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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.
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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