Abstract
Optimization in buildings has been increasingly popular due to its growing availability and documented ability to improve the performance of building designs following specified targets. However, the quality and robustness of optimized solutions may be dependent on how the optimization problem is formulated, and few studies have investigated the impact of modelling choices or optimization strategies. This study presents a simulation-based investigation of the impact of problem formulation in building design optimization using the case study of a PV integrated shading device (PVSD) and an evolutionary algorithm. For this, we modify both the size of the solution space and how it is searched using three different approaches to define the objective function(s): single-objective optimization, bi-objective optimization, and tri-objective optimization. The results show that increasing the size of the solution space provided better designs compared to both a full factorial parametric analysis and an optimized but more rigid model, regardless of the nature and number of objectives. The findings support the idea that exploring the impact of problem formulation may be an important part of the process of optimization in buildings and allows obtaining more insight into the tradeoffs at play and the workings of a selected optimization study.
Highlights
The use of numerical optimization to design buildings and energy systems has become an increasingly popular topic in recent years with many algorithms available to researchers wishing to use optimization [1,2,3,4,5]
While extensive work has been done on benchmarking different optimization algorithms for building design [9,10,11], to the knowledge of the authors, only a handful of studies [12,13,14,15,16] have considered the impact of the phrasing of the optimization problem on the resulting optimal designs
This means that the analysis of variance (ANOVA) analysis could not identify inputs that could be eliminated to reduce the number of parameters based on the relationship between the inputs and the outputs
Summary
The use of numerical optimization to design buildings and energy systems has become an increasingly popular topic in recent years with many algorithms available to researchers wishing to use optimization [1,2,3,4,5]. Often, for computationally slow simulations based on physico-mathematical models such as raytracing, there is little time available to run multiple analysis before time-expensive optimization runs, and modellers must make several assumptions This means they may not have time to consider how the phrasing of their problem will impact their search. While extensive work has been done on benchmarking different optimization algorithms for building design [9,10,11], to the knowledge of the authors, only a handful of studies [12,13,14,15,16] have considered the impact of the phrasing of the optimization problem on the resulting optimal designs This results in a situation in which there are few guidelines available for researchers to understand what an adequate problem formulation is. We distinguish two aspects of problem formulation referred to as “soft” and “hard”
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