Project TERESA: A GIS-based multifactorial framework utilizing supervised machine learning for nation-scale electrification planning

Abstract

This paper presents a large-scale electrification strategy developed within the project ”Technology for Rural Electrification in Sub-Saharan Africa” (TERESA). The approach proposed enhances the existing electrification framework GISEle by improving the population aggregation, introducing a novel survey-based methodology for energy demand estimation in spatially clustered communities, and incorporating a rule-based meta-heuristic algorithm to solve the optimization problem. Initially, iterative DBSCAN clusters population data. Electric grid and nighttime lighting open-source datasets then determine the electrification stage of communities. Subsequent steps involve load profile estimation: Multi-Tier Framework surveys gather household, educational, healthcare, and commercial activities data in selected communities. This represents a significant innovation in modeling cluster-level demand by systematically integrating survey insights into scalable energy planning. Considering the non-linearity between energy consumption, socioeconomic and resource consuming on-field campaigns, a supervised machine learning model extrapolates the energy demand of all the communities recognized by the clustering procedure. Lastly, a rule-based approach is utilized to determine each community’s means of electrification, and a genetic algorithm is employed for expanding the national grid. The approach was applied to the Zambezia region—the second most populous and least electrified province in Mozambique. In this context, the method enabled demand estimation for 1,292 communities, leveraging and transferring insights derived from 726 on-field surveys to support broader regional planning.