Web-Books
in the Austria-Forum
Austria-Forum
Web-Books
Informatik
Short-Term Load Forecasting by Artificial Intelligent Technologies
Page - 281 -
  • User
  • Version
    • full version
    • text only version
  • Language
    • Deutsch - German
    • English

Page - 281 - in Short-Term Load Forecasting by Artificial Intelligent Technologies

Image of the Page - 281 -

Image of the Page - 281 - in Short-Term Load Forecasting by Artificial Intelligent Technologies

Text of the Page - 281 -

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
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