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Energies2018,11, 3442
Figure3. IndiaāsElectricityConsumptionbytheendof12thPlan(31March2017).
Thecategorywiseelectricityconsumptionof Indiacomparedtootherdevelopedanddeveloping
countries are analysedinFigures 4 and5, alongwith the InternationalEnergyAgencyās 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 thanChinaās
103,983GWhwhichcomesnext. In thecommercial sectorUSis themajorelectricityconsumer, it tops
the listwith1,359,480GWh.USconsumes1,401,616GWhintheresidential sectorwhich isalmost the
consumptionof theChineserepublicās756,521GWhandEuropeanUnionās795,406GWhcombined,
inspiteofworldāshighlypopulousnationssuchasChinaandIndia. In the transport categoryChinaās
179,638GWhelectricityconsumptionstandsoutwayaheadof theRussianfederationās82,120GWh,
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
identiļ¬cationdependsuponvariousfactorssuchas lucidity, technicalvalidity, strength,sensitivityand
thedegree towhichtheyaregelledtoeachother.Nosingle factorcandetermineeverythingsinceeach
indicatorneedsdifferentsetofdata.GDPisthecountryāsbroadestquantitativegaugeoftotaleconomic
activity. Inspeciļ¬cGDPtellsus theļ¬nancialvalueofall thegoodsandservicesmanufacturedwithin
thecountryāsbordersoveratimespan[34]. Thedatainthestudyhasbeengatheredfromtherespective
ministriesof theGovernmentof India (GoI).Energyintensityāsvaluehasdippedover the latest ten
yearswhichmightbeascribedto thequicker increaseofGDPthantheenergyneed.
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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