Page - 80 - in Short-Term Load Forecasting by Artificial Intelligent Technologies

Energies2018,11, 3433 20. Bedi, J.;Toshniwal,D.Empiricalmodedecompositionbaseddeeplearningforelectricitydemandforecasting. IEEEAccess2018,6, 49144–49156. [CrossRef] 21. Liu,H.;Mi,X.; Li,Y.Anexperimental investigationof threenewhybridwindspeed forecastingmodels usingmulti-decomposingstrategyandelmalgorithm.Renew. Energy2018,123, 694–705. [CrossRef] 22. Lahmiri, S.Comparingvariational andempiricalmodedecomposition in forecastingday-aheadenergy prices. IEEESyst. J.2017,11, 1907–1910. [CrossRef] 23. Dragomiretskiy,K.;Zosso,D.Variationalmodedecomposition. IEEETrans. SignalProcess. 2014,62, 531–544. [CrossRef] 24. Huang,N.;Yuan,C.;Cai,G.;Xing,E.Hybridshort termwindspeedforecastingusingvariationalmode decompositionandaweightedregularizedextremelearningmachine.Energies2016,9, 989. [CrossRef] 25. Lin,Y.;Luo,H.;Wang,D.;Guo,H.;Zhu,K.Anensemblemodelbasedonmachine learningmethodsand datapreprocessingforshort-termelectric loadforecasting.Energies2017,10, 1186. [CrossRef] 26. Ruiz-Abellón,M.;Gabaldón,A.;Guillamón,A.Loadforecastingforacampusuniversityusingensemble methodsbasedonregressiontrees.Energies2018,11, 2038. [CrossRef] 27. Dong,Y.;Zhang,Z.;Hong,W.-C.Ahybridseasonalmechanismwithachaoticcuckoosearchalgorithmwith asupportvector regressionmodel forelectric loadforecasting.Energies2018,11, 1009. [CrossRef] 28. Li,M.-W.;Geng, J.;Hong,W.-C.; Zhang,Y.Hybridizing chaotic andquantummechanismsand fruitfly optimization algorithmwith least squares support vector regressionmodel in electric load forecasting. Energies2018,11, 2226. [CrossRef] 29. Sheng,H.; Xiao, J.; Cheng, Y.; Ni, Q.;Wang, S. Short-term solar power forecasting based onweighted gaussianprocess regression. IEEETrans. Ind. Electron. 2018,65, 300–308. [CrossRef] 30. Manic, M.; Amarasinghe, K.; Rodriguez-Andina, J.J.; Rieger, C. Intelligent buildings of the future: Cyberaware, deep learningpowered, andhuman interacting. IEEE Ind. Electron. Mag. 2016, 10, 32–49. [CrossRef] 31. Li, C.; Ding, Z.; Yi, J.; Lv, Y.; Zhang, G.Deep belief network based hybridmodel for building energy consumptionprediction.Energies2018,11, 242. [CrossRef] 32. Wang,Y.;Zhang,N.;Tan,Y.;Hong,T.;Kirschen,D.S.;Kang,C.Combiningprobabilistic loadforecasts. IEEETrans. SmartGrid2018.Availableonline: https://arxiv.org/abs/1803.06730(accessedon5November2018). 33. Wang, J.;Gao,Y.;Chen,X.Anovelhybrid intervalpredictionapproachbasedonmodified lowerupper boundestimation incombinationwithmulti-objectivesalpswarmalgorithmforshort-termloadforecasting. Energies2018,11, 1561. [CrossRef] 34. Sun,W.;Zhang,C.Ahybridba-elmmodelbasedonfactoranalysisandsimilar-dayapproachforshort-term loadforecasting.Energies2018,11, 1282. [CrossRef] 35. Ruiz,L.G.B.;Cuéllar,M.P.;Calvo-Flores,M.D.; Jiménez,M.D.C.P.Anapplicationofnon-linearautoregressive neuralnetworks topredictenergyconsumption inpublicbuildings.Energies2016,9, 684. [CrossRef] 36. DiPietro,R.;Rupprecht,C.;Navab,N.;Hager,G.D.Analyzingandexploitingnarxrecurrentneuralnetworks for long-termdependencies. arXiv2017, arXiv:1702.07805. 37. Bouktif, S.;Fiaz,A.;Ouni,A.;Serhani,M.Optimaldeep learning lstmmodel forelectric loadforecasting usingfeatureselectionandgeneticalgorithm:Comparisonwithmachine learningapproaches.Energies2018, 11, 1636. [CrossRef] 38. Kong,W.;Dong,Z.Y.; Jia,Y.;Hill,D.J.;Xu,Y.;Zhang,Y.Short-termresidential loadforecastingbasedonlstm recurrentneuralnetwork. IEEETrans. SmartGrid2018. [CrossRef] 39. Chen,K.;Chen,K.;Wang,Q.;He,Z.;Hu, J.;He, J. Short-termloadforecastingwithdeepresidualnetworks. IEEETrans.SmartGrid2018.Availableonline: https://arxiv.org/abs/1805.11956(accessedon5November2018). 40. Shi,H.;Xu,M.;Li,R.Deeplearningforhousehold loadforecasting—Anovelpoolingdeeprnn. IEEETrans. SmartGrid2018,9, 5271–5280. [CrossRef] 41. Kuo, P.-H.; Huang, C.-J. A high precision artificial neural networksmodel for short-term energy load forecasting.Energies2018,11, 213. [CrossRef] 42. Wang,Y.;Liu,M.;Bao,Z.;Zhang,S.Short-termloadforecastingwithmulti-sourcedatausinggatedrecurrent unitneuralnetworks.Energies2018,11, 1138. [CrossRef] 43. Merkel,G.;Povinelli,R.;Brown,R.Short-termloadforecastingofnaturalgaswithdeepneuralnetwork regression.Energies2018,11, 2008. [CrossRef] 80