Seite - 389 - in Short-Term Load Forecasting by Artificial Intelligent Technologies
Bild der Seite - 389 -
Text der Seite - 389 -
Energies2018,11, 1138
4. Hong,T.;Fan,S.Probabilisticelectric loadforecasting:Atutorial review. Int. J.Forecast. 2016,32, 914â938.
[CrossRef]
5. Taylor, J.W.;McSharry,P.E.Short-termloadforecastingmethods:Anevaluationbasedoneuropeandata.
IEEETrans. PowerSyst. 2008,22, 2213â2219. [CrossRef]
6. Chen,H.;Wan,Q.;Zhang,B.;Li,F.;Wang,Y.Short-termloadforecastingbasedonasymmetricARCHmodels.
InProceedingsofthePowerandEnergySocietyGeneralMeeting,Providence,RI,USA,25â29July2010;pp.1â6.
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, 303â315. [CrossRef]
[PubMed]
8. Ding,N.;Benoit,C.;Foggia,G.;Besanger,Y.;Wurtz,F.Neuralnetwork-basedmodeldesignforshort-term
loadforecast indistributionsystems. IEEETrans. PowerSyst. 2016,31, 72â81. [CrossRef]
9. Cevik,H.H.;CunkaS,M.Short-termloadforecastingusingfuzzy logicandANFIS.NeuralComput. Appl.
2015,26, 1355â1367. [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, 504â507. [CrossRef] [PubMed]
12. Hinton,G.E.;Osindero,S.;Teh,Y.W.Afast learningalgorithmfordeepbeliefnets.NeuralComput. 2006,18,
1527â1554. [CrossRef] [PubMed]
13. Chen,K.;Huang,C.;He, J.Faultdetection, classiïŹcationandlocation for transmission linesanddistribution
systems:Areviewonthemethods.HighVolt. 2016,1, 25â33. [CrossRef]
14. Wang,Y.;Liu,M.;Bao,Z.Deep learningneuralnetworkforpowersystemfaultdiagnosis. InProceedingsof
the35thChineseControlConference,Chengdu,China,27â29 July2016;pp. 6678â6683.
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, 246â255. [CrossRef]
17. Tong,C.;Li, J.;Lang,C.;Kong,F.;Niu, J.;Rodrigues, J.J.AnefïŹcientdeepmodel forday-aheadelectricity
loadforecastingwithstackeddenoisingauto-encoders. J.ParallelDistrib. Comput. 2017, 1â7. [CrossRef]
18. Liao,Y.;Chen,S.AmodularRNN-basedmethodforcontinuousMandarinspeechrecognition. IEEETrans.
SpeechAud. Proc. 2016,9, 252â263. [CrossRef]
19. Nakashika, T.; Takiguchi, T.; Ariki, Y. Voice conversion usingRNNpre-trained by recurrent temporal
restrictedBoltzmannmachines. IEEE/ACMTrans.Aud. SpeechLang. Proc. 2015,23, 580â587. [CrossRef]
20. Wu,H.;Prasad,S.Convolutional recurrentneuralnetworks forhyperspectraldataclassiïŹcation.RemoteSens.
2017,9, 298. [CrossRef]
21. Zuo,H.;Fan,H.;Blasch,E.;Ling,H.Combiningconvolutionalandrecurrentneuralnetworks forhuman
skindetection. IEEESignalProc. Lett. 2017,24, 289â293. [CrossRef]
22. Chien, J.T.;Ku,Y.C.Bayesianrecurrentneuralnetwork for languagemodeling. IEEETrans. NeuralNetw.
Learn. Syst. 2016,27, 361â374. [CrossRef] [PubMed]
23. Ororbia, A.G., II;Mikolov, T.; Reitter, D. Learning simpler languagemodelswith the differential state
framework.NeuralComput. 2017,29, 3327â3352. [CrossRef] [PubMed]
24. Shi,H.;Xu,M.;Li,R.Deeplearningforhousehold loadforecastingâAnovelpoolingdeepRNN. IEEETrans.
SmartGrid2017, 1â10. [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, 21â29.
26. Hochreiter, S.; Schmidhuber, J. Longshort-termmemory. NeuralComput. 1997,9, 1735â1780. [CrossRef]
[PubMed]
27. Wei,D.;Wang,B.; Lin,G.; Liu,D.;Dong,Z.; Liu,H.; Liu,Y.Researchonunstructured textdatamining
and fault classiïŹcationbasedonRNN-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, 7675â7686. [CrossRef]
389
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