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Energies2018,11, 3442 Figure3. India’sElectricityConsumptionbytheendof12thPlan(31March2017). Thecategorywiseelectricityconsumptionof Indiacomparedtootherdevelopedanddeveloping countries are analysedinFigures 4 and5, alongwith the InternationalEnergyAgency’s report for theyear2015. India falls shortofChinaandtheUSintermsofGWhinalmostall thesectorsexcept theagriculture sector.Whencompared to theEuropeanUnion, Indiaconsumeselectricitymore in theagriculture sectorand theothers sectorwhich isquite evident, since India is a tropical country and is agriculturebased. India consumes173,185GWhof electricitywhich ishigher thanChina’s 103,983GWhwhichcomesnext. In thecommercial sectorUSis themajorelectricityconsumer, it tops the listwith1,359,480GWh.USconsumes1,401,616GWhintheresidential sectorwhich isalmost the consumptionof theChineserepublic’s756,521GWhandEuropeanUnion’s795,406GWhcombined, inspiteofworld’shighlypopulousnationssuchasChinaandIndia. In the transport categoryChina’s 179,638GWhelectricityconsumptionstandsoutwayaheadof theRussianfederation’s82,120GWh, which is thesecondlargestconsumer.China isalso the topconsumer in the industrial sector in terms ofelectricitywhich is32,121,168GWhwhich ismore than26%of thewholeworld. EnergyStatisticsbringsoutenergy indicatorsmeant for thepracticeofpolicy framersand for wide-ranging coverage. Indicators participate in a critical job by transforming the data to useful information for theplanmakersandalsoaid in theprocessofmakingdecisions. Listof indicators identificationdependsuponvariousfactorssuchas lucidity, technicalvalidity, strength,sensitivityand thedegree towhichtheyaregelledtoeachother.Nosingle factorcandetermineeverythingsinceeach indicatorneedsdifferentsetofdata.GDPisthecountry’sbroadestquantitativegaugeoftotaleconomic activity. InspecificGDPtellsus thefinancialvalueofall thegoodsandservicesmanufacturedwithin thecountry’sbordersoveratimespan[34]. Thedatainthestudyhasbeengatheredfromtherespective ministriesof theGovernmentof India (GoI).Energyintensity’svaluehasdippedover the latest ten yearswhichmightbeascribedto thequicker increaseofGDPthantheenergyneed. 104
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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