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dc.contributor.advisorRobert Stoner.en_US
dc.contributor.authorAlsup, Meia(Meia L.)en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.coverage.spatiala-ii---en_US
dc.date.accessioned2021-01-06T17:38:57Z
dc.date.available2021-01-06T17:38:57Z
dc.date.copyright2020en_US
dc.date.issued2020en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/129084
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020en_US
dc.descriptionCataloged from student-submitted PDF of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 51-53).en_US
dc.description.abstractElectricity demand at the grid level is steadily growing in India. More areas are getting interconnected to the grid; and with rising incomes, electricity is highly affected by adoption of air conditioning systems and electric vehicles. Compared with the developed world context where electricity demand is approximately flat if not decreasing year to year, demand in India is growing. In this paper, we aim to examine forecasting methods and determine an optimal method for forecasts in India. Despite limited historical data, we improve forecasts of electricity demand in India out to the year 2050. The forecasts are in five year increments across three different GDP growth scenarios (not accounting for Covid-19). In addition, a layer of natural variation is added to the forecasts for the purpose of modeling the role of various energy technologies on the grid. The methodology to generate more realistic sample loads from predicted average scenarios is a key contribution.en_US
dc.description.statementofresponsibilityby Meia Alsup.en_US
dc.format.extent53 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleForecasting electricity demand in the data-poor Indian contexten_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.identifier.oclc1227274035en_US
dc.description.collectionM.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienceen_US
dspace.imported2021-01-06T17:38:55Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentEECSen_US


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