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

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,...

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Autores principales: Han, Jeongyun, Lee, Eunjung, Cho, Hyunghun, Yoon, Yoonjin, Lee, Hyoseop, Rhee, Wonjong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5982251/
https://www.ncbi.nlm.nih.gov/pubmed/29772823
http://dx.doi.org/10.3390/s18051606
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author Han, Jeongyun
Lee, Eunjung
Cho, Hyunghun
Yoon, Yoonjin
Lee, Hyoseop
Rhee, Wonjong
author_facet Han, Jeongyun
Lee, Eunjung
Cho, Hyunghun
Yoon, Yoonjin
Lee, Hyoseop
Rhee, Wonjong
author_sort Han, Jeongyun
collection PubMed
description 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.
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spelling pubmed-59822512018-06-05 Improving the Energy Saving Process with High-Resolution Data: A Case Study in a University Building Han, Jeongyun Lee, Eunjung Cho, Hyunghun Yoon, Yoonjin Lee, Hyoseop Rhee, Wonjong Sensors (Basel) Article 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. MDPI 2018-05-17 /pmc/articles/PMC5982251/ /pubmed/29772823 http://dx.doi.org/10.3390/s18051606 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Han, Jeongyun
Lee, Eunjung
Cho, Hyunghun
Yoon, Yoonjin
Lee, Hyoseop
Rhee, Wonjong
Improving the Energy Saving Process with High-Resolution Data: A Case Study in a University Building
title Improving the Energy Saving Process with High-Resolution Data: A Case Study in a University Building
title_full Improving the Energy Saving Process with High-Resolution Data: A Case Study in a University Building
title_fullStr Improving the Energy Saving Process with High-Resolution Data: A Case Study in a University Building
title_full_unstemmed Improving the Energy Saving Process with High-Resolution Data: A Case Study in a University Building
title_short Improving the Energy Saving Process with High-Resolution Data: A Case Study in a University Building
title_sort improving the energy saving process with high-resolution data: a case study in a university building
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5982251/
https://www.ncbi.nlm.nih.gov/pubmed/29772823
http://dx.doi.org/10.3390/s18051606
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