Building Matters: Spatial Variability in Machine Learning Based Thermal Comfort Prediction in Winters

TL;DR

This study investigates how spatial variability affects machine learning models for thermal comfort prediction in naturally ventilated school buildings, highlighting challenges in model accuracy and generalization across different indoor environments.

Contribution

The paper provides empirical evidence of spatial variability's impact on ML-based thermal comfort models in naturally ventilated buildings and compares performance between children and adults.

Findings

Prediction accuracy varies by up to 71% across different spatial contexts.

Feature importance analysis reveals environmental factors significantly influence model performance.

Models show limited generalization across different naturally ventilated indoor spaces.

Abstract

Thermal comfort in indoor environments has an enormous impact on the health, well-being, and performance of occupants. Given the focus on energy efficiency and Internet-of-Things enabled smart buildings, machine learning (ML) is being increasingly used for data-driven thermal comfort (TC) prediction. Generally, ML-based solutions are proposed for air-conditioned or HVAC ventilated buildings and the models are primarily designed for adults. On the other hand, naturally ventilated (NV) buildings are the norm in most countries. They are also ideal for energy conservation and long-term sustainability goals. However, the indoor environment of NV buildings lacks thermal regulation and varies significantly across spatial contexts. These factors make TC prediction extremely challenging. Thus, determining the impact of the building environment on the performance of TC models is important. Further, the generalization capability of TC prediction models across different NV indoor spaces needs to be studied. This work addresses these problems. Data is gathered through month-long field experiments conducted in 5 naturally ventilated school buildings, involving 512 primary school students. The impact of spatial variability on student comfort is demonstrated through variation in prediction accuracy (by as much as 71%). The influence of building environment on TC prediction is also demonstrated through variation in feature importance. Further, a comparative analysis of spatial variability in model performance is done for children (our dataset) and adults (ASHRAE-II database). Finally, the generalization capability of thermal comfort models in NV classrooms is assessed and major challenges are highlighted.