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Short-Term Load Forecasting by Artificial Intelligent Technologies
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Energies2018,11, 3442 Figure1.CategorywiseTECgrowth inIndia (1947–2018). Thegraphical representationof theDomestic,Commercial, Industrial,AgriculturalandTraction sectorsfortheperiod2000–2018hasbeenshowninFigure2. Thevaluefortheyear2018isanestimated valuecomparedtoall theothervalues. The industrial sectorhasseenasteep increasesince thestartof thecenturywhichusuallywillbe thecase foranycountry. Andall theother sectorshaveshowna gradual increase. Theagricultureandthedomesticcategorieshaveshownsomefluctuationswhereas theothersectorshaveshownasteadyincrease. Figure2.SectorwiseEnergyConsumptionsince theCentury(2000–2018). By the end of India’s 12th plan the split among all the sectors is shown above in Figure 3. Thedomesticandtheagriculturewhichweregoinghandtohand, fewdecadesearlierwere found to showavisible contrastofover6%between them. The industrial sector’s consumptionover the yearshasdecreasedconsiderablyeventhoughithappenedtobe thevital consumerofall thesectors. Andthesametrendisexpectedtocontinueover theyearswhichmight transformIndia frombeingan Agriculturebasedeconomy. 103
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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
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Short-Term Load Forecasting by Artificial Intelligent Technologies