TherML: occupancy prediction for thermostat control
TherML: occupancy prediction for thermostat control
复制标题
TherML：恒温器控制的占用预测
DOI：
10.1145/2493432.2493441
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
A. Dey
中科院分区：
文献类型：
--
作者：
Christian Koehler;Brian D. Ziebart;Jennifer Mankoff;A. Dey
降低温度调节系统的大量能耗对研究人员和从业人员来说都是一个挑战。在本文中，我们将探讨和比较两种常见的解决方案：鼓励减少能源使用的手动系统和智能自动控制系统。我们部署了一个生态反馈系统，能够远程控制一个人的恒温器，供十名参与者使用三个月。参与者赞赏远程控制恒温器的能力，并以78.8%的准确率控制他们的加热系统，比没有这个系统提高了6.3%。然而，尽管有反馈和远程控制，他们仍然浪费了大量的能源加热时，离家一天。利用我们部署的数据，我们开发了TherML，这是一种占用预测算法，它使用用户智能手机的GPS数据自动控制家庭的室内温度，准确率为92.1%。我们将TherML与其他最先进的技术进行了比较，并表明我们的方法具有更高的准确性，可以优化能源使用和用户舒适度。最后，我们提出了一个混合主动系统的建议，该系统利用了手动和自动方法的各个方面，可以更好地将加热控制与用户的例程和偏好相匹配。
Reducing the large energy consumption of temperature regulation systems is a challenge for researchers and practitioners alike. In this paper, we explore and compare two common types of solutions: A manual systems that encourages reduced energy use, and an intelligent automatic control system. We deployed an eco-feedback system with the ability to remotely control one's thermostat to ten participants for three months. Participants appreciated the ability to remotely control the thermostat, and controlled their heating system with 78.8% accuracy, a 6.3% improvement over not having this system. However, despite having feedback and remote control, they still wasted a lot of energy heating when away from home for the day. Using data from our deployment, we developed TherML, an occupancy prediction algorithm that uses GPS data from a user's smartphone to automatically control the indoor temperature of a home with 92.1% accuracy. We compare TherML to other state-of-the-art techniques, and show that the higher accuracy of our approach optimizes both energy usage and user comfort. We end with recommendations for a mixed initiative system that leverages aspects of both the manual and automated approaches that can better match heating control to users' routines and preferences.