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Energies2019,12, 164 25. Nadimi,V.;Azadeh,A.;Pazhoheshfar,P.; Saberi,M.AnAdaptive-Network-BasedFuzzyInferenceSystem forLong-TermElectricConsumptionForecasting (2008–2015): ACaseStudyof theGroupofSeven (G7) IndustrializedNations:USA,Canada,Germany,UnitedKingdom, Japan,FranceandItaly. InProceedings of the FourthUKSimEuropean SymposiumonComputerModeling and Simulation, Pisa, Italy, 17–19 November2010;pp. 301–305. 26. Lou,C.W.;Dong,M.C.Modelingdatauncertaintyonelectric loadforecastingbasedonType-2 fuzzy logic set theory.Eng.Appl.Artif. Intell. 2012,25, 1567–1576. [CrossRef] 27. Amjaday,N.;Keynia,F.Day-AheadPriceForecastingofElectricityMarketsbyMutualInformationTechnique andCascadedNeuro-EvolutionaryAlgorithm. IEEETrans. PowerSyst. 2009,24, 306–318. [CrossRef] 28. Amjady,N.;Keynia,F.;Zareipour,H.Short-TermLoadForecastofMicrogridsbyaNewBilevelPrediction Strategy. IEEETrans. SmartGrid2014,1, 286–294. [CrossRef] 29. Liu,N.;Tang,Q.;Zhang, J.;Fan,W.;Liu, J.AHybridForecastingModelwithParameterOptimizationfor Short-termLoadForecastingofMicro-grids.Appl. Energy2014, 129,336–345. [CrossRef] 30. Ahmad,A.; Javaid,N.;Alrajeh,N.;Khan,Z.A.;Qasim,U.;Khan,A.AmodiïŹedfeatureselectionandartiïŹcial neural network-basedday-ahead load forecastingmodel for a smart grid. Appl. Sci. 2015, 5, 1756–1772. [CrossRef] 31. Ahmad,A.; Javaid,N.;Guizani,M.;Alrajeh,N.;Khan,Z.A.Anaccurateandfast convergingshort-termload forecastingmodel for industrialapplications inasmartgrid. IEEETrans. Ind. Inform. 2017,13, 2587–2596. [CrossRef] 32. Bunn,D.W.;Farmer,E.D.ComparativeModels forElectricalLoadForecasting;Wiley:NewYork,NY,USA,1985; pp.13–30. 33. Ahmad, I.;Abdullah,A.B.;Alghamdi,A.S.ApplicationofartiïŹcialneuralnetwork indetectionofprobing attacks. IEEESympos. Ind. Electron.Appl. 2009, 57–62. 34. Malki, H.A.; Karayiannis, N.B.; Balasubramanian,M. Short termelectric power load forecastingusing feedforwardneuralnetworks.Exp. Syst. 2004,21, 157–167. [CrossRef] 35. Hahn,H.;Meyer-Nieberg,S.;Pickl,S.Electric loadforecastingmethods: Tools fordecisionmaking.Eur. J. Oper. Res. 2009,199, 902–907. [CrossRef] 36. Amakali, S.DevelopmentofModels for Short-TermLoadForecastingUsingArtïŹcialNeuralNetworks. Master’sThesis,CapePeninsulaUniversityofTechnology,CapeTown,SouthAfrica,2008. 37. Valova, I.; Szer,D.;Gueorguieva,N.;Buer,A.Aparallelgrowingarchitecture forself-organizingmapswith unsupervised learning.Neurocomputing2005,68, 177–195. [CrossRef] 38. Anderson, J.; Silverstein, J.; Ritz, S.; Jones,R.Distinctive features, categoricalperceptionandprobability learning: Someapplicationsonaneuralmodel.Psychol. Rev. 1977,84, 413–451. [CrossRef] 39. Yang,H.T.;Liao, J.T.;Lin,C.I.ALoadForecastingMethodforHEMSApplications. InProceedingsof the 2013IEEEGrenobleConference,Grenoble,France,16–20 June2013;pp. 1–6. 40. Amjady,N.;Keynia,F.Electricitymarketpricespikeanalysisbyahybriddatamodelandfeatureselection technique.Electr. PowerSyst. Res. 2010,80, 318–327. [CrossRef] 41. Amjady,N.;Keynia,F.Short-termloadforecastingofpowersystemsbycombinationofwavelet transform andneuro-evolutionaryalgorithm. J.Energy2009,34, 46–57. [CrossRef] 42. Engelbrecht,A.P.Computational Intelligence: An Introduction, 2nded.; JohnWiley&Sons: NewYork,NY, USA,2007. 43. Anderson, C.W.; Stolz, E.A.; Shamsunder, S. Multivariate autoregressive models for classiïŹcation of spontaneous electroencephalographic signals duringmental tasks. IEEETrans. Biomed. Eng. 1998, 45, 277–286. [CrossRef] [PubMed] 44. Lasseter, R.H.; Piagi, P. Microgrid: A conceptual solution. In Proceedings of the IEEE International ConferenceonPowerElectronicsSpecialists,Aachen,Germany,20–25 June2004;pp. 4285–4290. 45. Storn, R.; Price, K.Differential evolution—Asimple andefïŹcient heuristic for global optimizationover continuousspaces. J.Glob.Optim. 2009,11, 341–359. [CrossRef] 46. PJMElectricityMarket.Availableonline:www.pjm.com(accessedon1February2015). c©2019bytheauthors. LicenseeMDPI,Basel,Switzerland. Thisarticle isanopenaccess articledistributedunder the termsandconditionsof theCreativeCommonsAttribution (CCBY) license (http://creativecommons.org/licenses/by/4.0/). 64
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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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