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arXiv · 2508.20260

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

Zainab Akhtar·Eunice Jengo·Björn Haßler
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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.

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Zainab Akhtar, Eunice Jengo, Björn Haßler. 2025-08-27. Generalizable AI Model for Indoor Temperature Forecasting Across Sub-Saharan Africa. https://arxiv.org/abs/2508.20260

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