HARNESSING MACHINE LEARNING TO IMPROVE ENERGY ACCESS PLANS CASE STUDY

Since October 2022, SEforALL, IBM, and the German Aerospace Center (DLR) have collaborated to create an open-source planning tool using machine learning and satellite imagery. The tool aims to address data gaps regarding building footprints—locations and sizes of residential and commercial buildings—which serve as proxies for human activity. By providing more accurate foundational data, the tool helps policymakers and the private sector estimate future energy demand and determine the most suitable energy technologies for specific communities, thereby reducing investment risk and increasing the efficiency of energy infrastructure interventions. The project is currently developing a 'Minimum-Viable-Product' (MVP) for testing in pilot geographies before wider deployment.

• The partnership is developing an open-source tool that utilizes satellite imagery and machine-learning models to improve the accuracy of building footprint data, which is often difficult to source in developing regions.
• Building footprints—the size and location of commercial and residential buildings—act as proxies for human activity and are critical for estimating future energy demand and planning infrastructure.
• Accurate geospatial data allows decision-makers to identify the most appropriate energy technologies for specific communities based on factors such as distance from power grids, affordability, and suitability for renewables like biomass, small-scale hydro, or solar.
• The project aims to de-risk investments and improve the efficiency of energy interventions by providing precise data on the scale and location of projects.
• The project is currently in the phase of developing a 'Minimum-Viable-Product' (MVP) to be tested in pilot geographies before being expanded to other regions with inadequate planning data.

APA

Sustainable Energy for All (2023). HARNESSING MACHINE LEARNING TO IMPROVE ENERGY ACCESS PLANS CASE STUDY. https://www.seforall.org/system/files/2023-06/rm-case-study-machine-learning-005.pdf

Chicago

Sustainable Energy for All. HARNESSING MACHINE LEARNING TO IMPROVE ENERGY ACCESS PLANS CASE STUDY. 2023. https://www.seforall.org/system/files/2023-06/rm-case-study-machine-learning-005.pdf.

Wikipedia

{{cite report |author=Sustainable Energy for All |title=HARNESSING MACHINE LEARNING TO IMPROVE ENERGY ACCESS PLANS CASE STUDY |date=April 2023 |url=https://www.seforall.org/system/files/2023-06/rm-case-study-machine-learning-005.pdf |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{sustainableenergyforall2023harnessing, author = {{Sustainable Energy for All}}, title = {{HARNESSING MACHINE LEARNING TO IMPROVE ENERGY ACCESS PLANS CASE STUDY}}, institution = {Sustainable Energy for All}, year = {2023}, month = apr, url = {https://www.seforall.org/system/files/2023-06/rm-case-study-machine-learning-005.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }