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Energies2018,11, 3442
19. Tsekouras,G.J.;Dialynas,E.N.;Hatziargyriou,N.D.;Kavatza,S.Anon-linearmultivariableregressionmodel
formidtermenergyforecastingofpowersystems.Electr. PowerSyst. Res. 2007,77, 1560–1568. [CrossRef]
20. Persaud, J.;Kumar,U.Aneclecticapproach inenergyforecasting:AcaseofNaturalResourcesCanada’soil
andgasoutlook.EnergyPolicy2001,29, 303–313. [CrossRef]
21. Edigera,V.S.;Akarb,S.ARIMAforecastingofprimaryenergydemandbyfuel inTurkey.EnergyPolicy2007,
35, 1701–1708. [CrossRef]
22. Xie,N.-M.;Yuan,C.-Q.; Yang,Y.-J. ForecastingChina’s energydemandandself-sufficiency ratebygrey
forecastingmodelandMarkovmodel.Elect. PowerEnergySyst. 2015,66, 1–8. [CrossRef]
23. Bassam,M.;Al-Foul,A.ForecastingEnergyDemandinJordanUsingArtificialNeuralNetworks.Top.Middle
East.Afr. Econ. 2012,14, 473.
24. Kankal, M.; Akpınar, A.; Komurcu, M.I.; Ozsahin, T.S. Modeling and forecasting of Turkey’s energy
consumptionusingsocio-economicanddemographicvariables.Appl. Energy2011,88, 1927–1939. [CrossRef]
25. Hyndman,R.J.; Fan,S.DensityForecasting forLong-Term-PeakElectricityDemand. IEEETrans. PowerSyst.
2010,25, 1142–1153. [CrossRef]
26. Pławiak,P.NovelGeneticEnsemblesofClassifiersAppliedtoMyocardiumDysfunctionRecognitionBased
onECGSignals.SwarmEvol.Comput. 2018,39, 192–208. [CrossRef]
27. Pławiak,P.;Rzecki,K.ApproximationofPhenolConcentrationusingComputational IntelligenceMethods
BasedonSignals fromtheMetalOxideSensorArray. IEEESens. J.2015,15, 1770–1783.
28. Mallah,S.;Bansal,N.K.Allocationofenergyresources forpowergeneration in India: Businessasusualand
energyefficiency.EnergyPolicy2010,38, 1059–1066. [CrossRef]
29. Li,C.;Ding,Z.; Zhao,D.; Yi, J.; Zhang,G.BuildingEnergyConsumptionPrediction: AnExtremeDeep
LearningApproach.Energies2017,10, 1–20. [CrossRef]
30. Bianco,V.;Manca,O.;Nardini,S.Electricityconsumptionforecasting in Italyusing linear regressionmodels.
Energy2009,34, 1413–1421. [CrossRef]
31. Erdogdu,E.Electricitydemandanalysisusingco-integrationandARIMAmodeling:AcasestudyofTurkey.
EnergyPolicy2007,35, 1129–1146. [CrossRef]
32. Gajowniczek,K.;Nafkha,R.; Za˛bkowski, T. Electricitypeakdemandclassificationwith artificial neural
networks.Ann.Comput. Sci. Inf. Syst. 2017,11, 307–315.
33. Singh, S.; Yassine, A. Big Data Mining of Energy Time Series for Behavioral Analytics and Energy
ConsumptionForecasting.Energies2018,11, 452. [CrossRef]
34. Energy Statistics 2018; Central Statistics Office, Ministry of Statistics and Programme Implementation,
Governmentof India:NewDelhi, India,2018.
35. RobertMason,L.;RichardGunst,F.; JamesHess,L.StatisticalDesignandAnalysis ofExperiments; JohnWiley
&SonsPublication:NewYork,NY,USA,2003.
36. Hanief,M.;Wani,M.F.;Charoo,M.S.Modelingandpredictionofcuttingforcesduringthe turningofred
brass (C23000)usingANNandregressionanalysis.Eng. Sci. Technol. Int. J.2017,20, 1220–1226. [CrossRef]
37. Iniyan,S.; Suganthi,L.; Jagadeesan,T.R.;Samuel,A.A.Reliabilitybasedsocioeconomicoptimal renewable
energymodel for India.Renew. Energy2000,19, 291–297. [CrossRef]
38. Suganthi,L.;Williams,A.Renewableenergy in India—Amodellingstudyfor2020–2021.EnergyPolicy2000,
28, 1095–1109. [CrossRef]
39. Suganthi,L.; Samuel,A.A.Energymodels fordemandforecasting—Areview.Renew. Sus. EnergyRev. 2012,
16, 1223–1240. [CrossRef]
40. Venkatesan,G.;Kulasekharan,N.;Muthukumar,V.; Iniyan,S.Regressionanalysisofacurvedvanedemister
withTaguchibasedoptimization.Desalination2015,370, 33–43. [CrossRef]
41. TERI.National EnergyMap for India: TechnologyVision 2030: Summary for Policy-Makers; TheEnergyand
Resources InstituteTERI&Office of thePrincipal ScientificAdviser,Government of India: NewDelhi,
India,2015.
42. Gokarn,S.; Sajjanhar,A.;Sandhu,R.;Dubey,S.Energy2030;Brookings InstitutionIndiaCenter:NewDelhi,
India,2013.
43. TheEconomicTimes.Availableonline: https://economictimes.indiatimes.com/industry/energy/power/
indias-electricity-consumption-to-touch-4-trillion-units-by-2030/articleshow/52221341.cms(accessedon
21November2018).
117
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