Web-Books
in the Austria-Forum
Austria-Forum
Web-Books
Informatik
Short-Term Load Forecasting by Artificial Intelligent Technologies
Page - 285 -
  • User
  • Version
    • full version
    • text only version
  • Language
    • Deutsch - German
    • English

Page - 285 - in Short-Term Load Forecasting by Artificial Intelligent Technologies

Image of the Page - 285 -

Image of the Page - 285 - in Short-Term Load Forecasting by Artificial Intelligent Technologies

Text of the Page - 285 -

Energies2018,11, 1605 4. Youssef,A.M.; Pourghasemi,H.R.; Pourtaghi,Z.S.;Al-Katheeri,M.M.Landslide susceptibilitymapping usingrandomforest,boostedregressiontree, classificationandregressiontree,andgeneral linearmodels andcomparisonof theirperformanceatWadiTayyahBasin,AsirRegion,SaudiArabia.Landslides2016,13, 839–856. [CrossRef] 5. Shrestha,D.L.;Solomatine,D.P.ExperimentswithAdaBoost.RT,animprovedboostingschemeforregression. NeuralComput. 2006,18, 1678–1710. [CrossRef] [PubMed] 6. Morra, J.H.;Tu,Z.;Apostolova,L.G.;Green,A.E.;Toga,A.W.;Thompson,P.M.ComparisonofAdaBoostand supportvectormachines fordetectingAlzheimer’sdisease throughautomatedhippocampalsegmentation. IEEETrans.Med. Imaging2010,29, 30–43. [CrossRef] [PubMed] 7. Guo,L.;Ge,P.-S.; Zhang,M.-H.; Li, L.-H.; Zhao,Y.-B.Pedestriandetection for intelligent transportation systemscombiningadaboostalgorithmandsupportvectormachine.ExpertSyst. Appl. 2012,39, 4274–4286. [CrossRef] 8. Aldave,R.;Dussault, J.-P.Systematicensemble learningforregression. arXiv, 2014. 9. Lemke,C.;Gabrys,B.Meta-learningfor timeseries forecastingandforecastcombination.Neurocomputing 2010,73, 2006–2016. [CrossRef] 10. Crone,S.F.;Hibon,M.;Nikolopoulos,K.Advances in forecastingwithneuralnetworks?Empirical evidence fromtheNN3competitionontimeseriesprediction. Int. J.Forecast. 2011,27, 635–660. [CrossRef] 11. Ardakani, F.J.;Ardehali,M.M.Novel effects ofdemandsidemanagementdataonaccuracyof electrical energy consumptionmodeling and long-term forecasting. Energy Convers. Manag. 2014, 78, 745–752. [CrossRef] 12. Gómez-Gil,P.;Ramírez-Cortes, J.M.;Hernández,S.E.P.;Alarcón-Aquino,V.Aneuralnetworkschemefor long-termforecastingofchaotic timeseries.NeuralProcess. Lett. 2011,33, 215–233. [CrossRef] 13. Fonseca-Delgado,R.;Gomez-Gil,P.Selectingandcombiningmodelswithself-organizingmapsforlong-term forecasting of chaotic time series. In Proceedings of the 2014 International JointConference onNeural Networks,Beijing,China,6–11 July2014;pp.2616–2623. 14. Simmons, L. Time-series decomposition using the sinusoidalmodel. Int. J. Forecast. 1990, 6, 485–495. [CrossRef] 15. Abdoos,A.;Hemmati,M.;Abdoos,A.A. Short term load forecastingusingahybrid intelligentmethod. Knowl.-BasedSyst. 2015,76, 139–147. [CrossRef] 16. DeGooijer, J.G.; Hyndman, R.J. 25 years of time series forecasting. Int. J. Forecast. 2006, 22, 443–473. [CrossRef] 17. Stock, J.H.;Watson,M.W.Combinationforecastsofoutputgrowthinaseven-countrydataset. J.Forecast. 2010,23, 405–430. [CrossRef] 18. Khairalla,M.;Xu,N.;Al-Jallad,N.Modelingandoptimizationofeffectivehybridizationmodelfortime-series data forecasting. J.Eng. 2018. [CrossRef] 19. Hsiao,C.;Wan,S.K. Is thereanoptimal forecast combination? J.Econom. 2014,178, 294–309. [CrossRef] 20. Barrow,D.;Crone,S.Dynamicmodelselectionandcombination in forecasting:Anempiricalevaluationof baggingandboosting.Med. Phys. 2011,25, 435–443. 21. Barrow,D.K.;Crone,S.F.;Kourentzes,N.Anevaluationofneuralnetworkensemblesandmodelselection for time series prediction. In Proceedings of the International Joint Conference onNeural Networks, Barcelona,Spain,18–23 July2010;pp.1–8. 22. Chiroma,H.; Abubakar,A.I.; Herawan, T. Soft computing approach for predictingOPECcountries’ oil consumption. Int. J.OilGasCoalTechnol. 2017,15, 298–316. [CrossRef] 23. Weron,R.Electricitypriceforecasting:Areviewofthestate-of-the-artwithalookintothefuture. Int. J.Forecast. 2014,30,1030–1081. [CrossRef] 24. Cincotti, S.; Gallo, G.; Ponta, L.; Raberto, M. Modeling and forecasting of electricity spot-prices: Computational intelligencevs. classicaleconometrics.AICommun. 2014,27, 301–314. 25. Amjady,N.;Keynia,F.Dayaheadprice forecastingofelectricitymarketsbyamixeddatamodelandhybrid forecastmethod. Int. J.Electr. PowerEnergySyst. 2008,30, 533–546. [CrossRef] 26. Azadeh,A.;Ghaderi,S.;Sohrabkhani,S.Forecastingelectricalconsumptionbyintegrationofneuralnetwork, timeseriesandANOVA.Appl.Math. Comput. 2007,186, 1753–1761. [CrossRef] 27. Bianco, V.; Manca, O.; Nardini, S.; Minea, A.A. Analysis and forecasting of nonresidential electricity consumption inRomania.Appl. Energy2010,87, 3584–3590. [CrossRef] 285
back to the  book Short-Term Load Forecasting by Artificial Intelligent Technologies"
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
Web-Books
Library
Privacy
Imprint
Austria-Forum
Austria-Forum
Web-Books
Short-Term Load Forecasting by Artificial Intelligent Technologies