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Energies2018,11, 1009 10. Maçaira,P.M.;Souza,R.C.;Oliveira,F.L.C.Modellingandforecasting theresidential electricityconsumption inBrazilwithpegelsexponential smoothingtechniques.ProcediaComput. Sci. 2015,55, 328–335. [CrossRef] 11. Al-Hamadi,H.M.;Soliman,S.A.Short-termelectric loadforecastingbasedonKalmanfilteringalgorithm withmovingwindowweatherandloadmodel.Electr. PowerSyst. Res. 2004,68, 47–59. [CrossRef] 12. Zhang,M.;Bao,H.;Yan,L.;Cao, J.;Du, J.Researchonprocessingofshort-termhistoricaldataofdaily load basedonKalmanfilter.PowerSyst. Technol. 2003,9, 39–42. 13. Hippert,H.S.; Taylor, J.W.Anevaluation of Bayesian techniques for controllingmodel complexity and selecting inputs in a neural network for short-term load forecasting. Neural Netw. 2010, 23, 386–395. [CrossRef] [PubMed] 14. Zhang,W.;Yang, J.Forecastingnaturalgasconsumption inChinabyBayesianmodelaveraging.EnergyRep. 2015,1, 216–220. [CrossRef] 15. Kelo, S.; Dudul, S.Awavelet Elmanneural network for short-termelectrical loadpredictionunder the influenceof temperature. Int. J.Electr. PowerEnergySyst. 2012,43, 1063–1071. [CrossRef] 16. Li,H.Z.;Guo,S.; Li,C.J.; Sun, J.Q.Ahybridannualpower load forecastingmodelbasedongeneralized regression neural networkwith fruit fly optimization algorithm. Knowl.-Based Syst. 2013, 37, 378–387. [CrossRef] 17. Ertugrul,Ö.F.Forecastingelectricity loadbyanovel recurrentextremelearningmachinesapproach. Int. J. Electr. PowerEnergySyst. 2016,78, 429–435. [CrossRef] 18. Bennett,C.J.; Stewart,R.A.;Lu, J.W.Forecasting lowvoltagedistributionnetworkdemandprofilesusing apatternrecognitionbasedexpert system.Energy2014,67, 200–212. [CrossRef] 19. Lahouar, A.; Slama, J.B.H. Day-ahead load forecast using random forest and expert input selection. EnergyConvers.Manag. 2015,103, 1040–1051. [CrossRef] 20. Akdemir,B.;Çetinkaya,N.Long-termloadforecastingbasedonadaptiveneural fuzzy inferencesystem usingrealenergydata.EnergyProcedia2012,14, 794–799. [CrossRef] 21. Chaturvedi,D.K.;Sinha,A.P.;Malik,O.P.Short termloadforecastusingfuzzy logicandwavelet transform integratedgeneralizedneuralnetwork. Int. J.Electr. PowerEnergySyst. 2015,67, 230–237. [CrossRef] 22. Lou,C.W.;Dong,M.C.Anovel randomfuzzyneuralnetworks for tacklinguncertaintiesof electric load forecasting. Int. J.Electr. PowerEnergySyst. 2015,73, 34–44. [CrossRef] 23. Bahrami,S.;Hooshmand,R.-A.;Parastegari,M.Short termelectric loadforecastingbywavelet transformand greymodel improvedbyPSO(particle swarmoptimization)algorithm.Energy2014,72, 434–442. [CrossRef] 24. Hahn,H.;Meyer-Nieberg,S.;Pickl,S.Electric loadforecastingmethods: Tools fordecisionmaking.Eur. J. Oper. Res. 2009,199, 902–907. [CrossRef] 25. Vapnik, V.; Golowich, S.; Smola, A. Support vector machine for function approximation, regression estimation,andsignalprocessing.Adv.Neural Inf. Process. Syst. 1996,9, 281–287. 26. Zhang,X.;Ding,S.;Xue,Y.Animprovedmultiplebirthsupportvectormachineforpatternclassification. Neurocomputing2017,225, 119–128. [CrossRef] 27. Hua,X.;Ding,S.Weightedleast squaresprojectiontwinsupportvectormachineswith local information. Neurocomputing2015,160, 228–237. [CrossRef] 28. Hong,W.C.Electric load forecastingbyseasonal recurrentSVR(supportvector regression)withchaotic artificialbeecolonyalgorithm.Energy2011,36, 5568–5578. [CrossRef] 29. Hong,W.-C.;Dong,Y.;Zhang,W.;Chen,L.-Y.;Panigrahi,B.K.Cyclicelectric loadforecastingbyseasonal SVRwithchaoticgeneticalgorithm. Int. J.Electr. PowerEnergySyst. 2013,44, 604–614. [CrossRef] 30. Gandomi, A.H.; Yang, X.S.; Alavi, A.H. Cuckoo search algorithm: Ametaheuristic approach to solve structuraloptimizationproblems.Eng.Comput. 2013,29, 17–35. [CrossRef] 31. Yang, X.S.; Deb, S. Cuckoo search via Lévy flights. In Proceedings of theWorld Congress onNature andBiologically InspiredComputing(NaBic),Coimbatore, India,9–11December2009; IEEEPublications: Coimbatore, India,2009;pp.210–214. 32. Lakshminarayanan,S.;Kaur,D.Optimalmaintenanceschedulingofgeneratorunitsusingdiscrete integer cuckoosearchoptimizationalgorithm.SwarmEvolut. Comput. 2018. [CrossRef] 33. Boushaki, S.I.; Kamel, N.; Bendjeghaba, O. A newquantum chaotic cuckoo search algorithm for data clustering.ExpertSyst.Appl. 2018,96, 358–372. [CrossRef] 34. Daniel, E.; Anitha, J.; Gnanaraj, J.Optimumlaplacianwaveletmaskbasedmedical imageusinghybrid cuckoosearch–greywolfoptimizationalgorithm.Knowl.-BasedSyst. 2017,131, 58–69. [CrossRef] 42
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Short-Term Load Forecasting by Artificial Intelligent Technologies
Title
Short-Term Load Forecasting by Artificial Intelligent Technologies
Authors
Wei-Chiang Hong
Ming-Wei Li
Guo-Feng Fan
Editor
MDPI
Location
Basel
Date
2019
Language
English
License
CC BY 4.0
ISBN
978-3-03897-583-0
Size
17.0 x 24.4 cm
Pages
448
Keywords
Scheduling Problems in Logistics, Transport, Timetabling, Sports, Healthcare, Engineering, Energy Management
Category
Informatik
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