Do occupancy-responsive learning thermostats save energy? A field study in university residence halls
Do occupancy-responsive learning thermostats save energy? A field study in university residence halls
复制标题
占用响应型学习恒温器可以节省能源吗？
DOI：
10.1016/j.enbuild.2016.05.024
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发表时间：
2016
影响因子：
6.7
通讯作者：
M. Modera
中科院分区：
文献类型：
--
作者：
Marco Pritoni;J. Woolley;M. Modera
居住者的存在和行为可以而且应该影响建筑物的能源使用。如果占用率被测量、预测或以其他方式推断，建筑控制可以自动调整系统运行参数，以使用更少的能源，而不牺牲用户服务。然而，之前的现场评估和模拟研究似乎高估了与这种类型的智能控制相关的能源节约。在这篇文章中，我们展示了对安装在三座高层大学宿舍每间卧室的占用响应式学习恒温器进行仔细控制的现场评估的结果。虽然在改造之前开发的标准实践能源模型估计可以节省10-25%的冷却和20-50%的采暖，但测量表明，控制方案仅减少了0-9%的冷却能耗，减少了5-8%的采暖在学术期间的正常运行。然而，在非学术期间，当宿舍人口稀少时，该方案减少了20-30%的冷却能耗。我们分析了这些观察结果与占用模式、室温记录、环境条件和设备运行时间的关系。这些发现为如何改进现场评估和完善模型假设提供了新的见解，以更好地预测占用响应式恒温器控制的影响。值得注意的是，虽然分析师经常使用部分建筑占用趋势来模拟建筑能源性能，但本研究强调了准确计算整个建筑中空置事件的时间和空间变化的重要性。
Occupant presence and behavior can and should influence energy use in buildings. If occupancy is measured, predicted, or otherwise inferred, building controls can automatically adjust system operating parameters to use less energy without sacrificing user services. However, previous field evaluations and simulation studies appear to have overestimated the energy savings associated with this type of smart control. In this article we present results from a carefully controlled field evaluation of occupancy-responsive learning thermostats installed in every bedroom of three high-rise university residence halls. While a standard practice energy model developed prior to the retrofit estimated 10–25% savings for cooling and 20–50% savings for heating, measurements reveal that the control scheme only reduced energy consumption by 0–9% for cooling, and by 5–8% for heating for normal operation during academic periods. However, for non-academic periods when the residence halls were sparsely populated, the scheme reduced cooling energy consumption by 20–30%. We analyzed these observations in relation to occupancy patterns, room temperature records, ambient conditions, and equipment run time. The findings provide novel insight about how to improve field evaluations and refine model assumptions to better predict the impact of occupancy-responsive thermostat controls. Notably, while analysts often use fractional building occupancy trends to simulate building energy performance, this study highlights the importance of accounting accurately for both the temporal and spatial variation of vacancy events throughout a building.