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Massachusetts Institute of Technology

Network partitioning algorithms for electricity consumer clustering

Abstract

dc:description.abstract

In 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.

Degree

thesis:*
Name thesis:degree_name
Master
Department dc:contributor.department
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2018

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Oladeji, Olamide.
Advisor dc:contributor.advisor
  • Ignacio Perez-Arriaga.

Subjects

dc:subject × 3

Rights

dc:rights
Statement dc:rights
  • MIT 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.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/1721.1/122917
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/122917

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Oladeji, Olamide.. Network partitioning algorithms for electricity consumer clustering. Massachusetts Institute of Technology, 2018. https://hdl.handle.net/1721.1/122917