This study presents a lightweight, domain-informed AI model for predicting indoor temperatures in naturally ventilated schools and homes in Sub-Saharan Africa. The model extends the Temp-AI-Estimator framework, trained on Tanzanian school data, and evaluated on Nigerian schools and Gambian homes. It achieves robust cross-country performance using only minimal accessible inputs, with mean absolute errors of 1.45{\deg}C for Nigerian schools and 0.65{\deg}C for Gambian homes. These findings highlight AI's potential for thermal comfort management in resource-constrained environments.
@article{arxiv.2508.20260,
title = {Generalizable AI Model for Indoor Temperature Forecasting Across Sub-Saharan Africa},
author = {Zainab Akhtar and Eunice Jengo and Björn Haßler},
journal= {arXiv preprint arXiv:2508.20260},
year = {2025}
}