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Energies2018,11, 1009 35. Dao,T.-P.;Huang,S.-C.;Thang,P.T.HybridTaguchi-cuckoosearchalgorithmforoptimizationofacompliant focuspositioningplatform.Appl. SoftComput. 2017,57, 526–538. [CrossRef] 36. Puspaningrum,A.;Sarno,R.Ahybridcuckoooptimizationandharmonysearchalgorithmforsoftwarecost estimation.ProcediaComput. Sci. 2017,124, 461–469. [CrossRef] 37. Huang,L.;Ding, S.; Yu, S.;Wang, J.; Lu,K.Chaos-enhancedCuckoosearchoptimizationalgorithms for globaloptimization.Appl.Math.Model. 2016,40, 3860–3875. [CrossRef] 38. Li, X.; Yin, M. A particle swarm inspired cuckoo search algorithm for real parameter optimization. SoftComput. 2016,20, 1389–1413. [CrossRef] 39. Sheng, Y.; Pan, H.; Xia, L.; Cai, Y.; Sun, X. Hybrid chaos particle swarm optimization algorithm and application inbenzene-tolueneflashvaporization. J.ZhejiangUniv. Technol. 2010,38, 319–322. 40. Li,M.;Hong,W.-C.; Kang,H.Urban trafficflowforecastingusingGauss-SVRwith catmapping, cloud modelandPSOhybridalgorithm.Neurocomputing2013,99, 230–240. [CrossRef] 41. Yang,X.S.;Deb,S.Cuckoosearch: Recentadvancesandapplications.NeuralComput.Appl. 2014,24, 169–174. [CrossRef] 42. Koc,E.;Altinay,G.Ananalysisofseasonality inmonthlyperpersontourist spendinginTurkish inbound tourismfromamarketsegmentationperspective.Tour.Manag. 2007,28, 227–237. [CrossRef] 43. Goh, C.; Law, R.Modeling and forecasting tourismdemand for arrivalswith stochastic nonstationary seasonalityandintervention.Tour.Manag. 2002,23, 499–510. [CrossRef] 44. Wang, J.;Zhu,W.;Zhang,W.;Sun,D.Atrendfixedonfirstlyandseasonaladjustmentmodelcombinedwith theε-SVRforshort-termforecastingofelectricitydemand.EnergyPolicy2009,37, 4901–4909. [CrossRef] 45. Martens,K.;Chang,Y.C.;Taylor,S.Acomparisonofseasonaladjustmentmethodswhenforecastingintraday volatility. J.Financ. Res. 2002,25, 283–299. [CrossRef] 46. Deo,R.;Hurvich,C.;Lu,Y.Forecastingrealizedvolatilityusinga long-memorystochasticvolatilitymodel: Estimation,predictionandseasonaladjustment. J.Econom. 2006,131, 29–58. [CrossRef] 47. The Electricity Demand Data of National Electricity Market. Available online: https://www.aemo. com.au/Electricity/National-Electricity-Market-NEM/Data-dashboard#aggregated-data (accessed on 2March2018). 48. The Electricity Demand Data of the New York Independent System Operator (NYISO). Available online: http://www.nyiso.com/public/markets_operations/market_data/load_data/index.jsp (accessed on2April2018). 49. Schalkoff,R.J.ArtificialNeuralNetworks;McGraw-Hill:NewYork,USA,1997. 50. Diebold,F.X.;Mariano,R.S.Comparingpredictiveaccuracy. J.Bus. Ecosn. Stat. 1995,13, 134–144. 51. Derrac, J.;García,S.;Molina,D.;Herrera,F.Apractical tutorialontheuseofnonparametric statistical tests asamethodologyforcomparingevolutionaryandswarmintelligencealgorithms.SwarmEvolut. Comput. 2011,1, 3–18. [CrossRef] 52. Wilcoxon,F. Individualcomparisonsbyrankingmethods.Biom.Bull. 1945,1, 80–83. [CrossRef] 53. Friedman,M.Acomparisonofalternativetestsofsignificancefortheproblemofmrankings.Ann.Math. Stat. 1940,11, 86–92. [CrossRef] ©2018bytheauthors. LicenseeMDPI,Basel,Switzerland. Thisarticle isanopenaccess articledistributedunder the termsandconditionsof theCreativeCommonsAttribution (CCBY) license (http://creativecommons.org/licenses/by/4.0/). 43
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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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