arXiv:2508.20260v1 Announce Type: new Abstract: 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.
Original: https://arxiv.org/abs/2508.20260