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Short-Term Load Forecasting by Artificial Intelligent Technologies
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Energies2018,11, 1605 2026. However, the average annual rateswill decrease in all. For the period between 1965 and 2016, the rate of increaseswas 2.2% forBPNN,1.3% forLR, 1.8% for SVR, 1.5% for bagging1.6%, forAR2.0%forSMLE-basedSVR,2.0%forSMLE-basedBPNN,and2.1%forSMLE-basedBPNN-SVR. Additionally, for the forecastedperiodbetween2017and2026 therateswereexpected tobe0.74%, 1.39%,1.38%,1.42%,1.39%0.13%,0.38%,and0.44, respectively.Ontheotherhand, theaverageannual rateof totaloildemanddecreasedfrom1.8%between1965and2016 to0.91%between2017and2026. Lastly, thesummarizedresults inTable6demonstrate that theannualgrowthratesof1-ahead OCweremoresignificant thanthe totalaverageOCin10-aheadyears. Figure8, showstheapparent rise in the1-aheadinbothsingleandclassicensemblemodels,andfor theSMLEmodels therewas asuddendropfrom1- to2-aheadyears, alsonote thestability in thegrowthfrom2-aheadto10-ahead, withclosevalues inallmodels, except forSMLE-basedBPNNwhere therewasafewdecreasing in the 9-, 10-ahead, sequentially. Thedecrease in the rate of oil demandmaybe interpretedas there beingotheralternativeenergies thataffectoildemand, thiswillbeachievedinthecomingdecades, ascomparedwithallotherenergytypeconsumption.Ratesofchangesandreserves in theOCofall themodels indicate that theSMLEschemewas thebest todetermine theactualdemandofenergy globally,which facilitates theplanningprocess, associatedwith the issueOCprediction. Basedon thesestudyfindings,wesuggestedsomerecommendations. Figure7. Illustrated10-aheadconsumptionpredictionerrorsof (a) singlemodels (b) classicmodels (c)SMLEensemblemodels. Table6.Summaryof forecastedvaluesandCGRforOCusingallmodels from2017to2026. Years Models BPNN LR SVR Bagging RF 1stSMLE 2ndSMLE 3rdSMLE 2017 4279.16 4531.93 4504.13 4559.73 4531.93 4554.73 4395.00 4459.94 2018 4316.27 4595.00 4567.34 4646.02 4595.00 4737.14 4409.57 4523.12 2019 4349.24 4677.23 4650.48 4748.19 4677.23 4764.54 4426.70 4511.95 2020 4401.42 4771.37 4746.31 4819.88 4771.37 4835.67 4454.33 4530.88 2021 4448.96 4857.41 4821.93 4896.70 4857.41 4967.25 4482.28 4592.90 281
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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
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Short-Term Load Forecasting by Artificial Intelligent Technologies