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Short-Term Load Forecasting by Artificial Intelligent Technologies
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Energies2018,11, 1561 References 1. Fan,S.;Hyndman,R.J. Short-termloadforecastingbasedonasemi-parametricadditivemodel. IEEETrans. PowerSyst. 2012,27, 134–141. [CrossRef] 2. Du,P.;Wang, J.;Yang,W.;Niu,T.Multi-stepaheadforecasting inelectricalpowersystemusingahybrid forecastingsystem.Renew. Energy2018,122, 533–550. [CrossRef] 3. Shrivastava,N.A.;Khosravi,A.;Panigrahi,B.K.PredictionIntervalEstimationofElectricityPricesusing PSOtunedSupportVectorMachines. IEEETrans. Ind. Inform. 2015,11. [CrossRef] 4. Hagan,M.T.;Behr,S.M.TheTimeSeriesApproachtoShortTermLoadForecasting. IEEETrans. PowerSyst. 1987,2, 785–791. [CrossRef] 5. Papalexopoulos,A.D.;Hesterberg,T.C.Aregression-basedapproachtoshort-termsystemloadforecasting. IEEETrans. PowerSyst. 1990,5, 1535–1547. [CrossRef] 6. Christiaanse,W.R.Short-TermLoadForecastingUsingGeneralExponentialSmoothing. IEEETrans. Power Appar. Syst. 1971,PAS-90, 900–911. [CrossRef] 7. Al-Hamadi,H.M.;Soliman,S.A.Short-termelectric loadforecastingbasedonKalmanfilteringalgorithm withmovingwindowweatherandloadmodel.Electr. PowerSyst. Res. 2004,68, 47–59. [CrossRef] 8. Metaxiotis, K.; Kagiannas,A.; Askounis,D.; Psarras, J.Artificial intelligence in short termelectric load forecasting: A state-of-the-art survey for the researcher. Energy Convers. Manag. 2003, 44, 1525–1534. [CrossRef] 9. Yoo, H.; Pimmel, R.L. Short term load forecasting using a self-supervised adaptive neural network. IEEETrans. PowerSyst. 1999,14, 779–784. [CrossRef] 10. Ho,K.L.;Hsu,Y.Y.;Chen,C.F.; Lee,T.E.; Liang,C.C.; Lai,T.S.;Chen,K.K.Short termload forecastingof taiwanpowersystemusingaknowledge-basedexpert system. IEEETrans. PowerSyst. 1990,5, 1214–1221. [CrossRef] 11. Mohandes,M.Supportvectormachines forshort-termelectrical loadforecasting. Int. J.EnergyRes. 2002, 26, 335–345. [CrossRef] 12. Hong,W.C.Electric load forecastingbyseasonal recurrentSVR(supportvector regression)withchaotic artificialbeecolonyalgorithm.Energy2011,36, 5568–5578. [CrossRef] 13. Liu,Z.;Li,W.;Sun,W.Anovelmethodofshort-termloadforecastingbasedonmultiwavelet transformand multipleneuralnetworks.NeuralComput.Appl. 2013,22, 271–277. [CrossRef] 14. Fan,G.F.; Peng, L.L.; Hong,W.C.; Sun, F. Electric load forecasting by the SVRmodelwith differential empiricalmodedecompositionandautoregression.Neurocomputing2016,173, 958–970. [CrossRef] 15. AlRashidi,M.R.;EL-Naggar,K.M.Longtermelectric loadforecastingbasedonparticle swarmoptimization. Appl. Energy2010,87, 320–326. [CrossRef] 16. Wang, J.;Li,L.;Niu,D.;Tan,Z.Anannual loadforecastingmodelbasedonsupportvector regressionwith differentialevolutionalgorithm.Appl. Energy2012,94, 65–70. [CrossRef] 17. Ghayekhloo, M.; Menhaj, M.B.; Ghofrani, M. A hybrid short-term load forecasting with a new data preprocessingframework.Electr. PowerSyst. Res. 2015,119, 138–148. [CrossRef] 18. Zhang,X.;Wang, J.Anoveldecomposition-ensemblemodel for forecastingshort-termload-timeserieswith multipleseasonalpatterns.Appl. SoftComput. J.2018,65, 478–494. [CrossRef] 19. Tian,C.;Hao,Y.ANovelNonlinearCombinedForecastingSystemforShort-TermLoadForecasting.Energies 2018,11, 714. 20. Khotanzad,A.;Hwang,R.C.;Abaye,A.;Maratukulam,D.AnAdaptiveModularArtificialNeuralNetwork Hourly Load Forecaster and its Implementation at Electric Utilities. IEEE Trans. Power Syst. 1995, 10, 1716–1722. [CrossRef] 21. Bishop,C.M.Neuralnetworks forpatternrecognition. J.Am. Stat.Assoc. 1995,92, 482. [CrossRef] 22. Hwang, J.T.G.; Ding,A.A.Prediction Intervals forArtificialNeuralNetworks. J.Am. Stat. Assoc. 1997, 92, 748–757. [CrossRef] 23. Heskes,T.Practical confidenceandprediction intervals.Adv.Neural Inf. Process. Syst. 1997,9, 176–182. 24. Nix, D.A.; Weigend, A.S. Estimating the mean and variance of the target probability distribution. InProceedingsof the1994 IEEE InternationalConferenceonNeuralNetworks (ICNN’94),Orlando, FL, USA,28 June–2July1994;Volume1,pp.55–60. 315
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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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