Page - 22 - in Short-Term Load Forecasting by Artificial Intelligent Technologies

Energies2018,11, 2226 43. Li, M.W.; Geng, J.; Hong, W.C.; Chen, Z.Y. A novel approach based on the Gauss-vLS-SVR with a newhybrid evolutionary algorithmand input vector decisionmethod for port throughput forecasting. NeuralComput.Appl.2017,28, S621–S640. [CrossRef] 44. Li,M.W.;Hong,W.C.;Geng, J.;Wang, J.Berthandquaycranecoordinatedschedulingusingchaoscloud particleswarmoptimizationalgorithm.NeuralComput.Appl. 2017,28, 3163–3182. [CrossRef] 45. Xiong, Y. Study on Short-TermMicro-Grid LoadForecastingBased on IGA-PSORBFNeuralNetwork. Master’sThesis,SouthChinaUniversityofTechnology,Guangzhou,China,2016. 46. Hong,T.; Pinson,P.; Fan, S.; Zareipour,H.; Troccoli,A.;Hyndman,R.J. Probabilistic energy forecasting: GlobalEnergyForecastingCompetition2014andbeyond. Int. J.Forecast. 2016,32, 896–913. [CrossRef] 47. Hong, W.C. Application of seasonal SVR with chaotic immune algorithm in traffic flow forecasting. NeuralComput.Appl.2012,21, 583–593. [CrossRef] 48. Diebold,F.X.;Mariano,R.S.Comparingpredictiveaccuracy. J.Bus. Econ. Stat. 1995,13, 134–144. 49. Derrac, J.;García,S.;Molina,D.;Herrera,F.Apractical tutorialontheuseofnonparametric statistical tests as amethodology for comparingevolutionaryandswarmintelligencealgorithms. SwarmEvol. Comput. 2011,1, 3–18. [CrossRef] 50. Wilcoxon,F. Individualcomparisonsbyrankingmethods.Biom.Bull. 1945,1, 80–83. [CrossRef] ©2018bytheauthors. LicenseeMDPI,Basel,Switzerland. Thisarticle isanopenaccess articledistributedunder the termsandconditionsof theCreativeCommonsAttribution (CCBY) license (http://creativecommons.org/licenses/by/4.0/). 22