Seite - 227 - in Short-Term Load Forecasting by Artificial Intelligent Technologies

Energies2018,11, 1900 References 1. Verhelst, J.;Van,H.G.;Saelens,D.Model selection forcontinuouscommissioningofHVAC-systems inoffice buildings:Areview.Renew. Sustain. EnergyRev. 2017,76, 673–686. [CrossRef] 2. Xia,C.H.;Xiang,X.J.;Hu,X.Y.Designofvirtual instrumentfor loadforecastingbasedonRBFneuralnetwork andweatherdata.Relay2007,35, 29–32. 3. Ruzic, S.; Vuckovic,A.;Nikolic,N.Weather sensitivemethod for short term load forecasting inElectric PowerUtilityofSerbia. IEEETrans. PowerSyst. 2003,18, 1581–1586. [CrossRef] 4. Wi,Y.M.; Joo,S.K.;Songk,K.B.Holiday loadforecastingusingfuzzypolynomial regressionwithweather featureselectionandadjustment. IEEETrans. PowerSyst. 2012,27, 596–603. [CrossRef] 5. Cao, L. Support vectormachines experts for time series forecasting. Neurocomputing 2003, 51, 321–339. [CrossRef] 6. Huang,W.;Nakamori,Y.;Wang,S.Y.Forecastingstockmarketmovementdirectionwithsupportvector machine.Comput.Oper. Res. 2005,32, 2513–2522. [CrossRef] 7. Pai,P.F.;Lin,C.S.AhybridARIMAandsupportvectormachinesmodel instockprice forecasting.Omega 2005,33, 497–505. [CrossRef] 8. Pai,P.F.;Hong,W.C.Softwarereliability forecastingbysupportvectormachineswithsimulatedannealing algorithms. J.Syst. Softw. 2006,79, 747–755. [CrossRef] 9. Mohandes,M.A.; Halawani, T.O.; Rehman, S.; Hussain,A.A. Support vectormachines forwind speed prediction.Renew. Energy2004,29, 939–947. [CrossRef] 10. Amari,S.;Wu,S. Improvingsupportvectormachineclassiersbymodifyingkernel functions.NeuralNetw. 1999,12, 783–789. [CrossRef] 11. Cherkassky,V.;Ma, Y. Practical selection of SVMparameters andnoise estimation for SVMregression. NeuralNetw. 2004,17, 113–126. [CrossRef] 12. Barman,M.;Choudhury,N.B.D.;Sutradhar,S.AregionalhybridGOA-SVMmodelbasedonsimilarday approachforshort-termloadforecasting inAssam, India.Energy2018,145, 710–720. [CrossRef] 13. Li, X.; Shao,M.; Ding, L.; Xu,G. Particle swarmoptimization-basedLS-SVMfor building cooling load prediction. J.Comput. 2010,5, 614–621. [CrossRef] 14. Duanmu,L.;Wang,Z.;Zhai,Z.J.Asimplifiedmethod topredicthourlybuildingcooling load forurban energyplanning.EnergyBuild. 2013,58, 281–291. [CrossRef] 15. Wang,H.;Chen,Q.;Ren,Z. Impactof climatechangeheatingandcoolingenergyuse inbuildings in the UnitedStates.EnergyBuild. 2014,82, 428–436. [CrossRef] 16. Jiang,Y.Generationof typicalmeteorologicalyear fordifferentclimatesofChina.Energy2010,35, 1946–1953. [CrossRef] 17. Chen,D.;Wang,X.;Ren,Z.Selectionofclimaticvariablesandtimescales for futureweatherpreparation in buildingheatingandcoolingenergypredictions.EnergyBuild. 2012,51, 223–233. [CrossRef] 18. Petersen,S.;Bundgaard,K.W.Theeffectofweather forecastuncertaintyonapredictivecontrol concept for buildingsystemsoperation.Appl. Energy2014,116, 311–321. [CrossRef] 19. Eua-Arporn, B.; Yokoyama, A. Short-term operating strategywith consideration of load forecast and generatingunituncertainty. IEEJTrans. PowerEnergy2007,127, 1159–1167. [CrossRef] 20. Domínguez-Muñoz,F.;Cejudo-López,J.M.;Carrillo-Andrés,A.Uncertaintyinpeakcoolingloadcalculations. EnergyBuild. 2010,42, 1010–1018. [CrossRef] 21. Douglas,A.P.; Breipohl,A.M.; Lee, F.N.; Adapa,R. The impacts of temperature forecast uncertainty on Bayesian loadforecasting. IEEETrans. PowerSyst. 1998,13, 1507–1513. [CrossRef] 22. MacDonald, I.QuantifyingtheEffectsofUncertainty inBuildingSimulation. Ph.DThesis,Departmentof MechanicalEngineering,UniversityofStrathclyde,Glasgow,UK,2002. 23. Domínguez-Muñoz,F.;Anderson,B.;Cejudo-López, J.M.;Carrillo-Andrés,A.Uncertainty in the thermal conductivity of insulationmaterials. In Proceedings of the Eleventh International IBPSAConference, Glasgow,UK,27–30 July2009. 24. Sten,D.W.; Augenbroe,G.Analysis of uncertainty in buildingdesign evaluations and its implications. EnergyBuild. 2002,34, 951–958. 25. Reiter,D.TheMonteCarloMethod,anIntroduction.Lect.NotesPhys. 2008,50, 63–78. 227