Electricity demand mapping from open-source data for low- and middle-income countries
File(s)
Published version
Author(s)
Millot, Ariane
Kerekeš, Anđelka
Korkovelos, Alexandros
Stringer, Martin
Hawkes, Adam
Type
Journal Article
Abstract
Spatially resolved energy systems modelling is increasingly used to provide more accurate insights into electrification planning and infrastructure development, yet spatially resolved electricity demand data is often unavailable in low- and middle-income countries (LMICs). This study presents a novel, open-source methodology to build a high-resolution electricity demand map covering the buildings and industry sectors, and applies it to Zambia as a case study. Our approach integrates publicly available GIS data, national surveys (DHS), and official statistics. For the buildings sector, machine learning is used to map residential demand and a top-down model for services; industrial demand is assessed with a separate bottom-up process model. Our bottom-up estimates are validated against national statistics, capturing 70 % of residential and 80 % of industrial demand before final scaling. The results reveal a stark geographic concentration of consumption, with the Lusaka and Copperbelt provinces alone accounting for nearly 60 % of building demand and the vast majority of industrial demand. This granular dataset can underpin the development of spatially explicit energy system models, facilitating informed decisions on grid infrastructure expansion, optimising electrification for off-grid areas, and supporting more equitable energy access in line with Sustainable Development Goals. The methodology is designed for replicability in other countries, offering a valuable tool for researchers and policymakers across other LMICs.
Date Issued
2026-06-01
Date Acceptance
2026-01-13
Citation
Renewable and Sustainable Energy Transition, 2026, 9
ISSN
2667-095X
Publisher
Elsevier BV
Journal / Book Title
Renewable and Sustainable Energy Transition
Volume
9
Copyright Statement
© 2026 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Publication Status
Published
Article Number
100138
Date Publish Online
2026-01-14