Learning lighting models for optimal control of lighting system via experimental and numerical approach

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskrift

Abstract

Lighting control systems have been traditionally employed to reduce energy use for lighting by, for example, maximizing daylight harvesting. When highly efficient light sources are installed and for tasks where maintaining target illuminance is particularly important, designers may decide to prioritize the latter together with energy use. In this context, the use of data-driven algorithms is emerging. In this paper different data-driven approaches are proposed as lighting control systems, to maximize daylight harvesting
and to optimize energy consumption. The approaches employ experimental data of occupancy and lighting switch on/off events of a private side-lit office in an academic building. The office is later modeled in DIVA4Rhino to provide yearly illuminances and electric lighting dimming profiles. These data are used to implement data-driven optimal controls. Three different approaches have been
employed: Regression Trees; Random Forests; Least Squares. Different lighting control strategies have been hypothesized based on installed Lighting Power Densities (LPD). Results show that Regression Trees outperforms both Least Squares and Random Forests, in terms of model accuracy and control performance.

Detaljer

Författare
  • Tullio de Rubeis
  • Francesco Smarra
  • Niko Gentile
  • Alessandro D'Innocenzo
  • Dario Ambrosini
  • Domenica Paoletti
Enheter & grupper
Externa organisationer
  • University of L'Aquila
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Arkitekturteknik
  • Husbyggnad
  • Reglerteknik
Originalspråkengelska
Sidor (från-till)1018-1030
Antal sidor13
TidskriftScience and Technology for the Built Environment
Volym27
Utgåva nummer8
Tidigt onlinedatum2020 nov 30
StatusPublished - 2021 sep 1
PublikationskategoriForskning
Peer review utfördJa

Relaterad forskningsoutput

Tullio de Rubeis, Niko Gentile, Francesco Smarra, Alessandro D'Innocenzo, Dario Ambrosini & Domenica Paoletti, 2020 mar 20, Proceedings of Building Simulation 2019: 16th Conference of IBPSA. Corrado, V., Fabrizio, E., Gasparella, A. & Patuzzi, F. (red.). Rome: International Building Performance Simulation Association (IBPSA), Vol. 16. s. 1036-1043 5112 s. 210494

Forskningsoutput: Kapitel i bok/rapport/Conference proceedingKonferenspaper i proceeding

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