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Data-driven simulation of a thermal comfort-based temperature set-point control with ASHRAE RP884

机译：Ashrae RP884的热舒适温度设定点控制的数据驱动模拟

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摘要

In the course of thermal comfort theory, researchers have been investigating both static thermal comfort and adaptive thermal comfort. Compared to static thermal comfort metrics such as predicted mean vote (PMV), adaptive thermal comfort emphasizes the interactions between occupants and indoor environment by collecting both environment-related and occupant-related data in real commercial buildings. Therefore, data-driven approaches to developing adaptive thermal comfort models have been well investigated and development of ASHRAE RP884 dataset can be seen as one of the milestones. Moreover, as thermal comfort is an occupant-centric concept for operation of HVAC system, well-developed thermal comfort model can be applied into HVAC control. Nowadays, reinforcement learning-based HVAC control has drawn much more attention in that the control system can learn by itself through the interactions between occupants and environment, which also aligns the concept of adaptive thermal comfort. Therefore, this paper mainly has two goals. The first is to develop a thermal comfort model with RP 884 of three major climate zones based on k-nearest neighbor (KNN), random forest (RF) and support vector machine (SVM). The second goal is to simulate a tabular Q-learning temperature set-point control system with the statistical thermal comfort model. The results have shown that the best recall of the statistical thermal comfort model is 49.3%, which outperforms that of PMV being 43% based on 7-point thermal sensation scale. In addition, the Q-learning based temperature control can indeed reach the comfortable temperature ranges for occupants with whatever initial temperature set-point.
机译：在热舒适理论过程中，研究人员一直在研究静态热舒适度和自适应热舒适度。与静态热舒适度量相比，如预测的平均投票（PMV），自适应热舒适性通过在真正的商业建筑中收集与环境相关和乘员相关的数据来强调乘员和室内环境之间的相互作用。因此，已经很好地研究了发展自适应热舒适模型的数据驱动方法，并且可以将Ashrae RP884数据集发育为一个里程碑。此外，随着热舒适性是HVAC系统操作的以乘客为中心的概念，良好发育的热舒适模型可以应用于HVAC控制。如今，基于加强学习的HVAC控制，更多地引起了控制系统，通过乘员和环境之间的相互作用本身可以自行学习，这也可以对准自适应热舒适度的概念。因此，本文主要有两个目标。首先是基于K最近邻（knn），随机森林（RF）和支持向量机（SVM）的三大气候区RP 884开发热舒适模型。第二个目标是通过统计热舒适模型模拟表格Q学习温度设定点控制系统。结果表明，统计热舒适模型的最佳召回是49.3％，其基于7点热敏尺度的PMV的表现优于43％。此外，基于Q学习的温度控制确实可以达到距离初始温度设定点的乘员舒适的温度范围。

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