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Short-Term Load Forecasting by Artificial Intelligent Technologies
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Energies2018,11, 2038 References 1. Hahn,H.;Meyer-Nieberg,S.;Pickl,S.Electric loadforecastingmethods: Tools fordecisionmaking.Eur. J. Oper. Res. 2009,199, 902–907. [CrossRef] 2. Alfares,H.K.;Nazeeruddin,M.Electric loadforecasting: LiteraturesurveyandclassiïŹcationofmethods. Int. J.Syst. Sci. 2002,33, 23–34. [CrossRef] 3. Yang,H.T.; Huang,C.M.;Huang,C.L. IdentiïŹcationofARMAXmodel for short term load forecasting: Anevolutionaryprogrammingapproach. IEEETrans. PowerSyst. 1996,11, 403–408. [CrossRef] 4. Taylor,W.;Menezes,L.M.;McSharry,P.E.Acomparisonofunivariatemethods for forecastingelectricity demanduptoadayahead. Int. J.Forecast. 2006,22, 1–16. [CrossRef] 5. Newsham,G.R.;Birt,B.J.Building-leveloccupancydata to improvearima-basedelectricityuse forecasts. InProceedingsof the2ndACMWorkshoponEmbeddedSensingSystemsforEnergy-EfïŹciency inBuilding, Zurich,Switzerland,3–5November2010;pp.13–18. 6. Massana, J.;Pous,C.;Burgas,L.;Melendez, J.;Colomer, J.Shorttermloadforecasting inanon-residential buildingcontrastingmodelsandattributes.EnergyBuild. 2015,92, 322–330. [CrossRef] 7. Bruhns, A.; Deurveilher, G.; Roy, J.S. A nonlinear regressionmodel formidterm load forecasting and improvements inseasonality. InProceedingsof the15thPowerSystemsComputationConference,Liege, Belgium,22–26August2005. 8. Charytoniuk,W.;Chen,M.S.;VanOlinda,P.Nonparametric regressionbasedshort-termloadforecasting. IEEETrans. PowerSyst. 1998,13, 725–730. [CrossRef] 9. Amber,K.P.;Aslam,M.W.;Mahmood,A.;Kousar,A.;Younis,M.Y.;Akbar,B.;Chaudhary,G.Q.;Hussain,S.K. EnergyConsumptionForecastingforUniversitySectorBuildings.Energies2017,10, 1579. [CrossRef] 10. Tso,G.K.F.;Yau,K.K.W.Predictingelectricityenergyconsumption:Acomparisonof regressionanalysis, decisiontreeandneuralnetworks.Energy2007,32, 1761–1768. [CrossRef] 11. Li, K.; Su, H.; Chu, J. Forecasting building energy consumption using neural networks and hybrid neuro-fuzzysystem:Acomparativestudy.EnergyBuild. 2011,43, 2893–2899. [CrossRef] 12. Liao,G.C.;Tsao,T.P.Applicationofa fuzzyneuralnetworkcombinedwithachaosgeneticalgorithmand simulatedannealingtoshort-termloadforecasting. IEEETrans. Evol. Comput. 2016,10, 330–340. [CrossRef] 13. Hippert,H.S.;Pedreira,C.E.;Souza,R.C.Neuralnetworks forshort-termloadforecasting:Areviewand evaluation. IEEETrans. PowerSyst. 2001,16, 44–55. [CrossRef] 14. Karatasou, S.; Santamouris,M.;Geros,V.Modelingandpredictingbuilding’s energyusewith artiïŹcial neuralnetworks:Methodsandresults.EnergyBuild. 2006,38, 949–958. [CrossRef] 15. Metaxiotis, K.; Kagiannas,A.; Askounis,D.; Psarras, J.ArtiïŹcial intelligence in short termelectric load forecasting: A state-of-the-art survey for the researcher. Energy Convers. Manag. 2003, 44, 1525–1534. [CrossRef] 16. Buitrago, J.;Asfour,S.Short-termforecastingofelectric loadsusingnonlinearautoregressiveartiïŹcialneural networkswithexogenousvector inputs.Energies2017,10, 40. [CrossRef] 17. Hashmi,M.U.;Arora,V.;Priolkar, J.G.Hourlyelectric loadforecastingusingNonlinearAutoRegressive witheXogenous(NARX)basedneuralnetworkforthestateofGoa, India. InProceedingsof theInternational ConferenceonIndustrial InstrumentationandControl. (ICIC),Pune, India,28–30May2015;pp.1418–1423. [CrossRef] 18. Hanshen,L.;Yuan,Z.; Jinglu,H.;Zhe,L.A localizedNARXNeuralNetworkmodel forShort-termload forecastingbaseduponSelf-OrganizingMapping. InProceedings of the IEEE3rd International Future EnergyElectronicsConferenceandECCEAsia(IFEEC2017—ECCEAsia),Kaohsiung,Taiwan,3–7June2017; pp.749–754. [CrossRef] 19. Fan,G.-F.;Qing,S.;Wang,H.;Hong,W.-C.;Li,H.-J.SupportVectorRegressionModelBasedonEmpirical ModeDecomposition andAuto Regression for Electric Load Forecasting. Energies 2013, 6, 1887–1901. [CrossRef] 20. Dong,Y.;Ma,X.;Ma,C.;Wang, J.ResearchandApplicationofaHybridForecastingModelBasedonData DecompositionforElectricalLoadForecasting.Energies2016,9, 1050. [CrossRef] 21. Dudek,G. Short-TermLoadForecastingUsingRandomForests. In Intelligent Systems’2014. Advances in IntelligentSystemsandComputing; Springer:Cham,Switzerland,2015;pp.821–828. ISBN978-3-319-11310-4. 177
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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