A flexible and time-efficient schedule-based communication tool for integrated lighting and thermal simulations of spaces with controlled artificial lighting and complex fenestration systems

Complex Fenestration Systems (CFS) incorporate a non-specular layer within the window assembly (e.g. undulated and perforated screens). CFS are often incorporated to the building façade for improving building thermal and visual performance. Thermal–lighting simulations of spaces with CFS are carried out with different tools. However, an integrated lighting and thermal analysis is a complex task to be implemented at the design stage of buildings that incorporates CFS. This paper presents mkSchedule, a new Radiance-based tool that allows the flexible and time-efficient generation of a schedule for the CFS position and luminaries power fraction. The methodology incorporates the three-phase method and use scripts written in Lua programming language. The schedule can be used for detailed thermal–lighting simulations. This work presents the application of this methodology to an office space, showing that the methodology is flexible enough to set different CFS and luminaries' controls, providing annual schedule in short computing time.

[1]  P. Tregenza,et al.  Daylight coefficients , 1983 .

[2]  J. Michalsky,et al.  Modeling daylight availability and irradiance components from direct and global irradiance , 1990 .

[3]  J. Michalsky,et al.  All-weather model for sky luminance distribution—Preliminary configuration and validation , 1993 .

[4]  Gregory J. Ward,et al.  The RADIANCE lighting simulation and rendering system , 1994, SIGGRAPH.

[5]  Georgios A. Florides,et al.  Modeling of the modern houses of Cyprus and energy consumption analysis , 2000 .

[6]  R. Weidenbach,et al.  700 MHz window R & D at LBNL , 2000 .

[7]  Christoph F. Reinhart,et al.  Validation of dynamic RADIANCE-based daylight simulations for a test office with external blinds , 2001 .

[8]  Daniel E. Fisher,et al.  EnergyPlus: creating a new-generation building energy simulation program , 2001 .

[9]  Daniel E. Fisher,et al.  Energyplus: New, capable, and linked , 2004 .

[10]  Toke Rammer Nielsen,et al.  Simple tool to evaluate energy demand and indoor environment in the early stages of building design , 2005 .

[11]  A. Athienitis,et al.  The impact of shading design and control on building cooling and lighting demand , 2007 .

[12]  A. Laouadi,et al.  Optical models of complex fenestration systems , 2007 .

[13]  Maria Wall,et al.  Energy Simulations for Glazed Office Buildings in Sweden , 2008 .

[14]  Toke Rammer Nielsen,et al.  Simple tool to evaluate the impact of daylight on building energy consumption , 2008 .

[15]  Svend Svendsen,et al.  Method and simulation program informed decisions in the early stages of building design , 2010 .

[16]  Athanasios Tzempelikos,et al.  Indoor thermal environmental conditions near glazed facades with shading devices – Part I: Experiments and building thermal model , 2010 .

[17]  Lisa Heschong,et al.  DYNAMIC RADIANCE – PREDICTING ANNUAL DAYLIGHTING WITH VARIABLE FENESTRATION OPTICS USING BSDFS , 2010 .

[18]  Martin Vraa Nielsen,et al.  Quantifying the potential of automated dynamic solar shading in office buildings through integrated simulations of energy and daylight , 2011 .

[19]  Christoph F. Reinhart,et al.  DIVA 2.0: INTEGRATING DAYLIGHT AND THERMAL SIMULATIONS USING RHINOCEROS 3D, DAYSIM AND ENERGYPLUS , 2011 .

[20]  Sandra Mende,et al.  CLIMATE BASED SIMULATION OF DIFFERENT SHADING DEVICE SYSTEMS FOR COMFORT AND ENERGY DEMAND , 2011 .

[21]  Athanasios Tzempelikos,et al.  Daylighting and energy analysis of private offices with automated interior roller shades , 2012 .

[22]  Rodrigo Escobar,et al.  Thermal and lighting behavior of office buildings in Santiago of Chile , 2012 .

[23]  Kostas Laskos,et al.  Assessing cooling energy performance of windows for office buildings in the Mediterranean zone , 2012 .

[24]  Jan Hensen,et al.  Considerations on design optimization criteria for windows providing low energy consumption and high visual comfort , 2012 .

[25]  Hyung-Jo Jung,et al.  Optimization of building window system in Asian regions by analyzing solar heat gain and daylighting elements , 2013 .

[26]  Matthias Haase,et al.  Optimizing the configuration of a façade module for office buildings by means of integrated thermal and lighting simulations in a total energy perspective , 2013 .

[27]  Laura Bellia,et al.  Effects of solar shading devices on energy requirements of standalone office buildings for Italian climates , 2013 .

[28]  Athanasios Tzempelikos,et al.  Sensitivity analysis on daylighting and energy performance of perimeter offices with automated shading , 2013 .

[29]  Kang Soo Kim,et al.  An empirical validation of lighting energy consumption using the integrated simulation method , 2013 .

[30]  Andrew McNeil,et al.  A validation of a ray-tracing tool used to generate bi-directional scattering distribution functions for complex fenestration systems , 2013 .

[31]  Siân Kleindienst,et al.  Interactive expert support for early stage full-year daylighting design: a user’s perspective on Lightsolve , 2013 .

[32]  Andrew McNeil,et al.  A validation of the Radiance three-phase simulation method for modelling annual daylight performance of optically complex fenestration systems , 2013 .

[33]  A. Moret Rodrigues,et al.  Solar and visible optical properties of glazing systems with venetian blinds: Numerical, experimental and blind control study , 2014 .

[34]  Marco Manzan,et al.  Genetic optimization of external fixed shading devices , 2014 .

[35]  Paul Fazio,et al.  Evaluation of radiance's genBSDF capability to assess solar bidirectional properties of complex fenestration systems , 2015 .