Salakhutdinov,R.R.Reducingthedimensionalityofdatawithneuralnetworks.Science2006, 313, 504–507. [CrossRef] [PubMed] 12. Hinton,G.E.;Osindero,S.;Teh,Y.W.Afast learningalgorithmfordeepbeliefnets.NeuralComput. 2006,18, 1527–1554. [CrossRef] [PubMed] 13. Chen,K.;Huang,C.;He, J.Faultdetection, classiïŹcationandlocation for transmission linesanddistribution systems:Areviewonthemethods.HighVolt. 2016,1, 25–33. [CrossRef] 14. Wang,Y.;Liu,M.;Bao,Z.Deep learningneuralnetworkforpowersystemfaultdiagnosis. InProceedingsof the35thChineseControlConference,Chengdu,China,27–29 July2016;pp. 6678–6683. 15. Ryu,S.;Noh, J.;Kim,H.Deepneuralnetworkbaseddemandsideshort termloadforecasting.Energies2016, 10, 3. [CrossRef] 16. Qiu,X.;Ren,Y.;Suganthan,P.N.;Amaratunga,G.A.Empiricalmodedecompositionbasedensembledeep learningfor loaddemandtimeseries forecasting.Appl. SoftComput. 2017,54, 246–255. [CrossRef] 17. Tong,C.;Li, J.;Lang,C.;Kong,F.;Niu, J.;Rodrigues, J.J.AnefïŹcientdeepmodel forday-aheadelectricity loadforecastingwithstackeddenoisingauto-encoders. J.ParallelDistrib. Comput. 2017, 1–7. [CrossRef] 18. Liao,Y.;Chen,S.AmodularRNN-basedmethodforcontinuousMandarinspeechrecognition. IEEETrans. SpeechAud. Proc. 2016,9, 252–263. [CrossRef] 19. Nakashika, T.; Takiguchi, T.; Ariki, Y. Voice conversion usingRNNpre-trained by recurrent temporal restrictedBoltzmannmachines. IEEE/ACMTrans.Aud. SpeechLang. Proc. 2015,23, 580–587. [CrossRef] 20. Wu,H.;Prasad,S.Convolutional recurrentneuralnetworks forhyperspectraldataclassiïŹcation.RemoteSens. 2017,9, 298. [CrossRef] 21. Zuo,H.;Fan,H.;Blasch,E.;Ling,H.Combiningconvolutionalandrecurrentneuralnetworks forhuman skindetection. IEEESignalProc. Lett. 2017,24, 289–293. [CrossRef] 22. Chien, J.T.;Ku,Y.C.Bayesianrecurrentneuralnetwork for languagemodeling. IEEETrans. NeuralNetw. Learn. Syst. 2016,27, 361–374. [CrossRef] [PubMed] 23. Ororbia, A.G., II;Mikolov, T.; Reitter, D. Learning simpler languagemodelswith the differential state framework.NeuralComput. 2017,29, 3327–3352. [CrossRef] [PubMed] 24. Shi,H.;Xu,M.;Li,R.Deeplearningforhousehold loadforecasting—AnovelpoolingdeepRNN. IEEETrans. SmartGrid2017, 1–10. [CrossRef] 25. Wei,L.Y.;Tsai,C.H.;Chung,Y.C.;Liao,K.H.;Chueh,H.E.;Lin, J.S.Astudyof thehybridrecurrentneural networkmodel forelectricity loads forecasting. Int. J.Acad. Res.Account. Fin.Manag. Sci. 2017,7, 21–29. 26. Hochreiter, S.; Schmidhuber, J. Longshort-termmemory. NeuralComput. 1997,9, 1735–1780. [CrossRef] [PubMed] 27. Wei,D.;Wang,B.; Lin,G.; Liu,D.;Dong,Z.; Liu,H.; Liu,Y.Researchonunstructured textdatamining and fault classiïŹcationbasedonRNN-LSTMwithmalfunction inspection report. Energies 2017, 10, 406. [CrossRef] 28. Zhang, S.;Wang,Y.; Liu,M.;Bao,Z.Data-based line trip faultprediction inpowersystemsusingLSTM networksandSVM. IEEEAccess2017,6, 7675–7686. [CrossRef] 389
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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