An Anomaly Detection Model for Enhancing Energy Management in Smart Buildings

Muhammad Fahim, Alberto Sillitti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Smart buildings provide an excellent opportunity to monitor the energy consumption behavior. It can assist the building management to find unexpected energy usage patterns. In this research, we present our model to find abnormal energy consumption patterns by analyzing the temporal data streams gathered from smart meters. We investigate support vector regression with radial basis function to find the mismatch between actual and expected energy consumption. It has the ability to map the non-linearity of data and predict expected energy consumption. We build the energy usage profile and provide visualization services over it. Furthermore, energy profiles may be used for different objectives including customer classification and load forecasting. In this preliminary study, we performed the experiments over a real electrical load measurements dataset collected from a dwelling. The obtained results suggest that our proposed model is feasible and practical solution to detect anomalies and provide good insight to visualize the energy consumption behavior.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm 2018): Proceeding
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781538679548
DOIs
Publication statusPublished - 27 Dec 2018
Externally publishedYes
Event2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2018 - Aalborg, Denmark
Duration: 29 Oct 201831 Oct 2018

Publication series

Name2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2018

Conference

Conference2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2018
Country/TerritoryDenmark
CityAalborg
Period29/10/201831/10/2018

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.

Keywords

  • Anomaly detection
  • Smart buildings
  • Support vector regression
  • Time-series analysis

ASJC Scopus subject areas

  • Artificial Intelligence
  • Energy Engineering and Power Technology

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