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Related Experiment Video

Updated: Feb 10, 2026

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data
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Improving the Energy Saving Process with High-Resolution Data: A Case Study in a University Building.

Jeongyun Han1, Eunjung Lee2, Hyunghun Cho3

  • 1Department of Transdisciplinary Studies, Seoul National University, Seoul 08826, Korea. hanjy@snu.ac.kr.

Sensors (Basel, Switzerland)
|May 19, 2018
PubMed
Summary

An IoT platform with high-resolution sensors achieved 25.4% energy savings in a university building through a one-week reward program. Sustained savings were observed for 44 days, highlighting the importance of data-driven interventions for energy conservation.

Keywords:
behavior detectiondata resolutiondata-drivenenergy savingintervention design

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Area of Science:

  • Environmental Science
  • Building Energy Systems
  • Behavioral Science

Background:

  • University buildings present significant energy consumption challenges.
  • Effective energy saving strategies require detailed consumption data.
  • Behavioral interventions can influence energy usage patterns.

Purpose of the Study:

  • To evaluate the effectiveness of an IoT platform for energy saving in a university building.
  • To assess the impact of daily feedback and reward systems on energy consumption.
  • To investigate the role of high-resolution data in identifying energy waste and behavioral changes.

Main Methods:

  • Deployment of an Internet of Things (IoT) platform with 1 Hz sampling sensors to collect electric power consumption data.
  • Implementation of a one-week intervention involving daily feedback from an energy delegate and a reward system.
  • Development of data-driven behavior detection measures to differentiate behavioral changes from external factors.

Main Results:

  • Achieved a 25.4% reduction in energy consumption during the one-week experiment.
  • Sustained energy savings of 10% or more for 44 days post-intervention without additional efforts.
  • Demonstrated that high-resolution data is crucial for effective waste behavior investigation and intervention design.

Conclusions:

  • High-resolution, space-time data is pivotal for successful energy saving initiatives.
  • Data-driven behavioral analysis is essential for confirming the impact of interventions in complex environments like university campuses.
  • The findings suggest broader applicability of high-resolution data for various data-driven energy management applications.