Seite - 116 - in Short-Term Load Forecasting by Artificial Intelligent Technologies

Energies2018,11, 3442 AuthorContributions:Conceptualization,H.R. and I.S.;Methodology,H.R.; Software,H.R.;Validation,H.R.; FormalAnalysis,H.R. and I.S.; Investigation, I.S. and J.B.A.; Resources,H.R. and J.B.A.;DataCuration,H.R.; Writing-Original Draft Preparation, H.R.; Writing-Review&Editing, I.S. and J.B.A.; Supervision, I.S. H.R., IniyanSelvarasanandJ.B.A.haveaddedinputs indevelopingstrategies for totalenergyconsumptionforecasting andthecollectionof thedatasetwas takencarebyH.R.All theauthorswere involvedindraftingandrevising themanuscript. Funding:This researchreceivednoexternal funding. Acknowledgments:Thecorrespondingauthorfirst thanks theAlMighty.Henextexpresseshisgratitudetohis parentswhohelphimgreatly tocarryouthis researchwork.Hewholeheartedly thankshis researchsupervisor forhis invaluableguidance. Theauthors thankK.Padmanathan,whomadeavailablesomeof thedata.Healso thanks theauthoritiesandfaculty inDepartmentofMechanicalengineering,AnnaUniversity,Chennai. Conflictsof Interest:Theauthorsdeclarenoconflictof interest. References 1. CentralElectricityAuthority.Availableonline:www.cea.nic.in (accessedon15October2018). 2. Kalyani,K.A.;Pandey,K.K.Waste toEnergyStatus in India:AShortReview.Renew. Sustain. EnergyRev. 2014,31, 113–120. [CrossRef] 3. Alagh,Y.K.TheFood,WaterandEnergy, InterLinkages forSustainableDevelopment in India.SouthAsian Surv. 2010,17, 159–178. [CrossRef] 4. Taylor, J.W.Tripleseasonalmethodsforshort-termelectricitydemandforecasting.Eur. J.Oper. Res. 2010, 204, 139–152. [CrossRef] 5. Taylor, J.W.;McSharry,P.E.Short-TermLoadForecastingMethods:AnEvaluationBasedonEuropeanData. IEEETrans. PowerSyst. 2008,22, 2213–2219. [CrossRef] 6. Park,D.C.;El-Sharkawi,M.A.;Marks,R.J.;Atlas,L.E.;Damborg,M.J.ElectricLoadForecastingUsingan ArtificialNeuralNetwork. IEEETrans. PowerSyst. 1991,6, 442–449. [CrossRef] 7. Mohamed, Z.; Bodger, P. Forecasting electricity consumption in New Zealand using economic and demographicvariables.Energy2005,30, 1833–1843. [CrossRef] 8. Haida,T.;Muto,S.Regressionbasedpeakloadforecastingusingatransformationtechnique. IEEETrans. PowerSyst. 1994,9, 1788–1794. [CrossRef] 9. Mirasgedis, S.; Safaridis, Y.; Georgopoulou, E.; Lalas, D.P.; Moschovits, M.; Karagiannis, F.; Papakonstantinou,D.Models formid-termelectricitydemandforecasting incorporatingweather influences. Energy2006,31, 208–227. [CrossRef] 10. Da,X.; Jiangyan,Y.; Jilai,Y.Thephysicalseriesalgorithmofmid-longtermloadforecastingofpowersystems. Electr. PowerSyst. Res. 2000,53, 31–37. [CrossRef] 11. Muis, Z.A.; Hashim, H.; Manan, Z.A.; Taha, F.M.; Douglas, P.L. Optimal planning of renewable energy—Integrated electricity generation schemeswithCO2 reduction target. Renew. Energy 2010, 35, 2562–2570. [CrossRef] 12. Kale, R.V.; Pohekar, S.D. Electricity demand and supply scenarios for Maharashtra (India) for 2030: Anapplicationof longrangeenergyalternativesplanning.EnergyPolicy2014,72, 1–13. [CrossRef] 13. Messner,S.;Golodnikov,A.;Gritsevskii,A.AStochasticversionof thedynamic linearprogrammingmodel, MESSAGEIII.Energy1996,21, 775–784. [CrossRef] 14. Tardioli,G.;Kerrigan,R.;Oates,M.;O’Donnell, J.;Finn,D.Datadrivenapproachesforpredictionofbuilding energyconsumptionaturban level.EnergyProcedia2015,78, 3378–3383. [CrossRef] 15. Choi,M.S.;Xiang,L.;Lee,S.J.;Kim,T.W.AnInnovativeApplicationMethodofMonthlyLoadForecasting forSmart IEDs. J.Electr. Eng. Technol. 2013,8, 984–990. [CrossRef] 16. Yalcinoz, T.; Eminoglu,U. Short termandmedium termpowerdistribution load forecastingbyneural networks.EnergyConvers.Manag. 2005,46, 1393–1405. [CrossRef] 17. Chen,G.J.;Li,K.K.;Chung,T.S.;Sun,H.B.;Tang,G.Q.Applicationofan innovativecombinedforecasting methodinpowersystemloadforecasting.Electr. PowerSyst. Res. 2001,59, 131–137. [CrossRef] 18. Mestekemper,T.;Kauermann,G.;Smith,M.S.Acomparisonofperiodicautoregressiveanddynamic factor models in intradayenergydemandforecasting. Int. J.Forecast. 2013,29, 1–12. [CrossRef] 116