Early-warning application for real-time detection of energy consumption anomalies in buildings

Jui Sheng Chou, Abdi S. Telaga, Oswald Chong, Edd Gibson

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Energy consumption data must be presented to office occupants to encourage them to save energy when in their office buildings. Therefore, this work develops an early warning application (EWA) that intelligently analyzes electricity consumption and provides a real-time visualization of anomalous consumption based on data from smart meters and sensors to various stakeholders. Although smart meters collect massive amounts of data from different sources, many systems cannot analyze and informatively present rapidly collected data. Accordingly, they do not motivate people to adopt energy-saving behaviors. The contribution of this study is to design an EWA architecture that visually presents real-time anomalous power consumption in an office space based on data that are obtained from various instruments (smart meters and sensors) to office occupants. The anomalous consumption of the EWA dashboard is designed to ensure that office occupants with limited technical skills understand the presented energy consumption data. The collected anomaly data provide post-occupancy information. Electricity consumption data from smart meters and sensors in a real office space are used to demonstrate the effectiveness of the proposed EWA. A building manager can use archived anomaly data to audit energy consumption, to produce an energy reduction policy, and to support a retrofitting strategy.

Original languageEnglish (US)
Pages (from-to)711-722
Number of pages12
JournalJournal of Cleaner Production
Volume149
DOIs
StatePublished - Apr 15 2017

Fingerprint

Smart meters
Smart sensors
Energy utilization
anomaly
Electricity
Office buildings
Retrofitting
sensor
Energy conservation
Managers
Electric power utilization
Visualization
savings behavior
energy consumption
detection
Early warning
Anomaly
Energy consumption
Sensor
visualization

Keywords

  • Anomalous consumption
  • Building energy monitoring
  • Early warning
  • Energy usage and policy
  • Feedback visualization
  • Smart meter

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Environmental Science(all)
  • Strategy and Management
  • Industrial and Manufacturing Engineering

Cite this

Early-warning application for real-time detection of energy consumption anomalies in buildings. / Chou, Jui Sheng; Telaga, Abdi S.; Chong, Oswald; Gibson, Edd.

In: Journal of Cleaner Production, Vol. 149, 15.04.2017, p. 711-722.

Research output: Contribution to journalArticle

@article{f6d6c22eb23b436cb3c0a8ff8d0c978d,
title = "Early-warning application for real-time detection of energy consumption anomalies in buildings",
abstract = "Energy consumption data must be presented to office occupants to encourage them to save energy when in their office buildings. Therefore, this work develops an early warning application (EWA) that intelligently analyzes electricity consumption and provides a real-time visualization of anomalous consumption based on data from smart meters and sensors to various stakeholders. Although smart meters collect massive amounts of data from different sources, many systems cannot analyze and informatively present rapidly collected data. Accordingly, they do not motivate people to adopt energy-saving behaviors. The contribution of this study is to design an EWA architecture that visually presents real-time anomalous power consumption in an office space based on data that are obtained from various instruments (smart meters and sensors) to office occupants. The anomalous consumption of the EWA dashboard is designed to ensure that office occupants with limited technical skills understand the presented energy consumption data. The collected anomaly data provide post-occupancy information. Electricity consumption data from smart meters and sensors in a real office space are used to demonstrate the effectiveness of the proposed EWA. A building manager can use archived anomaly data to audit energy consumption, to produce an energy reduction policy, and to support a retrofitting strategy.",
keywords = "Anomalous consumption, Building energy monitoring, Early warning, Energy usage and policy, Feedback visualization, Smart meter",
author = "Chou, {Jui Sheng} and Telaga, {Abdi S.} and Oswald Chong and Edd Gibson",
year = "2017",
month = "4",
day = "15",
doi = "10.1016/j.jclepro.2017.02.028",
language = "English (US)",
volume = "149",
pages = "711--722",
journal = "Journal of Cleaner Production",
issn = "0959-6526",
publisher = "Elsevier Limited",

}

TY - JOUR

T1 - Early-warning application for real-time detection of energy consumption anomalies in buildings

AU - Chou, Jui Sheng

AU - Telaga, Abdi S.

AU - Chong, Oswald

AU - Gibson, Edd

PY - 2017/4/15

Y1 - 2017/4/15

N2 - Energy consumption data must be presented to office occupants to encourage them to save energy when in their office buildings. Therefore, this work develops an early warning application (EWA) that intelligently analyzes electricity consumption and provides a real-time visualization of anomalous consumption based on data from smart meters and sensors to various stakeholders. Although smart meters collect massive amounts of data from different sources, many systems cannot analyze and informatively present rapidly collected data. Accordingly, they do not motivate people to adopt energy-saving behaviors. The contribution of this study is to design an EWA architecture that visually presents real-time anomalous power consumption in an office space based on data that are obtained from various instruments (smart meters and sensors) to office occupants. The anomalous consumption of the EWA dashboard is designed to ensure that office occupants with limited technical skills understand the presented energy consumption data. The collected anomaly data provide post-occupancy information. Electricity consumption data from smart meters and sensors in a real office space are used to demonstrate the effectiveness of the proposed EWA. A building manager can use archived anomaly data to audit energy consumption, to produce an energy reduction policy, and to support a retrofitting strategy.

AB - Energy consumption data must be presented to office occupants to encourage them to save energy when in their office buildings. Therefore, this work develops an early warning application (EWA) that intelligently analyzes electricity consumption and provides a real-time visualization of anomalous consumption based on data from smart meters and sensors to various stakeholders. Although smart meters collect massive amounts of data from different sources, many systems cannot analyze and informatively present rapidly collected data. Accordingly, they do not motivate people to adopt energy-saving behaviors. The contribution of this study is to design an EWA architecture that visually presents real-time anomalous power consumption in an office space based on data that are obtained from various instruments (smart meters and sensors) to office occupants. The anomalous consumption of the EWA dashboard is designed to ensure that office occupants with limited technical skills understand the presented energy consumption data. The collected anomaly data provide post-occupancy information. Electricity consumption data from smart meters and sensors in a real office space are used to demonstrate the effectiveness of the proposed EWA. A building manager can use archived anomaly data to audit energy consumption, to produce an energy reduction policy, and to support a retrofitting strategy.

KW - Anomalous consumption

KW - Building energy monitoring

KW - Early warning

KW - Energy usage and policy

KW - Feedback visualization

KW - Smart meter

UR - http://www.scopus.com/inward/record.url?scp=85015762490&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85015762490&partnerID=8YFLogxK

U2 - 10.1016/j.jclepro.2017.02.028

DO - 10.1016/j.jclepro.2017.02.028

M3 - Article

VL - 149

SP - 711

EP - 722

JO - Journal of Cleaner Production

JF - Journal of Cleaner Production

SN - 0959-6526

ER -