Fast bidirectional building performance optimization at the early design stage

Decisions made at the early design stage have tremendous impacts on building performance (energy consumption, daylight, life cycle cost, natural ventilation, sunshine hours, etc.). Owing to progress in the design process, the opportunity to improve building performance is constantly reducing, while the cost of optimization is constantly increasing. The literature review shows that the commonly used building performance optimization workflow is divided into two categories: the forward optimization workflow and the inverse optimization workflow. In the forward workflow, designers are allowed to optimize building schemes according to feedback gleaned from the performance metrics; in the inverse workflow, however, designers are allowed to utilize software to search for optimal design solutions. Both workflows have their advantages, and their collective advantages can result in a highly efficient building design; however, in practice, the two processes are often separated. Furthermore, the simulation engines used in these two workflows are simulation software quite widely used. Using these software often requires a large amount of information, which are not suitable for an early design. In this paper, a bidirectional workflow for building performance optimization at the early design stage is proposed. The building energy consumption prediction model is then improved to make the workflow provide real-time performance feedback, and the optimization workflow is realized in SketchUp. This approach can provide quick feedback from building performance metrics, and allows designers to search for optimal solutions, using a genetic algorithm to support early design decisions. Because of the different structures of the simplified model and the standard model in BESTEST, we chose to use the results of DesignBuilder as the baseline to calibrate the simplified model. The model verification results show that the relative deviation of the total energy consumption of working condition 1 and 2 is between 20% and 27% due to the relatively large heating deviation in Beijing. The relative deviation of the total energy consumption of other cities is within 10%. In future work, we plan to rebuild the codes of the simplified model, and perform energy calibration under the standard procedure in BESTEST. Finally, the workflow is illustrated through a case study. Compared to previous studies, through the inverse-forward workflow and the simplified energy prediction model, the proposed workflow is demonstrated to better provide fast performance optimization at the early design stage.

[1]  Mohamed Hamdy,et al.  Mobo A New Software For Multi-objective Building Performance Optimization , 2013, Building Simulation Conference Proceedings.

[2]  Iva Kovacic,et al.  Building life cycle optimization tools for early design phases , 2015 .

[3]  Alfonso P. Ramallo-González,et al.  Using self-adaptive optimisation methods to perform sequential optimisation for low-energy building design , 2014 .

[4]  Mario Sassone,et al.  The early design stage of a building envelope: Multi-objective search through heating, cooling and lighting energy performance analysis , 2015 .

[5]  John Christopher Miles,et al.  The conceptual design of commercial buildings using a genetic algorithm , 2001 .

[6]  Xing Shi.  Design optimization of insulation usage and space conditioning load using energy simulation and gene , 2011 .

[7]  Nick Baker,et al.  Energy and Environment in Architecture: A Technical Design Guide , 2000 .

[8]  Tianzhen Hong,et al.  Building simulation: an overview of developments and information sources , 2000 .

[9]  Maurizio Cellura,et al.  Modeling the energy and environmental life cycle of buildings: A co-simulation approach , 2017 .

[10]  Moncef Krarti,et al.  Genetic-algorithm based approach to optimize building envelope design for residential buildings , 2010 .

[11]  Shady Attia,et al.  "ARCHITECT FRIENDLY": A COMPARISON OF TEN DIFFERENT BUILDING PERFORMANCE SIMULATION TOOLS , 2009 .

[12]  Ulrich Bogenstätter,et al.  Prediction and optimization of life-cycle costs in early design , 2000 .

[13]  R. D. Fischer,et al.  Degree-days method for simplified energy analysis , 1982 .

[14]  Marco Marengo,et al.  Towards energy performance evaluation in early stage building design: A simplification methodology for commercial building models , 2014 .

[15]  Jae-Weon Jeong,et al.  Optimization of a free-form building shape to minimize external thermal load using genetic algorithm , 2014 .

[16]  Roberto Lamberts,et al.  The use of simplified weather data to estimate thermal loads of non-residential buildings , 2004 .

[17]  Khaled Nassar,et al.  Genetic algorithms for ceiling form optimization in response to daylight levels , 2011 .

[18]  Rudi Stouffs,et al.  Design explorations of performance driven geometry in architectural design using parametric modeling and genetic algorithms , 2011, Adv. Eng. Informatics.

[19]  Rabee M. Reffat,et al.  Genetic algorithm based optimization for photovoltaics integrated building envelope , 2016 .

[20]  Zhiqiang John Zhai,et al.  Roles of building simulation tools in sustainable building design , 2014 .

[21]  Karen Kensek.  Building Information Modeling , 2014 .

[22]  J. Owen Lewis,et al.  A Green Vitruvius: Principles and Practice of Sustainable Architectural Design , 2011 .

[23]  Wei Tian,et al.  Relationship between built form and energy performance of office buildings in a severe cold Chinese region , 2017 .

[24]  Jon Sargent,et al.  SHADERADE: COMBINING RHINOCEROS AND ENERGYPLUS FOR THE DESIGN OF STATIC EXTERIOR SHADING DEVICES , 2011 .

[25]  Weimin Wang,et al.  Floor shape optimization for green building design , 2006, Adv. Eng. Informatics.

[26]  Enedir Ghisi,et al.  Residential building design optimisation using sensitivity analysis and genetic algorithm , 2016 .

[27]  Shady Attia,et al.  Simulation-based decision support tool for early stages of zero-energy building design , 2012 .

[28]  Luisa Caldas,et al.  Painting with light: An interactive evolutionary system for daylighting design , 2016 .

[29]  Shengwei Wang,et al.  Simplified building model for transient thermal performance estimation using GA-based parameter identification , 2006 .

[30]  Ep Erik Deiman,et al.  Cost information in succeeding stages of the design process , 1993 .

[31]  I. G. Capeluto,et al.  Advice tool for early design stages of intelligent facades based on energy and visual comfort approach , 2009 .

[32]  P.J.C.J. De Wilde,et al.  Computational Support for the Selection of Energy Saving Building Components , 2004 .

[33]  Moncef Krarti,et al.  Optimization of envelope and HVAC systems selection for residential buildings , 2011 .

[34]  Toke Rammer Nielsen,et al.  Building energy optimization in the early design stages: A simplified method , 2015 .

[35]  Roberta Padovan,et al.  Multi-criteria energy and daylighting optimization for an office with fixed and moveable shading devices , 2014 .

[36]  Lingling Zhang,et al.  Shape optimization of free-form buildings based on solar radiation gain and space efficiency using a multi-objective genetic algorithm in the severe cold zones of China , 2016 .

[37]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[38]  Yun Kyu Yi,et al.  Agent-based geometry optimization with Genetic Algorithm (GA) for tall apartment’s solar right , 2015 .

[39]  Kyle Konis,et al.  Passive performance and building form: An optimization framework for early-stage design support , 2016 .

[40]  J. A. White,et al.  Simplified method for predicting building energy consumption using average monthly temperatures , 1996, IECEC 96. Proceedings of the 31st Intersociety Energy Conversion Engineering Conference.

[41]  Rasmus Lund Jensen,et al.  Building simulations supporting decision making in early design – A review , 2016 .