Generalizable AI Model for Indoor Temperature Forecasting Across Sub-Saharan Africa
It addresses thermal comfort management in resource-constrained environments like schools and homes in Sub-Saharan Africa, but is incremental as it builds on prior work.
This study tackled indoor temperature forecasting in Sub-Saharan Africa by extending an existing framework to achieve cross-country performance with mean absolute errors of 1.45°C for Nigerian schools and 0.65°C for Gambian homes.
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°C for Nigerian schools and 0.65°C for Gambian homes. These findings highlight AI's potential for thermal comfort management in resource-constrained environments.