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

## TL;DR

This paper introduces a lightweight AI model capable of accurately predicting indoor temperatures across different Sub-Saharan African countries using minimal data, aiding thermal comfort management in resource-limited settings.

## Contribution

It extends the Temp-AI-Estimator framework to a cross-country context, demonstrating robust performance with minimal inputs in diverse environments.

## Key findings

- Mean absolute error of 1.45°C in Nigerian schools
- Mean absolute error of 0.65°C in Gambian homes
- Model generalizes well across different countries and building types

## 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.