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Temperature-preference learning with neural networks for occupant-centric building indoor climate controls

机译：神经网络的温度偏好学习，以居住者为中心的建筑物室内气候控制

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Heating, ventilation, and air-conditioning (HVAC) are vital components in providing a comfortable indoor climate for the occupants of buildings. In commercial buildings, HVAC setpoints are set according to average comfort temperatures. However, individual temperature preferences may be different. The purpose of this study is to explore the means of making HVAC systems respond automatically to local occupant temperature preferences. To create an occupant-centric indoor temperature environment, we propose an online-learning-based control strategy together with its design process. Four essential variables from four domains-time, indoor and outdoor climates, and occupant behavior-are extracted to construct datasets for preference models. A neural network algorithm and corresponding hyperparameters are suggested to model temperature preferences. According to time-dependent setpoints learned from dynamic contexts, a set of specified rules is used to determine setpoints for HVAC systems. For a period of five months, the resulting learning-based temperature preference control (LTPC) was applied to a cooling system of an office space under real-world conditions. The four case study rooms consisted of typical office uses: single-person and multi-person offices. The experimental results indicate that occupant preferences in the individual rooms differ from each other in both time horizon and temperature levels. The results report energy savings of between 4% and 25% as compared to static temperature setpoints at the low values of preferred temperature ranges. Meanwhile, during LPTC, the need for occupant interventions for adjusting room temperatures to fit their preferences was reduced from four to nine weekdays a month to a maximum of one weekday a month.
机译：暖气，通风和空调（HVAC）是为建筑物的居住者提供舒适的室内气候的重要组成部分。在商业建筑中，HVAC设定点是根据平均舒适温度设定的。但是，各个温度偏好可能会有所不同。这项研究的目的是探索使HVAC系统自动响应当地乘员温度偏好的方法。为了创建以居住者为中心的室内温度环境，我们提出了一种基于在线学习的控制策略及其设计过程。从四个方面（时间，室内和室外气候以及乘员行为）提取四个基本变量，以构建偏好模型的数据集。建议使用神经网络算法和相应的超参数对温度偏好进行建模。根据从动态上下文中学到的时间相关的设定点，使用一组指定规则来确定HVAC系统的设定点。在五个月的时间内，将基于学习的温度偏好控制（LTPC）应用于实际条件下的办公空间冷却系统。四个案例研究室包括典型的办公室用途：单人和多人办公室。实验结果表明，各个房间中的乘员偏好在时间范围和温度水平上都互不相同。结果表明，与较低的首选温度范围内的静态温度设定值相比，节能量在4％至25％之间。同时，在LPTC期间，为了适应他们的喜好，需要对房客进行干预以调节房间温度的需求从一个月的四个工作日减少到了九个工作日，一个月最多可以减少一个工作日。

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