Page - 63 - in Short-Term Load Forecasting by Artificial Intelligent Technologies

Energies2019,12, 164 3. National Institute of Standards and Technology. NIST Framework and Roadmap for Smart Grid InteroperabilityStandards.Release1.0. 2010.Availableonline: http://www.nist.gov/publicaffairs/releases/ upload/smartgridinteroperabilityfinal.pdf (accessedon10November2018 ). 4. Leiva, J.;Palacios,A.;Aguado, J.A.Smartmetering trends, implicationsandnecessities:Apolicyreview. Renew. Sustain. EnergyRev. 2016,55, 227–233. [CrossRef] 5. HowDoesForecastingEnhanceSmartGridBenefits?SASInstitute Inc.:Cary,NC,USA,2015;pp. 1–9. 6. Hernandez,L.;Baladron,C.;Aguiar, J.M.;Carro,B.;Sanchez-Esguevillas,A.J.;Lloret, J.;Massana, J.Asurvey on electric power demand forecasting: Future trends in smart grids, microgrids and smart buildings. IEEECommun. Surv. Tutor. 2014,16, 1460–1495. [CrossRef] 7. Vardakas, J.S.; Zorba, N.; Verikoukis, C.V. A Survey onDemandResponse Programs in Smart Grids: PricingMethodsandOptimizationAlgorithms. IEEECommun. Surv. Tutor. 2015,17, 152–178. [CrossRef] 8. Hippert,H.S.;Pedreira,C.E.;Souza,C.R.NeuralNetworks forShort-TermLoadForecasting:Areviewand Evaluation. IEEETrans. PowerSyst. 2001,16, 44–51. [CrossRef] 9. Raza,M.Q.;Khosravi,A.Areviewonartificial intelligencebasedloaddemandforecastingtechniques for smartgridandbuildings.Renew. Sustain. EnergyRev. 2015,50, 1352–1372. [CrossRef] 10. Hagan,M.T.;Behr,S.M.TheTimeSeriesApproachtoShortTermLoadForecasting. IEEETrans. PowerSyst. 1987,2, 785–791. [CrossRef] 11. Niu,D.;Wang,Y.;Wu,D.Power loadforecastingusingsupportvectormachineandantcolonyoptimization. Exp. Syst.Appl. 2010,37, 2531–2539. [CrossRef] 12. Li,H.;Guo, S.; Zhao,H.; Su,C.;Wang,B.AnnualElectricLoadForecastingbyaLeast SquaresSupport VectorMachinewithaFruitFlyOptimizationAlgorithm.Energies2012,5, 4430–4445. [CrossRef] 13. Aung,Z.;Toukhy,M.;Williams, J.R.; S’anchez,A.;Herrero,S.TowardsAccurateElectricityLoadForecasting inSmartGrids. InProceedingsoftheFourthInternationalConferenceonAdvancesinDatabases,Knowledge, andDataApplications,Athens,Greece,2–6 June2012;pp. 51–57. 14. Meidani, H.; Ghanem, R. Multiscale Markov models with random transitions for energy demand management.EnergyBuild. 2013,61, 267–274. [CrossRef] 15. Nijhuis,M.;Gibescu,M.;Cobben, J.F.Bottom-upMarkovChainMonteCarloapproachforscenariobased residential loadmodellingwithpubliclyavailabledata.EnergyBuild. 2016,112, 121–129. [CrossRef] 16. Guo, Z.; Wang, Z.J.; Kashani, A. Home appliance loadmodeling from aggregated smart meter data. IEEETrans. PowerSyst. 2015,30, 254–262. [CrossRef] 17. Gruber, J.K.; Prodanovic,M.Residential energy loadprofile generationusing a probabilistic approach. In Proceedings of the IEEEUKSim-AMSS 6th EuropeanModelling Symposium, Valetta, Malta, 14–16 November2012;pp. 317–322. 18. Kou,P.;Gao,F.Asparseheteroscedasticmodel for theprobabilistic load forecasting inenergy-intensive enterprises.Electr. PowerEnergySyst. 2014,55, 144–154. [CrossRef] 19. Fan,S.;Hyndman,R.J.Short-TermLoadForecastingBasedonaSemi-ParametricAdditiveModel. IEEETrans. PowerSyst. 2012,27, 134–141. [CrossRef] 20. Goude, Y.; Nedellec, R.; Kong, N. Local Short and Middle Term Electricity Load Forecasting with Semi-ParametricAdditiveModels. IEEETrans. PowerSyst. 2014,5, 440–446. [CrossRef] 21. Doveh,E.;Feigin,P.;Greig,D.;Hyams,L.ExperiencewithFNNModels forMediumTermPowerDemand Predictions. IEEETrans. PowerSyst. 1999,14, 538–546. [CrossRef] 22. Mahmoud,T.S.;Habibi,D.;Hassan,M.Y.;Bass,O.Modellingself-optimisedshort termloadforecastingfor mediumvoltage loadsusing tunningfuzzysystemsandArtificialNeuralNetworks.EnergyConvers.Manag. 2015,106, 1396–1408. [CrossRef] 23. Wang,Z.Y.DevelopmentCase-basedReasoningSystemforShorttermLoadForecasting. InProceedings of the IEEERussiaPowerEngineeringSocietyGeneralMeeting,Montreal,QC,Canada,18–22 June2006; pp.1–6. 24. Che, J.;Wang, J.;Wang,G.Anadaptive fuzzycombinationmodelbasedonself-organizingmapandsupport vector regressionforelectric loadforecasting.Energy2012,37, 657–664. [CrossRef] 63