An Overview of Daylighting and Thermal Simulation Coupling: Still More Research Work Needed

By Ravi Srinivasan, CEM, LEED AP

Architects and Engineers employ Building Energy Simulation (BES) tools to determine building energy performance. BES tools such as ESP-r2 (University of Strathclyde, 2008) and EnergyPlus (Clarke and Janak, 1998; Crawley et al., 2002; US Department of Energy, 2008) help extend building design strategies to improve the energy performance of buildings. These tools have undergone strict validation procedures to ensure model and data accuracy for widespread use.

In addition, integrated or stand-alone systems are used to analyze glazing energy performance, daylighting performance, passive heating techniques, etc. Due to advancements in building science research, BES tools allow integration of stand-alone systems with BES tools for further investigation.

Evaluation of the daylighting potential for a building and its addition to the existing design is critical to achieve energy savings. A number of standalone tools were developed for daylighting performance analysis, shading and/or glare analysis, etc. Several research studies were conducted independently on daylighting analysis (Lee et al., 1998; van Dijk and Oversloot, 2003; Andersen and de Boor, 2006; Window 6.1, 2007; Jenkins and Newborough, 2007; Tzempelikos and Athienitis, 2007). Yet only a few were integrated with BES tools. Although these standalone tools provided limited design and user interaction capabilities, they focused on the solar-optical properties of glazing such as spectral irradiance, illuminance, etc, focusing on energy savings.

Dynamic integration for fenestration and shading design on lighting demand and glare control uses a rapid whole-year integrated approach (Lehar and Glicksman, 2007; Walkenhorst et al., 2002; Frankzetti et al., 2004). Others include: DE-light (Hitchcock and Carroll, 2003), which uses a novel approach of interpolating between pre-calculated daylight factors for a limited number of sun positions. There is also a Windows Information System (WIS) integrated for calculating energy demand, detailed control strategy that adjusts the shading based on indoor operative temperature (Hviid et al., 2008). Yet again, these research developments focused on solar-optical properties of glazing and thereby on offsetting electrical energy consumption. Daylight tool developments, and integrated daylighting and thermal simulation research advancements are listed in table 1.

Table 1. Daylighting tools, and integrated daylighting and thermal simulation literature.

One of my research interests is synergistically balancing daylighting and solar heat gain, thereby, maximizing energy savings potential, and minimizing LCC during early stage architectural design evaluation. The purpose of this study is to develop a rapid optimization technique to bypass time-consuming “trial and error” simulations and / or traditional optimization, parametric study approach for balancing daylighting and solar heat gain for the given latitude and orientation.

If you are interested to jointly working on this research, write to me at ravi@greenroundtable.org.

References

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Andersen, M., & de Boer, J. (2006). Goniophotometry and assessment of bidirectional photometric properties of complex fenestration systems. Energy and Buildings , 38 (7), 836- 848.

Architectural Energy Corporation. (2006). SPOT v. 3.1 – Sensor Placement + Optimization Tool, User’s manual.

Bund, S., & Yi-Luen Do, E. (2005). Spot! Fetch Light Interactive navigable 3D visualization of direct sunlight. Automation in Construction, 14 (2), 181-188.

Clarke, J., Janak, M., 1998. Simulating the thermal effects of daylight-controlled lighting. Building Perform-ance (BEPAC UK), Issue 1.

Crawley, D. B., Lawrie, L. K., Pedersen, C. O., Strand, R. K., Liesen, R. J., Winkelmann, F. C., Buhl, W. F., Huang, Y. J., Witte, M. J., Henninger, R. J., Glazer, J., Fisher, D. E., Shirey, D., 2002. Energyplus: New, capable and linked. Proceedings of esim, Montréal, Canada, pp. 244-251

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Chutarat, A. (2001). Experience of Light: The Use of an Inverse Method and a Genetic Algorithm in Daylighting Design. Ph.D. Thesis, MIT, Building Technology, Dept of Architecture, Cambridge.

van Dijk, D., Oversloot, H., 2003. WIS, the European tool to calculate thermal and solar properties of win-dows and window components. Proceedings of IBPSA, Building Simulation, Eindhoven, Netherlands, pp.259-266

ESP-r, Energy Systems Research Unit, University of. C Strathclyde.

Franzetti, C., Fraisse, G., Achard, G., 2004. Influence of the coupling between daylight and artificial lighting on thermal loads in office buildings. Energy and Buildings 36 (2), 117-126

Hviid CA, Nielsen R, and Svendsen S. Simple Tool to Evaluate the Impact of Daylight on Building Energy Consumption. In Solar Energy 82 (2008): 787-798.

Jenkins, D., Newborough, M., 2007. An approach for estimating the carbon emissions associated with office lighting with a daylight contribution. Applied Energy 84 (6), 608-622.

Lehar, M., & Glicksman, L. (2007). Rapid algorithm for modeling daylight distributions in office buildings. Building and Environment , 42 (8), 2908–2919.

Lee, E.S., DiBartolomeo, D.L., Selkowitz, S.E., 1998. Thermal and daylighting performance of an automated venetian blind and lighting system in a full-scale private office. Energy and Buildings 29 (1), 47-63

Paule, B., & Scartezzini, J.-L. (1997). “Leso-DIAL”, a new Computer-based Daylighting Design Tool. Right Light 4. 1. IAEEL.

Reinhart, C., Bourgeois, D., Dubrous, F., Laouadi, A., Lopez, P., & Stelescu, O. (2007). Daylight 1-2-3 - A state-of-the-art daylighting/energy analysis software for initial design investigations . Proceedings of the Buildings Simulation 2007 (IBPSA). Beijing, China, September 3-6.

Tzempelikos, A., Athienitis, A.K., 2007. The impact of shading design and control on building cooling and lighting demand. Solar Energy 81 (3), 369-382

US Department of Energy, 2008. EnergyPlus, Energy Efficiency and Renewable Energy, Washington DC, USA. Available from: http://apps1.eere.energy.gov/buildings/energyplus/

Ward, G.L., Shakespeare, R.A., 1998. Rendering with Radiance - The art and science of lighting visualiza-tion, 2nd ed., Morgan Kaufmann, San Francisco

Walkenhorst, O., Luther, J., Reinhart, C., & Timmer, J. (2002). Dynamic annual daylight simulations based on one-hour and one-minute means of irradiance data. Solar Energy, 72 (5), 385-395.

Window 6.1 Research Version, 2007. Huizenga, C., Arasteh, D., Curcija, D., Klems, J., Kohler, C., Mitchell, R., Yu, T. Ver. 6.1.06. Windows and Daylighting Group, LBNL, Berkeley, California

Image Credit: US Department of Energy