Bi-objective optimization of building enclosure design for thermal and lighting performance

Futrell, Benjamin; Özelkan, Ertunga C.; Brentrup, Dale

doi:10.1016/j.buildenv.2015.03.039

Cited by 96 publications

(46 citation statements)

References 23 publications

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“…Four optimization algorithms implemented in GenOpt are compared: Simplex Algorithm of Nelder and Mead with the Extension of O'Neill (SA), Hooke Jeeves (HJ), Particle Swarm Optimization using Inertia Weight (PSOIW), and a hybrid PSO Constriction/Hooke Jeeves (PSOC/HJ) algorithm. Futrell et al [43] then propose a bi-objective optimization of building enclosures, both for thermal and lighting performance.…”

Section: Related Workmentioning

confidence: 99%

Painting with light: An interactive evolutionary system for daylighting design

Caldas

Santos

2016

Building and Environment

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Painting with Light is a user-guided interactive evolutionary system for daylighting design. It allows architects to use color to specify desired light levels in spaces, and searches for solutions that bring building geometry and materials close to performance targets desired by the architect. The proposed interface directly addresses a main limitation of generative design systems based on building performance metrics, by allowing the user to specify daylight spatial patterns with a high degree of granularity. This development poses new challenges for both objective function and penalty function definitions, addressed in this paper. Painting with Light automatically computes and displays statistical indexes that inform the user on the deviation error between the performance of the solution found by the system and the desired targets. The system was implemented in Python on top of a popular Computer-Aided Design software, Rhinoceros, as an add-on to its Visual Programming Language, Grasshopper. This choice of implementation allows the use of Grasshopper's built-in functions and methods for 3D parametric modeling, of tools that provide direct access to Radiance, a lighting simulation software, and of different types of genetic algorithms. Five experiments were conducted on a freeform parametric model for progressive system calibration, which encompassed four steps: 1) adjustment of painted targets to fit the problem feasible solution space; 2) devise appropriate weights and penalty factors for the fitness function; 3) test two different evolutionary solvers; 4) test the system's capability to find a predefined solution where the optimal values were known. After calibration, the system was able to produce solutions that closely approximate the painted goals.

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Section: Related Workmentioning

confidence: 99%

Painting with light: An interactive evolutionary system for daylighting design

Caldas

Santos

2016

Building and Environment

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“…The necessity to design sustainable buildings has long driven the scientific community towards modelling methodologies and building simulation tools [1,7,18]. The typical approach undertaken in energy renovation studies is to select the most suitable measures, based on the enhancement they can induce on the building performance (e.g.…”

Section: Introductionmentioning

confidence: 99%

Energy renovation of an office building using a holistic design approach

Bournas

Abugabbara

Balcerzak

et al. 2016

Journal of Building Engineering

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“…As we reviewed in the Introduction, many multi-objective algorithms-such as NSGA, NSGA-II, etc.-were successfully applied in various practical optimizations [15,16,20,21].…”

Section: Multi-objective-based Optimization Algorithmmentioning

confidence: 99%

“…More control-friendly and accurate optimization frameworks are frequently reported in the field of building enclosure design. Commercial optimization packages such as GenOpt, BeOpt, and Opt-E-Plus have been successfully applied in various practical optimizations [13][14][15][16]. Futrell et al [16] combined Genopt with EnergyPlus to find efficient solutions to the daylighting and thermal performance of a classroom design.…”

Section: Introductionmentioning

confidence: 99%

“…Commercial optimization packages such as GenOpt, BeOpt, and Opt-E-Plus have been successfully applied in various practical optimizations [13][14][15][16]. Futrell et al [16] combined Genopt with EnergyPlus to find efficient solutions to the daylighting and thermal performance of a classroom design. Asadi et al [15] employed TRNSYS and GenOpt to optimize the retrofit cost, energy savings, and thermal comfort of a residential building.…”

Section: Introductionmentioning

confidence: 99%

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Exploring the Environment/Energy Pareto Optimal Front of an Office Room Using Computational Fluid Dynamics-Based Interactive Optimization Method

Xue

2017

Energies

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This paper is concerned with the development of a high-resolution and control-friendly optimization framework in enclosed environments that helps improve thermal comfort, indoor air quality (IAQ), and energy costs of heating, ventilation and air conditioning (HVAC) system simultaneously. A computational fluid dynamics (CFD)-based optimization method which couples algorithms implemented in Matlab with CFD simulation is proposed. The key part of this method is a data interactive mechanism which efficiently passes parameters between CFD simulations and optimization functions. A two-person office room is modeled for the numerical optimization. The multi-objective evolutionary algorithm-non-dominated-and-crowding Sorting Genetic Algorithm II (NSGA-II)-is realized to explore the environment/energy Pareto front of the enclosed space. Performance analysis will demonstrate the effectiveness of the presented optimization method.

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Bi-objective optimization of building enclosure design for thermal and lighting performance

Cited by 96 publications

References 23 publications

Painting with light: An interactive evolutionary system for daylighting design

Painting with light: An interactive evolutionary system for daylighting design

Energy renovation of an office building using a holistic design approach

Exploring the Environment/Energy Pareto Optimal Front of an Office Room Using Computational Fluid Dynamics-Based Interactive Optimization Method

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