Efficient Modeling Strategies for Performance-based Building Design Supported by Daylight and Building Energy Simulations
The resources involved in the construction and operation of buildings represent nearly 40% of the global emissions of greenhouse gases (GHG), making the building sector one of the primary contributors to global warming. This reality has led to the creation of many prescriptive regulatory and voluntary programs that aim to mitigate the environmental impact of the building sector while ensuring high standards for Indoor Environmental Quality (IEQ), particularly those regarding the thermal and visual comfort of building occupants. Thus, the design of high-performance buildings, i.e., resource- and energy-efficient buildings that yield high levels of IEQ, is a pressing need. This scenario pushes architects to simulate their projects’ environmental performance to better support design tasks in a process referred to as performance-based design.
This dissertation studies the integration of daylighting and Building Energy Simulation (BES) tools into performance-based design supported by computational design (CD) methods, particularly parametric design and Building Performance Optimization (BPO). The assumption is that the early integration of parametric, BES, and daylighting simulation tools can be highly effective in the design, analysis, and optimization of high-performance buildings.
However, the research argues that the current daylighting and Building Energy Simulation (BES) tools pose critical challenges to that desirable integration, thus hindering the deployment of efficient exploratory design methods such as Parametric Design and Analysis (PDA) and BPO. These challenges arise from limitations regarding (i) tool interoperability, (ii) computationally expensive simulation processes, and (iii) problem and performance goal definition in BPO.
The primary objective of the dissertation is to improve the use of daylighting and BES tools in PDA and BPO. To that end, the research proposes and validates five modeling strategies that directly tackle the limitations mentioned above. The strategies are the following: (i) Strategy A: Automatically generate valid building geometry for BES; (ii) Strategy B: Automatically simplify building geometry for BES; (iii) Strategy C: Abstract Complex Fenestration Systems (CFS) for BES; (iv) Strategy D: Assess glare potential of indoor spaces using a time and spatial sampling technique; and (v) Strategy E: Painting with Light - a novel method for spatially specifying daylight goals in BPO.
The research work shows that the strategies address the research problem and current limitations by (i) improving the interoperability between design and BES and daylighting simulation tools (Strategies A, B, and C); (ii) producing quick and adequate feedback on the daylight, thermal, and energy behavior of buildings (Strategies B, C, and D); and (iii) facilitating the spatial definition of performance goals in daylighting BPO workflows (Strategy E). These three important merits of the proposed strategies effectively contribute to improving the efficiency of using daylight and BES tools in the design, analysis, and optimization of high-performance buildings.
Strategies A, B, and C enable the automatic generation of efficient Building Energy Models (BEMs). Strategy A uses advanced planarization techniques to parse any complex curved or double-curved building envelope for EnergyPlus, a state-of-the-art BES. In order to improve calculation times and thus performance feedback, Strategy B simplifies the models generated by Strategy A. The resulting simplified BEMs run significantly faster than equivalent standard BEMs without compromising the quality of simulation output. Strategy C combines co-simulation and linear regression techniques to generate BEM surrogates of sophisticated façade systems, which are easily designed with parametric approaches. The resulting surrogates run quickly and are useful for year-based building energy analysis.
Strategy D provides an alternative method to initial visual comfort studies by reducing the use of computationally expensive simulations required by some building standards (e.g., EN 170377 – Daylight in Buildings). The strategy utilizes easier-to-compute daylight metrics to spatially assess glare potential and identify worst-case scenarios in order to conduct detailed point-in-time glare simulations.
Strategy E implements a painting-style interface that helps designers to spatially specify daylight goals in indoor spaces. Hence, the strategy (i) reduces the difficulty of defining the daylight optimization (or design) problem, (ii) expands the generative potential of goal-oriented design procedures for daylighting design, and (iii) mitigates the gap between standard optimization approaches used in inverse-design and common methods applied in architectural design.
Finally, the dissertation discusses the merits and limitations of each strategy, provides useful guidelines and recommendations for their use in building design, and suggests future directions for further research.