Sensor data-driven lighting energy performance prediction

David Caicedo, Ashish Pandharipande

Research output: Contribution to journalArticleAcademicpeer-review

13 Citations (Scopus)

Abstract

We consider the problem of quantifying energy savings when a lighting control system is upgraded. A conventional approach to determine energy savings resulting from the control upgrade is using the energy consumption values over the pre-upgrade and post-upgrade time periods. However, energy consumption depends on operational factors, such as occupancy and daylight conditions, which may be different over the two periods. This may result in incorrect estimation of the energy savings. To address this problem, we consider energy performance prediction using sensor data from the lighting system. Specifically, occupancy and light sensor data in the lighting control system over the pre-upgrade period are used to construct a prediction model of the energy consumption using support vector regression. Sensor data from the post-upgrade period is then used in this model to estimate the energy consumption of the pre-upgrade control system under operating conditions in the post-upgrade period. Using data from an indoor office lighting model, we show that the proposed method provides better estimates of energy savings than the conventional approach.

Original languageEnglish
Article number7489030
Pages (from-to)6397-6405
Number of pages9
JournalIEEE Sensors Journal
Volume16
Issue number16
DOIs
Publication statusPublished - 1 Aug 2016

Keywords

  • energy performance
  • Sensor data analytics
  • smart lighting
  • support vector regression

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