A data-driven daylight estimation approach to lighting control

Stefano Borile, Ashish Pandharipande, David Caicedo, Luca Schenato, Angelo Cenedese

Research output: Contribution to journalArticleAcademicpeer-review

28 Citations (Scopus)
83 Downloads (Pure)


We consider the problem of controlling a smart lighting system of multiple luminaires with collocated occupancy and light sensors. The objective is to attain illumination levels higher than specified values (possibly changing over time) at the workplace by adapting dimming levels using sensor information, while minimizing energy consumption. We propose to estimate the daylight illuminance levels at the workplace based on the daylight illuminance measurements at the ceiling. More specifically, this daylight estimator is based on a model built from data collected by light sensors placed at workplace reference points and at the luminaires in a training phase. Three estimation methods are considered: Regularized least squares, locally weighted regularized least squares, and cluster-based regularized least squares. This model is then used in the operational phase by the lighting controller to compute dimming levels by solving a linear programming problem, in which power consumption is minimized under the constraint that the estimated illuminance is higher than a specified target value. The performance of the proposed approach with the three estimation methods is evaluated using an open-office lighting model with different daylight conditions. We show that the proposed approach offers reduced under-illumination and energy consumption in comparison to existing alternative approaches.

Original languageEnglish
Article number7875101
Pages (from-to)21461-21471
Number of pages11
JournalIEEE Access
Publication statusPublished - 1 Jan 2017
Externally publishedYes


  • daylight estimation
  • least squares
  • Lighting control systems
  • linear programming
  • occupancy and daylight adaptation


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