Page - 64 - in Short-Term Load Forecasting by Artificial Intelligent Technologies
Image of the Page - 64 -
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Energies2019,12, 164
25. Nadimi,V.;Azadeh,A.;Pazhoheshfar,P.; Saberi,M.AnAdaptive-Network-BasedFuzzyInferenceSystem
forLong-TermElectricConsumptionForecasting (2008â2015): ACaseStudyof theGroupofSeven (G7)
IndustrializedNations:USA,Canada,Germany,UnitedKingdom, Japan,FranceandItaly. InProceedings
of the FourthUKSimEuropean SymposiumonComputerModeling and Simulation, Pisa, Italy, 17â19
November2010;pp. 301â305.
26. Lou,C.W.;Dong,M.C.Modelingdatauncertaintyonelectric loadforecastingbasedonType-2 fuzzy logic
set theory.Eng.Appl.Artif. Intell. 2012,25, 1567â1576. [CrossRef]
27. Amjaday,N.;Keynia,F.Day-AheadPriceForecastingofElectricityMarketsbyMutualInformationTechnique
andCascadedNeuro-EvolutionaryAlgorithm. IEEETrans. PowerSyst. 2009,24, 306â318. [CrossRef]
28. Amjady,N.;Keynia,F.;Zareipour,H.Short-TermLoadForecastofMicrogridsbyaNewBilevelPrediction
Strategy. IEEETrans. SmartGrid2014,1, 286â294. [CrossRef]
29. Liu,N.;Tang,Q.;Zhang, J.;Fan,W.;Liu, J.AHybridForecastingModelwithParameterOptimizationfor
Short-termLoadForecastingofMicro-grids.Appl. Energy2014, 129,336â345. [CrossRef]
30. Ahmad,A.; Javaid,N.;Alrajeh,N.;Khan,Z.A.;Qasim,U.;Khan,A.AmodiïŹedfeatureselectionandartiïŹcial
neural network-basedday-ahead load forecastingmodel for a smart grid. Appl. Sci. 2015, 5, 1756â1772.
[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, 2587â2596.
[CrossRef]
32. Bunn,D.W.;Farmer,E.D.ComparativeModels forElectricalLoadForecasting;Wiley:NewYork,NY,USA,1985;
pp.13â30.
33. Ahmad, I.;Abdullah,A.B.;Alghamdi,A.S.ApplicationofartiïŹcialneuralnetwork indetectionofprobing
attacks. IEEESympos. Ind. Electron.Appl. 2009, 57â62.
34. Malki, H.A.; Karayiannis, N.B.; Balasubramanian,M. Short termelectric power load forecastingusing
feedforwardneuralnetworks.Exp. Syst. 2004,21, 157â167. [CrossRef]
35. Hahn,H.;Meyer-Nieberg,S.;Pickl,S.Electric loadforecastingmethods: Tools fordecisionmaking.Eur. J.
Oper. Res. 2009,199, 902â907. [CrossRef]
36. Amakali, S.DevelopmentofModels for Short-TermLoadForecastingUsingArtïŹcialNeuralNetworks.
MasterâsThesis,CapePeninsulaUniversityofTechnology,CapeTown,SouthAfrica,2008.
37. Valova, I.; Szer,D.;Gueorguieva,N.;Buer,A.Aparallelgrowingarchitecture forself-organizingmapswith
unsupervised learning.Neurocomputing2005,68, 177â195. [CrossRef]
38. Anderson, J.; Silverstein, J.; Ritz, S.; Jones,R.Distinctive features, categoricalperceptionandprobability
learning: Someapplicationsonaneuralmodel.Psychol. Rev. 1977,84, 413â451. [CrossRef]
39. Yang,H.T.;Liao, J.T.;Lin,C.I.ALoadForecastingMethodforHEMSApplications. InProceedingsof the
2013IEEEGrenobleConference,Grenoble,France,16â20 June2013;pp. 1â6.
40. Amjady,N.;Keynia,F.Electricitymarketpricespikeanalysisbyahybriddatamodelandfeatureselection
technique.Electr. PowerSyst. Res. 2010,80, 318â327. [CrossRef]
41. Amjady,N.;Keynia,F.Short-termloadforecastingofpowersystemsbycombinationofwavelet transform
andneuro-evolutionaryalgorithm. J.Energy2009,34, 46â57. [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 classiïŹcation of
spontaneous electroencephalographic signals duringmental tasks. IEEETrans. Biomed. Eng. 1998, 45,
277â286. [CrossRef] [PubMed]
44. Lasseter, R.H.; Piagi, P. Microgrid: A conceptual solution. In Proceedings of the IEEE International
ConferenceonPowerElectronicsSpecialists,Aachen,Germany,20â25 June2004;pp. 4285â4290.
45. Storn, R.; Price, K.Differential evolutionâAsimple andefïŹcient heuristic for global optimizationover
continuousspaces. J.Glob.Optim. 2009,11, 341â359. [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
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