Department of Transdisciplinary Studies, Seoul National University, Seoul 08826, Korea.
Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea.
Sensors (Basel). 2018 May 17;18(5):1606. doi: 10.3390/s18051606.
In this paper, we provide findings from an energy saving experiment in a university building, where an IoT platform with 1 Hz sampling sensors was deployed to collect electric power consumption data. The experiment was a reward setup with daily feedback delivered by an energy delegate for one week, and energy saving of 25.4% was achieved during the experiment. Post-experiment sustainability, defined as 10% or more of energy saving, was also accomplished for 44 days without any further intervention efforts. The saving was possible mainly because of the data-driven intervention designs with high-resolution data in terms of sampling frequency and number of sensors, and the high-resolution data turned out to be pivotal for an effective waste behavior investigation. While the quantitative result was encouraging, we also noticed many uncontrollable factors, such as exams, papers due, office allocation shuffling, graduation, and new-comers, that affected the result in the campus environment. To confirm that the quantitative result was due to behavior changes, rather than uncontrollable factors, we developed several data-driven behavior detection measures. With these measures, it was possible to analyze behavioral changes, as opposed to simply analyzing quantitative fluctuations. Overall, we conclude that the space-time resolution of data can be crucial for energy saving, and potentially for many other data-driven energy applications.
本文提供了一项大学建筑节能实验的研究结果。该实验使用具有 1Hz 采样传感器的物联网平台来收集电力消耗数据。实验采用奖励设置，由能源代表每周提供每日反馈，实验期间实现了 25.4%的节能。实验结束后，在没有任何进一步干预措施的情况下，44 天内实现了 10%或更多的节能，即具有可持续性。节能的主要原因是数据驱动的干预设计，具有高分辨率的数据（采样频率和传感器数量），而高分辨率数据对于有效调查浪费行为至关重要。虽然定量结果令人鼓舞，但我们也注意到许多不可控因素，如考试、交论文、办公室调整、毕业和新员工等，这些因素影响了校园环境中的结果。为了确认定量结果是由于行为改变而不是不可控因素造成的，我们开发了几种数据驱动的行为检测措施。有了这些措施，就可以分析行为变化，而不仅仅是简单地分析数量波动。总的来说，我们得出结论，数据的时空分辨率对于节能至关重要，对于许多其他数据驱动的能源应用也可能至关重要。
