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

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