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Energies2018,11, 3283 37. Kanai,S.;Fujiwara,Y.; Iwamura,S.PreventingGradientExplosionsinGatedRecurrentUnits. InProceedings of theNeural InformationProcessingSystems,LongBeach,CA,USA,4–9December2017;pp.435–444. 38. Cho,K.;vanMerriënboer,B.;Gulcehre,C.;Bahdanau,D.;Bougares,F.;Schwenk,H.;Bengio,Y.Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv 2014, arXiv:1406.1078. 39. Hochreiter, S.; Schmidhuber, J. Longshort-termmemory. NeuralComput. 1997,9, 1735–1780. [CrossRef] [PubMed] 40. Rutkowski,L.; Jaworski,M.;Pietruczuk,L.;Duda,P.TheCARTdecisiontree forminingdatastreams. Inf. Sci. 2014,266, 1–15. [CrossRef] 41. Breiman,L.Randomforests.Mach. Learn. 2001,45, 5–32. [CrossRef] 42. Oshiro, T.M.; Perez, P.S.; Baranauskas, J.A.Howmany trees in a random forest? InProceedings of the InternationalConferenceonMachineLearningandDataMining inPatternRecognition,Berlin,Germany, 13–20 July2012;pp.154–168. 43. Díaz-Uriarte,R.;DeAndres,S.A.Geneselectionandclassificationofmicroarraydatausingrandomforest. BMCBioinform. 2006,7, 3. [CrossRef] [PubMed] 44. Suliman,A.; Zhang,Y.AReviewonBack-PropagationNeuralNetworks in theApplication ofRemote SensingImageClassification. J.EarthSci. Eng. (JEASE)2015,5, 52–65. [CrossRef] 45. Bengio,Y.Learningdeeparchitectures forAI.Found. Trends®Mach. Learn. 2009,2, 1–127. [CrossRef] 46. Clevert,D.-A.;Unterthiner,T.;Hochreiter,S.Fastandaccuratedeepnetwork learningbyexponential linear units (elus). arXiv2015, arXiv:1511.07289. 47. Sheela, K.G.; Deepa, S.N. Review on methods to fix number of hidden neurons in neural networks. Math.Probl. Eng. 2013, 425740. [CrossRef] 48. Xu,S.;Chen,L.Anovelapproachfordeterminingtheoptimalnumberofhiddenlayerneurons forFNN’s and its application in datamining. In Proceedings of the 5th InternationalConference on Information TechnologyandApplication(ICITA),Cairns,Australia, 23–26 June2008;pp.683–686. 49. Hyndman,R.J.;Athanasopoulos,G.Forecasting: Principles andPractice;Otexts:Melbourne,Australia, 2014; ISBN0987507117. 50. Pedregosa,F.;Varoquaux,G.;Gramfort,A.;Michel,V.;Thirion,B.;Grisel,O.;Blondel,M.;Prettenhofer,P.; Weiss,R.;Dubourg,V.Scikit-learn:Machine learning inPython. J.Mach. Learn.Res. 2011,12, 2825–2830. 51. Abadi,M.;Barham,P.;Chen, J.;Chen,Z.;Davis,A.;Dean, J.;Devin,M.;Ghemawat,S.; Irving,G.; Isard,M. TensorFlow:ASystemforLarge-ScaleMachineLearning. InProceedingsof the12thUSENIXSymposium onOperatingSystemsDesignandImplementation(OSDI ’16),Savannah,GA,USA,2–4November2016; pp.265–283. 52. Ketkar,N. IntroductiontoKeras. InDeepLearningwithPython;Apress: Berkeley,CA,USA,2017;pp.97–111. ©2018bytheauthors. LicenseeMDPI,Basel,Switzerland. Thisarticle isanopenaccess articledistributedunder the termsandconditionsof theCreativeCommonsAttribution (CCBY) license (http://creativecommons.org/licenses/by/4.0/). 138
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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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Short-Term Load Forecasting by Artificial Intelligent Technologies