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Energies 2018,11, 213 Conflictsof Interest:Theauthorsdeclarenoconflictof interest. References 1. Raza,M.Q.;Khosravi,A.Areviewonartificial intelligencebasedloaddemandforecastingtechniques for smartgridandbuildings.Renew. Sustain. EnergyRev. 2015,50, 1352–1372. [CrossRef] 2. DaGraçaCarvalho,M.;Bonifacio,M.;Dechamps,P.Buildingalowcarbonsociety.Energy2011,36,1842–1847. [CrossRef] 3. Jiang,B.; Sun,Z.;Liu,M.China’senergydevelopmentstrategyunder the low-carboneconomy.Energy2010, 35, 4257–4264. [CrossRef] 4. Cho,H.;Goude,Y.;Brossat,X.;Yao,Q.Modelingandforecastingdailyelectricity loadcurves:Ahybridapproach. J.Am. Stat.Assoc. 2013,108, 7–21. [CrossRef] 5. Javed, F.; Arshad,N.;Wallin, F.; Vassileva, I.; Dahlquist, E. Forecasting for demand response in smart grids:Ananalysisonuseofanthropologicandstructuraldataandshort termmultiple loads forecasting. Appl.Energy2012,96, 150–160. [CrossRef] 6. Iwafune, Y.; Yagita, Y.; Ikegami, T.; Ogimoto,K. Short-term forecasting of residential building load for distributedenergymanagement. InProceedingsof the2014IEEEInternationalEnergyConference,Cavtat, Croatia,13–16May2014;pp.1197–1204. [CrossRef] 7. ShortTermElectricityLoadForecastingonVaryingLevelsofAggregation.Availableonline: https://arxiv. org/abs/1404.0058v3(accessedon11January2018). 8. Gerwig,C.Shorttermloadforecastingforresidentialbuildings—Anextensiveliteraturereview.Smart Innov. Syst. 2015,39, 181–193. 9. Hippert,H.S.;Pedreira,C.E.;Souza,R.C.Neuralnetworksforshort-termloadforecasting:Areviewandevaluation. IEEETrans. PowerSyst. 2001,16, 44–55. [CrossRef] 10. Metaxiotis, K.; Kagiannas,A.; Askounis, D.; Psarras, J. Artificial intelligence in short term electric load forecasting:Astate-of-the-artsurveyfortheresearcher.EnergyConvers.Manag.2003,44,1524–1534. [CrossRef] 11. Tzafestas,S.;Tzafestas,E.Computational intelligence techniques for short-termelectric loadforecasting. J. Intell. Robot. Syst. 2001,31, 7–68. [CrossRef] 12. Ghayekhloo, M.; Menhaj, M.B.; Ghofrani, M. A hybrid short-term load forecasting with a new data preprocessingframework.Electr. PowerSyst. Res. 2015,119, 138–148. [CrossRef] 13. Xia,C.;Wang, J.;McMenemy,K. Short,mediumand long term load forecastingmodel andvirtual load forecasterbasedonradialbasis functionneuralnetworks. Int. J.Electr. PowerEnergySyst. 2010,32, 743–750. [CrossRef] 14. Bughin, J.;Hazan,E.;Ramaswamy,S.;Chui,M.Artificial Intelligence—TheNextDigitalFrontier?Mckinsey Global Institute:NewYork,NY,USA,2017;pp.1–80. 15. Oh,C.; Lee,T.;Kim,Y.; Park, S.;Kwon,S.B.; Suh,B.Usvs. Them: UnderstandingArtificial Intelligence Technophobiaover theGoogleDeepMindChallengeMatch. InProceedingsof the2017CHIConferenceon HumanFactors inComputingSystems,Denver,CO,USA,6–11May2017;pp.2523–2534. [CrossRef] 16. Skilton,M.;Hovsepian,F.ExampleCaseStudiesof ImpactofArtificial IntelligenceonJobsandProductivity. In4th IndustrialRevolution;NationalAcademiesPress:Washingtom,DC,USA,2018;pp.269–291. 17. Ekonomou,L.; Christodoulou,C.A.;Mladenov,V.Ashort-term load forecastingmethodusingartificial neuralnetworksandwaveletanalysis. Int. J.PowerSyst. 2016,1, 64–68. 18. Valgaev,O.;Kupzog,F.Low-VoltagePowerDemandForecastingUsingK-NearestNeighborsApproach. InProceedingsof theInnovativeSmartGridTechnologies—Asia (ISGT-Asia),Melbourne,VIC,Australia, 28November–1December2016. 19. Valgaev,O.;Kupzog,F.BuildingPowerDemandForecastingUsingK-NearestNeighborsModel—InitialApproach. In Proceedings of the IEEE PESAsia-Pacific Power Energy Conference, Xi’an, China, 25–28October2016; pp.1055–1060. 20. Humeau, S.;Wijaya, T.K.; Vasirani,M.; Aberer,K. Electricity load forecasting for residential customers: Exploitingaggregationandcorrelationbetweenhouseholds. InProceedingsof the2013Sustainable Internet andICTforSustainability,Palermo, Italy,30–31October2013. 428
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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