Generalizable AI Model for Indoor Temperature Forecasting Across Sub-Saharan Africa

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 1.45degC for Nigerian schools and 0.65degC for Gambian homes. These findings highlight AI's potential for thermal comfort management in resource-constrained environments.