Seite - 317 - in Short-Term Load Forecasting by Artificial Intelligent Technologies
Bild der Seite - 317 -
Text der Seite - 317 -
Energies2018,11, 1561
48. Torres, M.E.; Colominas, M.A.; Schlotthauer, G.; Flandrin, P. A complete ensemble empirical mode
decompositionwithadaptivenoise. InProceedingsof the2011 IEEEInternationalConferenceonAcoustics,
SpeechandSignalProcessing,Prague,CzechRepublic,22–27May2011;Volume7,pp.4144–4147. [CrossRef]
49. Elman, J.L.Findingstructure in time.Cogn. Sci. 1990,14, 179–211. [CrossRef]
50. Quan,H.;Srinivasan,D.;Khosravi,A.Uncertaintyhandlingusingneuralnetwork-basedpredictionintervals
forelectrical loadforecasting.Energy2014,73, 916–925. [CrossRef]
51. CoelloCoello,C.A.Evolutionarymulti-objectiveoptimization: Somecurrent research trendsandtopics that
remaintobeexplored.Front. Comput. Sci. China2009,3, 18–30. [CrossRef]
52. Branke, J.;KauĂźler,T.; Schmeck,H.Guidance inevolutionarymulti-objectiveoptimization.Adv. Eng. Softw.
2001,32, 499–507. [CrossRef]
53. Deb, K. Advances in EvolutionaryMulti-objectiveOptimization. In Search Based Software Engineering;
Springer: Berlin/Heidelberg,Germany,2012;pp.1–26. ISBN978-3-642-33119-0.
54. Mirjalili, S.; Gandomi,A.H.; Mirjalili, S.Z.; Saremi, S.; Faris,H.; Mirjalili, S.M. Salp SwarmAlgorithm:
Abio-inspiredoptimizer forengineeringdesignproblems.Adv. Eng. Softw. 2017,114, 163–191. [CrossRef]
55. Wang, J.;Niu,T.;Lu,H.;Guo,Z.;Yang,W.;Du,P.Ananalysis-forecast systemforuncertaintymodelingof
windspeed:Acasestudyof large-scalewindfarms.Appl. Energy2018,211, 492–512. [CrossRef]
56. Eckmann, J.-P.;Kamphorst, S.O.;Ruelle,D.RecurrencePlotsofDynamicalSystems.Europhys. Lett. 1987,
4, 973–977. [CrossRef]
57. Marwan,N.;Wessel,N.;Meyerfeldt,U.; Schirdewan,A.; Kurths, J. Recurrence-plot-basedmeasures of
complexityandtheirapplicationtoheart-rate-variabilitydata.Phys. Rev. EStat. Phys. PlasmasFluidsRelat.
Interdiscip. Top. 2002,66. [CrossRef] [PubMed]
58. Shu,F.;Luonan,C.Short-termloadforecastingbasedonanadaptivehybridmethod. IEEETrans. PowerSyst.
2006,21, 392–401.
©2018bytheauthors. LicenseeMDPI,Basel,Switzerland. Thisarticle isanopenaccess
articledistributedunder the termsandconditionsof theCreativeCommonsAttribution
(CCBY) license (http://creativecommons.org/licenses/by/4.0/).
317
Short-Term Load Forecasting by Artificial Intelligent Technologies
- Titel
- Short-Term Load Forecasting by Artificial Intelligent Technologies
- Autoren
- Wei-Chiang Hong
- Ming-Wei Li
- Guo-Feng Fan
- Herausgeber
- MDPI
- Ort
- Basel
- Datum
- 2019
- Sprache
- englisch
- Lizenz
- CC BY 4.0
- ISBN
- 978-3-03897-583-0
- Abmessungen
- 17.0 x 24.4 cm
- Seiten
- 448
- Schlagwörter
- Scheduling Problems in Logistics, Transport, Timetabling, Sports, Healthcare, Engineering, Energy Management
- Kategorie
- Informatik