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dc.contributor.advisorIgnacio J. Pérez-Arriaga.en_US
dc.contributor.authorLee, Stephen James, S.M. Massachusetts Institute of Technologyen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2018-09-17T15:48:14Z
dc.date.available2018-09-17T15:48:14Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/117878
dc.descriptionThesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, Technology and Policy Program, 2018.en_US
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 139-149).en_US
dc.description.abstractAbout 1.1 billion people worldwide lack access to electricity and an additional 1 billion have unreliable access. The social ramifications of this problem are noteworthy because access to electric power has the potential to transform societies. While admirable efforts are underway, there is general consensus that progress is falling far short of what is needed to reach international electricity access goals. In light of such deficiencies, it is arguable that systems-level experimentation and innovation is required if we are to achieve universal electricity access in the next one to two decades. With the advancement of technology, new opportunities are emerging that can potentially change the game. Machine learning methods and detailed technoeconomic models for planning comprise one set of technologies that hold significant promise for accelerating access. This thesis builds upon recent work towards the development of more intelligent decision support systems for electrification planning. Progress towards automated and scalable software systems for the extraction of building footprints from satellite imagery are presented. In addition, a novel model for probabilistic data fusion and other machine learning methods are compared for electrification status estimation. Inference tools such as these allow for the cost-effective provision of granular data required by techno-economic models. We also acknowledge that the technologies we detail should not be developed in a vacuum. Given that electrification is a complex endeavor involving numerous social and technical factors, careful consideration must be given to human, policy, and regulatory concerns during the planning process. We notice how uncertainty abounds in these activities and propose "adaptive electricity access planning" as a new model-assisted framework for the explicit consideration of uncertainty in large-scale planning. This work aspires to provide valuable perspective on the importance of uncertainty in planning as these endeavors continue to evolve.en_US
dc.description.statementofresponsibilityby Stephen James Lee.en_US
dc.format.extent149 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectInstitute for Data, Systems, and Society.en_US
dc.subjectTechnology and Policy Program.en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleAdaptive electricity access planningen_US
dc.typeThesisen_US
dc.description.degreeS.M. in Technology and Policyen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Data, Systems, and Society.en_US
dc.contributor.departmentTechnology and Policy Program.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.identifier.oclc1051190434en_US


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