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

Lala, Betty; Kala, Srikant Manas; Rastogi, Anmol; Dahiya, Kunal; Yamaguchi, Hirozumi; Hagishima, Aya

doi:10.1109/smartcomp55677.2022.00078

Cited by 7 publications

(9 citation statements)

References 14 publications

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“…Surprisingly, many ML-based thermal comfort (MLTC) studies have been conducted for adults [17,18,24], yet school-going prediction studies are performed for children or primary school students [25][26][27]. Given the lower cognitive capabilities, limited opportunities and ability to adapt and express themselves, and the early physiological developmental stage of primary students, their subjective response votes are likely to suffer from data bias and data imbalance.…”

Section: Motivationmentioning

confidence: 99%

“…Given the lower cognitive capabilities, limited opportunities and ability to adapt and express themselves, and the early physiological developmental stage of primary students, their subjective response votes are likely to suffer from data bias and data imbalance. Therefore, predicting thermal comfort of primary school students is shown to be more challenging as compared to adults [25,26].…”

Section: Motivationmentioning

confidence: 99%

“…There exists a considerable body of literature on ML and AI for TC that focuses on adults in various settings of environments along with data-driven building performance predictions. However, ML-based predictive models for children or school students are only recently being proposed in research works [25][26][27].…”

Section: Machine Learning For Thermal Comfort Prediction-an Overviewmentioning

confidence: 99%

“…The study demonstrates temporal variability through variances of up to 80% in deep learning model prediction accuracy. Likewise, in [26], the impact of building environment on student comfort in NV buildings is demonstrated through a 71% variation in prediction accuracy and feature importance. The study also evaluates the generalization capability of TC models in naturally ventilated classrooms to test the feasibility of "train once deploy anywhere" models.…”

Section: Tc Prediction For Childrenmentioning

confidence: 99%

“…Although NV schools are ideal for energy conservation and long-term sustainability, due to the lack of thermal regulation through HVACs, they tend to be vulnerable to outdoor weather conditions and building designs, making TC prediction extremely challenging. Only a handful of AI/ML studies have focused on NV buildings, and the prediction performance is generally inadequate [26]. Thus, improved prediction models specifically for NV classrooms are required.…”

mentioning

confidence: 99%

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A Review of Thermal Comfort in Primary Schools and Future Challenges in Machine Learning Based Prediction for Children

Lala

Hagishima

2022

Buildings

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Children differ from adults in their physiology and cognitive ability. Thus, they are extremely vulnerable to classroom thermal comfort. However, very few reviews on the thermal comfort of primary school students are available. Further, children-focused surveys have not reviewed the state-of-the-art in thermal comfort prediction using machine learning (AI/ML). Consequently, there is a need for discussion on children-specific challenges in AI/ML-based prediction. This article bridges these research gaps. It presents a comprehensive review of thermal comfort studies in primary school classrooms since 1962. It considers both conventional (non-ML) studies and the recent AI/ML studies performed for children, classrooms, and primary students. It also underscores the importance of AI/ML prediction by analyzing adaptive opportunities for children/students in classrooms. Thereafter, a review of AI/ML-based prediction studies is presented. Through an AI/ML case-study, it demonstrates that model performance for children and adults differs markedly. Performance of classification models trained on ASHRAE-II database and a recent primary students’ dataset shows a 29% difference in thermal sensation and 86% difference in thermal preference, between adults and children. It then highlights three major children-specific AI/ML challenges, viz., “illogical votes”, “multiple comfort metrics”, and “extreme class imbalance”. Finally, it offers several technical solutions and discusses open problems.

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