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Sensors (Basel). 2018 May 17;18(5). pii: E1606. doi: 10.3390/s18051606.

Improving the Energy Saving Process with High-Resolution Data: A Case Study in a University Building.

Author information

1
Department of Transdisciplinary Studies, Seoul National University, Seoul 08826, Korea. hanjy@snu.ac.kr.
2
Department of Transdisciplinary Studies, Seoul National University, Seoul 08826, Korea. ej-lee@snu.ac.kr.
3
Department of Transdisciplinary Studies, Seoul National University, Seoul 08826, Korea. webofthink@snu.ac.kr.
4
Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea. yoonjin@kaist.ac.kr.
5
Encored Technologies Inc., Seoul 06109, Korea. hslee@encoredtech.com.
6
Department of Transdisciplinary Studies, Seoul National University, Seoul 08826, Korea. wrhee@snu.ac.kr.

Abstract

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.

KEYWORDS:

behavior detection; data resolution; data-driven; energy saving; intervention design

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