Indoor thermal comfort immensely impacts the health and performance of occupants. Therefore, researchers and engineers have proposed numerous computational models to estimate thermal comfort (TC). Given the impetus toward energy efficiency, the current focus is on data-driven TC prediction solutions that leverage state-of-the-art machine learning (ML) algorithms. However, an indoor occupant's perception of indoor thermal comfort (TC) is subjective and multi-dimensional. Different aspects of TC are represented by various standard metrics/scales viz., thermal sensation (TSV), thermal comfort (TCV), and thermal preference (TPV). The current ML-based TC prediction solutions adopt the Single-task Learning approach, i.e., one prediction model per metric. Consequently, solutions often focus on only one TC metric. Moreover, when several metrics are considered, multiple TC models for a single indoor space lead to conflicting predictions, making real-world deployment infeasible. This work addresses these problems. With the vision toward energy conservation and real-world application, naturally ventilated primary school classrooms are considered. First, month-long field experiments are conducted in 5 schools and 14 classrooms, including 512 unique student participants. Further, "DeepComfort," a Multi-task Learning inspired deep-learning model is proposed. DeepComfort predicts multiple TC output metrics viz., TSV, TPV, and TCV, simultaneously, through a single model. It demonstrates high F1-scores, Accuracy (>90%), and generalization capability when validated on the ASHRAE-II database and the dataset created in this study. DeepComfort is also shown to outperform 6 popular metric-specific single-task machine learning algorithms. To the best of our knowledge, this work is the first application of Multi-task Learning to thermal comfort prediction in classrooms.
翻译:因此,研究人员和工程师提出了许多计算模型来估计热舒适度(TC)。鉴于能效的动力,当前的重点是数据驱动的TC预测解决方案,利用最先进的机器学习(ML)算法。然而,室内食宿者对室内热舒适度(TC)的看法是主观和多维的。三角的不同方面表现为各种标准度量/尺度、热感知(TSV)、热慰(TCFV)和热偏好(TPV)。当前以ML为基础的TC预测解决方案采用了单一任务学习方法,即每公吨一个预测模型。因此,解决方案往往只侧重于一个TC标准。此外,如果考虑数种计量,单室内舒适度(TC)的多种TC模型会导致相互矛盾的预测,使得真实世界的部署变得不可行。随着对节能和现实应用的愿景,自然通风的初级教室(TPV)的预测解决方案采用了单任务学习方法。TLLTS-L的预测方法采用单一任务学习方法。TFS-40的月长的实验方法在5所学校和14个教室中展示了一个独特的数据库。