Data-Driven Shading Systems

Application for Freeform Glass Facades

Overview

Authors

Photo of Simon Schleicher

Simon Schleicher

University of California, Berkeley

simon_s@berkeley.edu

Photo of Luis Santos

Luis Santos

University of California, Berkeley

luis_sds82@berkeley.edu

Photo of Luisa Caldas

Luisa Caldas

Professor

University of California, Berkeley

lcaldas@berkeley.edu


Keywords


Abstract

Analyzing the energy performance of complex building envelopes, determining the need for sun protection, and assessing the effectiveness of shading devices is a difficult problem. Modeling the complex geometry of facades and shading systems is very cumbersome and usually leads to computationally expensive energy simulations that cannot easily be integrated during the early stages of the design process.Aiming to improve the workflow of modeling and energy simulation, this paper presents a new approach that couplesparametric design with Building Energy Models (BEMs). The proposed method integrates different abstraction and datasimplification processes that convert geometrical parameters of shading systems into thermal and optical glazing properties and thus accelerate building energy simulations. This way, it becomes possible to use results from energy simulations at early design phases and compare, for example, different shading devices or customize solutions to specific facade constraints. To illustrate the potential of this approach, the paper will briefly summarize the general challenges related to shading free form facades and discuss an exemplary envelope in more detail. By mapping incident solar radiation, the authors will show that each facade panel is not only irregular in shape but also unique regarding its solar exposure and resulting shading requirements. This information can be used to assign custom panel properties and inform the shape, orientation, and distribution of common shading devices like glass frits or externally mounted louvers. Finally, by comparing multiple shading solutions, the paper will show how this design process can significantly reduce the facade’s cooling demands.

Introduction

The fact that the building sector is responsible for nearly half (44.6%) of U.S. CO2 emissions, calls for immediate and sustained action (EIA, 2012). Thus, transforming the way buildings are

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Background

One of the current limits in the design of shading systems for freeform facades is the difficulty to assess their energy performance. Digital simulations must consider not only the location

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Method

To illustrate the potential of the proposed workflow, the methodology will be applied on a concrete example: a prototypical freeform glass facade on which different shading systems are deployed. The

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Data

Table 2, shows the summary results of the E+ simulations for all shading alternatives. Figure 10-12 plot the results of the annual energy performance for each individual shading solution. The

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Explanation

Table 2 and Figure 10 show that cooling is the most relevant energy end-use. Even in the best performing case (D), cooling still represents more than 95% of the overall

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Conclusion and Future Work

This work is a first successful step in improving the design and energy performance evaluation of freeform glass facades with data-driven shading systems. By combining parametric modeling with building energy

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Acknowledgements

Work presented in this paper was partially financed by the Portuguese Science and Technology Foundation (FCT) through the PhD scholarship SFRH/BD/98658/2013. The authors like to thank Constantina Tsiara and Tanya Makker for their help in producing rendered images.

Rights and Permissions

ASHRAE. 2002. “Guideline 14-2002, Measurement of Energy and Demand Savings.” American Society of Heating, Ventilating, and Air Conditioning Engineers, Atlanta, Georgia.

ASHRAE. 2013. ASHRAE Standard 90.1-2013 Determination of Energy Savings: Quantitative Analysis.

Coakley, Daniel, Paul Raftery, and Marcus Keane. 2014. “A Review of Methods to Match Building Energy Simulation Models to Measured Data.” Renewable and Sustainable Energy Reviews 37: 123–141.

Crawley, Drury B., Linda K. Lawrie, Frederick C. Winkelmann, Walter F. Buhl, Y. Joe Huang, Curtis O. Pedersen, Richard K. Strand et al. "EnergyPlus: creating a new-generation building energy simulation program." Energy and buildings 33, no. 4 (2001): 319-331.

Energy Information Administration (US), ed. Annual Energy Outlook 2012: With Projections to 2035. Government Printing Office, 2012.

Huizenga, Charlie, Dariush Arasteh, Charlie Curcija, Joe Klems, Christian Kohler, Robin Mitchell, Tiefeng Yu, et al. 2017. WINDOW 7.5. Lawrence Berkeley National Laboratory.

Santos, Luis, Simon Schleicher, and Luisa Caldas. "Automation of CAD models to BEM models for performance based goaloriented design methods." Building and Environment 112 (2017): 144-158.

Sechelmann, Stefan, Thilo Rörig, and Alexander I. Bobenko. "Quasiisothermic mesh layout." In Advances in Architectural Geometry 2012, pp. 243-258. Springer, Vienna, 2013.

Ward, Gregory J., and Francis M. Rubinstein. "A new technique for computer simulation of illuminated spaces." Journal of the Illuminating Engineering Society 17, no. 1 (1988): 80-91.

All images by the authors.