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Energies2018,11, 2080 29. Singh,S.;Yassine,A.Bigdataminingofenergytimeseries forbehavioralanalyticsandenergyconsumption forecasting.Energies2018,11, 452. [CrossRef] 30. LĂłpez,M.;Valero,S.; Senabre,C.;Aparicio, J.;Gabaldon,A.Standardizationofshort-termloadforecasting models. InProceedingsof the20129th InternationalConferenceontheEuropeanEnergyMarket,Florence, Italy,10–12May2012;pp.1–7. 31. Kim,C.;Yu, I.; Song,Y.H.Kohonenneuralnetworkandwavelet transformbasedapproachtoshort-term loadforecasting.Electr. PowerSyst. Res. 2002,63, 169–176. [CrossRef] 32. Chen,Y.;Luh,P.B.;Rourke,S.J. Short-termloadforecasting: Similarday-basedwaveletneuralnetworks. InProceedingsof the20087thWorldCongressonIntelligentControlandAutomation,Chongqing,China, 25–27 June2008;pp.3353–3358. 33. LĂłpez,M.;Valero, S.; Senabre,C.;GabaldĂłn,A.Analysisof the inïŹ‚uenceofmeteorologicalvariableson real-timeShort-TermLoadForecastinginBalearic Islands. InProceedingsof the201711thIEEEInternational ConferenceonCompatibility,PowerElectronicsandPowerEngineering(CPE-POWERENG),Cadiz,Spain, 4–6April2017;pp.10–15. 34. Cancelo, J.R.;Espasa,A.;Grafe,R.Forecastingtheelectricity loadfromonedaytooneweekaheadfor the Spanishsystemoperator. Int. J.Forecast. 2008,24, 588–602. [CrossRef] 35. Lamedica,R.;Prudenzi,A.;Sforna,M.;Caciotta,M.;Cencellli,V.O.Aneuralnetworkbasedtechniquefor short-termforecastingofanomalous loadperiods. IEEETrans. PowerSyst. 1996,11, 1749–1756. [CrossRef] 36. Arora,S.;Taylor, J.W.Short-termforecastingofanomalous loadusingrule-basedtripleseasonalmethods. IEEETrans. PowerSyst. 2013,28, 3235–3242. [CrossRef] 37. Khotanzad,A.;Afkhami-Rohani,R.;Maratukulam,D.ANNSTLF-artiïŹcialneuralnetworkshort-termload forecastergenerationthree. IEEETrans. PowerSyst. 1998,13, 1413–1422. [CrossRef] 38. Fan,S.;Methaprayoon,K.;Lee,W.-J.Multiregion loadforecastingforsystemwith largegeographicalarea. IEEETrans. Ind.Appl. 2009,45, 1452–1459. [CrossRef] 39. LĂłpez,M.; Valero, S.; Rodriguez,A.; Veiras, I.; Senabre, C.Newonline load forecasting system for the Spanish transport systemoperator.Electr. PowerSyst. Res. 2018,154, 401–412. [CrossRef] 40. Mares, J.J.;Mercado,K.D.;QuinteroM.,C.G.AMethodology for short-term load forecasting. IEEELat. Am.Trans.2017,15, 400–407. [CrossRef] 41. Almeshaiei,E.; Soltan,H.Amethodologyforelectricpower loadforecasting.Alex. Eng. J.2011,50, 137–144. [CrossRef] 42. GarcĂ­a,M.L.;Valero,S.; Senabre,C.;MarĂ­n,A.G.Short-termpredictabilityof loadseries: characterizationof loaddatabases. IEEETrans. PowerSyst. 2013,28, 2466–2474. [CrossRef] 43. Box,G.E.P.; Jenkins,G.M.;Reinsel,G.C.;Ljung,G.M.TimeSeriesAnalysis: ForecastingandControl, 5thed.; JohnWiley&Sons, Inc.:Hoboken,NJ,USA,2016. 44. Akaike,H.Statisticalpredictor identiïŹcation.Ann. Inst. Stat.Math. 1970,22, 203–217. [CrossRef] 45. Akaike,H. Informationtheoryandanextensionof themaximumlikelihoodprinciple. InProceedingsof theSecondInternationalSymposiumonInformationTheory,Tsahkadsor,Armenia, 2–8September1971; Petrov,B.N.,Caski,F.,Eds.;AkadĂ©miaiKiado: Budapest,Hungary;pp.267–281. 46. GobiernodeEspaña. BoletĂ­nOïŹcialdelEstado.Availableonline:www.boe.es (accessedon10April2018). ©2018bytheauthors. LicenseeMDPI,Basel,Switzerland. Thisarticle isanopenaccess articledistributedunder the termsandconditionsof theCreativeCommonsAttribution (CCBY) license (http://creativecommons.org/licenses/by/4.0/). 157
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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