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dc.contributor.advisorIgnacio Perez-Arriaga.en_US
dc.contributor.authorOladeji, Olamide.en_US
dc.contributor.otherMassachusetts Institute of Technology. Institute for Data, Systems, and Society.en_US
dc.contributor.otherTechnology and Policy Program.en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2019-11-12T18:13:25Z
dc.date.available2019-11-12T18:13:25Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.date.issued2018en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/122917
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
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, 2018en_US
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 97-103).en_US
dc.description.abstractIn many developing countries, access to electricity remains a significant challenge. Electrification planners in these countries often have to make important decisions on the mode of electrification and the planning of electrical networks for those without access, while under resource constraints. To facilitate the achievement of universal energy access, the Reference Electrification Model (REM), a computational model capable of providing techno-economic analysis and data-driven decision support for these planning efforts, has been developed. Primary among REM's capabilities is the recommendation of the least-cost mode of electrification - i.e by electric grid extension or off-grid systems - for non-electrified consumers in a region under analysis, while considering technical, economic and environmental constraints.en_US
dc.description.abstractThis is achieved by the identification of consumer clusters (either as clusters of off-grid microgrids, stand-alone systems or grid-extension projects) using underlying clustering methods in the model. This thesis focuses on the development and implementation of partitioning algorithms to achieve this purpose. Building on previously implemented efforts on the clustering and recommendation capabilities of REM, this work presents the development, analysis and performance evaluation of alternative approaches to the consumer clustering process, in comparison with REM's previously incorporated clustering methodology. Results show that the alternative methodology proposed can compare favorably with the hitherto implemented method in REM. Consequently, the integration of the pro- posed network partitioning procedures within REM, as well as some potential future research directions, is discussed.en_US
dc.description.abstractFinally, this thesis concludes with a discourse on the social and regulatory aspects of energy access and electricity planning in developing countries, providing some perspectives on the development policies and business models that complement the technological contributions of this work.en_US
dc.description.statementofresponsibilityby Olamide Oladeji.en_US
dc.format.extent103 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.titleNetwork partitioning algorithms for electricity consumer clusteringen_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 Societyen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Engineering Systems Division
dc.contributor.departmentTechnology and Policy Program
dc.identifier.oclc1126790961en_US
dc.description.collectionS.M.inTechnologyandPolicy Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, Technology and Policy Programen_US
dc.description.collectionS.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienceen_US
dspace.imported2019-11-12T18:13:24Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentTPPen_US
mit.thesis.departmentESDen_US
mit.thesis.departmentIDSSen_US
mit.thesis.departmentEECSen_US


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