157
Views
5
CrossRef citations to date
0
Altmetric
Articles

The energy performance of the Central Sunlighting System

&
Pages 234-247 | Received 24 Sep 2010, Accepted 25 Jan 2011, Published online: 12 Apr 2011
 

Abstract

This article presents a simulation study to predict the energy performance of the Central Sunlighting System (CSS) installed in open-plan offices. Several simulation tools are combined to conduct the simulations. SkyVision calculates the daylight luminous flux and the lighting and solar heat gains of the CSS. A set of coefficients pre-calculated using Radiance relates the desktop illuminances to the CSS luminous fluxes. DaySim is used to compute the daylight illuminance from the perimeter windows. ESP-r is used to compute the heating and cooling energy use of the office spaces. The results show that the CSS may save a significant amount of energy in North American climates. Energy savings from the combination of daylighting from windows and the CSS for typical, four-cubicle, open-plan offices range from 44% to 57% for lighting, 8% for cooling and from 14% to 23% for the total (lighting, cooling and heating) energy.

Acknowledgements

This work was funded by the Institute for Research in Construction of the National Research Council Canada (NRC-IRC), and the Panel on Energy Research and Development (PERD) administered by Natural Resources Canada. The authors are very thankful for their contribution. The authors thank Dr Lorne Whitehead and Dr Michele Mossman from the University of British Columbia (Canada) for providing detailed information on the construction and function of the CSS.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 297.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.