Architectural Shape Optimization

Low-energy typical housing typologies in humid temperate climate

Overview

Abstract

This paper explores the architectural shape optimization of typical housing typologies: slab and high-rise residential buildings to reduce primary energy (PE) consumption. The method considered adds passive strategies at the concept design stage. The model is run for Buenos Aires, which has a warm temperate humid climate.

The proposed workflow consists of using the building envelope and dimensions as parametric inputs to calculate the PE index using a quasi-steady state method: the Argentinian energy labeling Standard IRAM 11900. Since this Standard does not consider either humidity or passive strategies, the methodology calculates two bioclimatic indicators: the passive volume ratio (PVR) and the photovoltaic potential of roofs and North facades (PVP). The PVR considers the natural ventilated and daylighted perimetric area, and the PVP measures the potential to provide solar energy to decrease PE consumption. A commonly used indicator to measure building energy efficiency is the shape coefficient (SC). However a low SC does not mean a high energy efficient building in temperate humid climates because of the influence of humidity. The psychrometric chart provides the recommended passive strategies for this case.

A multi-objective genetic algorithm (GA) optimizes the building shape, minimizing the PE consumption, and maximizing PVR and PVP. A Pareto front selects the non-dominated solutions that allow designers to understand how the variation of the building shapes influences the results. The methodology produces and evaluates complex shapes whose relations are not straightforwardly deduced. The best shapes are those that present the largest N facades and PVR, and the smallest W direction, which may provoke summer overheating. Finally a multi-variate linear regression shows the interdependence of the variables.

This workflow is suitable for the early stages of design since it allows designers to explore architectural shapes that reflect the complex relation between morphologies, passive strategies and energy performance, while they can keep control of spatial and aesthetics issues.


Authors

Photo of Patricia Edith Camporeale, PhD

Patricia Edith Camporeale, PhD

Posdoc researcher

National University of La Plata

pcamporeale@fau.unlp.edu.ar


Keywords

Introduction

Buildings account for nearly 40% of the primary energy consumption worldwide showing a growing tendency as population increases. In fact, cities will host nearly 70% of the world population by

Access Restricted

Members get unlimited access to all of our resources. Join now for the best value.

Background

Genetic algorithms (GA) constitute a branch of meta-heuristic algorithms which are inspired in the process of evolution that rules the natural world (Reynolds et al. 2019). Therefore, generative design consists

Access Restricted

Members get unlimited access to all of our resources. Join now for the best value.

Case Study

The case study is a new housing block in an urban plot in Buenos Aires. The research is structured in several steps using Rhinoceros 6 (P. Cook 2013), the algorithmic

Access Restricted

Members get unlimited access to all of our resources. Join now for the best value.

Data

The chart shows the results for 30 of the Pareto front non-dominated solutions that have been selected among a pool of 100 (Table 4). The final PEIWA is defined as

Access Restricted

Members get unlimited access to all of our resources. Join now for the best value.

Explanation

The morphological solutions depict how the three objective functions influence the architectural shape. The optimization process provides the designer with a set of optimal alternatives to begin a formal exploration

Access Restricted

Members get unlimited access to all of our resources. Join now for the best value.

Conclusion and Future Work

The results show the complex nature of architectural design in the early stages and intend to add sustainability goals like energy savings and GHG emissions reduction to this step. The

Access Restricted

Members get unlimited access to all of our resources. Join now for the best value.

Acknowledgements

The author acknowledges Eric Denovitzer for the proofreading of the manuscript.

Rights and Permissions

A. Payne, and R. Issa. 2009. The Grasshopper Primer. 2nd edition, USA: Lift Architects. https://static1.squarespace.com/static/51c6f9f3e4b0e47ad1bbc71c/t/521cf940e4b021571fc7d3a5/1377630528615/Grasshopper+Primer_Second+Edition_090323.pdf.

AENOR. 2011. “UNE-EN ISO 13790: 2011 Eficiencia Energética de Los Edificios. Cálculo Del Consumo de Energía Para Calefacción y Refrigeración de Espacios. (ISO 13790: 2008).” Asociación Española de Normalización y Certificación.

Agathokleous. R., G. Barone. A. Buonomano. C. Forzano. S.A. Kalogirou. and A. Palombo. 2019. “Building Facade Integrated Solar Thermal Collectors for Air Heating: Experimentation. Modelling and Applications.” Applied Energy 239 (April): 658–79. https://doi.org/10.1016/j.apenergy.2019.01.020.

Ascione. Fabrizio. Nicola Bianco. Gerardo Maria Mauro. and Giuseppe Peter Vanoli. 2019. “A New Comprehensive Framework for the Multi-Objective Optimization of Building Energy Design: Harlequin.” Applied Energy 241 (May): 331–61. https://doi.org/10.1016/j.apenergy.2019.03.028.

ASHRAE, 2013. 2013 ASHRAE Handbook: Fundamentals. Atlanta: American Society of Heating, Refrigerating and Air-conditioning EngineersE.

“ASHRAE Climatic Design Conditions 2009/2013/2017.” n.d. Accessed April 20. 2019. http://ashrae-meteo.info/.

Attia. Shady, Jan L. M, Hensen. Liliana Beltrán, and André De Herde. 2012. “Selection Criteria for Building Performance Simulation Tools: Contrasting Architects’ and Engineers’ Needs.” Journal of Building Performance Simulation 5 (3): 155–69. https://doi.org/10.1080/19401493.2010.549573.

Baker. Nick, and Koen Steemers. 1996. “LT Method 3.0 — a Strategic Energy-Design Tool for Southern Europe.” Energy and Buildings. PLEA ’94 International Conference. 23 (3): 251–56. https://doi.org/10.1016/0378-7788(95)00950-7.

Bianconi. Fabio. Marco Filippucci. and Alessandro Buffi. 2019. “Automated Design and Modeling for Mass-Customized Housing. A Web-Based Design Space Catalog for Timber Structures.” Automation in Construction 103 (July): 13–25. https://doi.org/10.1016/j.autcon.2019.03.002.

Brito. M.C., S. Freitas. S. Guimarães, C. Catita, and P. Redweik. 2017. “The Importance of Facades for the Solar PV Potential of a Mediterranean City Using LiDAR Data.” Renewable Energy 111 (October): 85–94. https://doi.org/10.1016/j.renene.2017.03.085.

Catita. C., P. Redweik, J. Pereira, and M.C. Brito. 2014. “Extending Solar Potential Analysis in Buildings to Vertical Facades.” Computers & Geosciences 66 (May): 1–12. https://doi.org/10.1016/j.cageo.2014.01.002.

Chen. Xi. Junchao Huang. Hongxing Yang, and Jinqing Peng. 2019. “Approaching Low-Energy High-Rise Building by Integrating Passive Architectural Design with Photovoltaic Application.” Journal of Cleaner Production 220 (May): 313–30. https://doi.org/10.1016/j.jcle... Consultant.” n.d. Accessed June 19. 2019. http://www.energy-design-tools.aud.ucla.edu/climate-consultant/.

Díez-Mediavilla. M., M.C. Rodríguez-Amigo. M.I. Dieste-Velasco. T. García-Calderón. and C. Alonso-Tristán. 2019. “The PV Potential of Vertical Facades: A Classic Approach Using Experimental Data from Burgos. Spain.” Solar Energy 177 (January): 192–99. https://doi.org/10.1016/j.sole... - SOP - PISA.” n.d. Accessed April 21. 2019. https://sop.tik.ee.ethz.ch/pisa/.

IEA, 2013. Transition to Sustainable Buildings: Strategies and Opportunities to 2050. Paris.

IRAM, 1996. IRAM 11605. Acondicionamiento Térmico de Edificios. Condiciones de Habitabilidad En Edificios Valores Máximos de Trasmitancla Térmica En Cerramientos Opacos. Buenos Aires: Argentine Normalisation and Certification Institute.

IRAM. 2001. IRAM STANDARD 11604 Aislamiento Térmico de Edificios. Verificación de Sus Condiciones Higrotérmicas. Ahorro de Energía En Calefacción. Coeficiente Volumétrico G de Pérdidas de Calor. Cálculo y Valores Límites. 2nd ed. Buenos AIres.

IRAM. 2002. IRAM 11601 Aislamiento Térmico de Edificios. Métodos de Cálculo. Propiedades Térmicas de Los Componentes y Elementos de Construcción En Régimen Estacionario. Buenos AIres: Argentine Normalisation and Certification Institute.

IRAM. 2010a. IRAM STANDARD 11507-1 Carpintería de Obra. Ventanas Exteriores. Requisitos Básicos y Clasificación. Bs As. Buenos AIres: Argentine Normalisation and Certification Institute.

IRAM. 2010b. IRAM STANDARD 11507-4 Carpintería de Obra y Fachadas Integrales Livianas. Ventanas Exteriores. Parte 4- Requisitos Complementarios. Aislación Térmica. Buenos AIres: Argentine Normalisation and Certification Institute.

IRAM. 2011. IRAM STANDARD 11603 Acondicionamiento Térmico de Edificios- Clasificación Bioambiental de La República Argentina-. Buenos AIres: Argentine Normalisation and Certification Institute.

IRAM. 2012. IRAM 11603. Acondicionamiento Térmico de Edificios. Clasificación Bioambiental de La República Argentina. Buenos Aires. Argentine Normalisation and Certification Institute.

IRAM, 2018. “IRAM 11900 Energy Performance in Residential Units. Calculation Method.” Instituto Argentino de Normalización y Certificación.

Kottek Markus, Jürgen Grieser, Christoph Beck, Bruno Rudolf, and Franz Rubel. 2006. “World Map of the Köppen-Geiger Climate Classification Updated.” Meteorologische Zeitschrift 15 (3): 259–63. https://doi.org/10.1127/0941-2... Meishun, Yiqun Pan, Weiding Long, and Weizhen Chen. 2014. “Influence of Building Shape Coefficient on Energy Consumption of Office Buildings in Hot-Summer-and-Cold-Winter Area of China.” In Nagoya. Japan. http://ibpsa.org/proceedings/asim2014/160_AsimC5-29-293.pdf.

“Octopus.” 2012. Text. Food4Rhino. December 6. 2012. https://www.food4rhino.com/app/octopus.

P. Cook. 2013. Rhinoceros v5.0. Level 1. Training Manual. Seattle: Robert McNeel & Associates.

Ratti. Carlo. Nick Baker. and Koen Steemers. 2005. “Energy Consumption and Urban Texture.” Energy and Buildings 37 (7): 762–76. https://doi.org/10.1016/j.enbuild.2004.10.010.

Reynolds. Jonathan. Muhammad Waseem Ahmad. Yacine Rezgui. and Jean-Laurent Hippolyte. 2019. “Operational Supply and Demand Optimisation of a Multi-Vector District Energy System Using Artificial Neural Networks and a Genetic Algorithm.” Applied Energy 235 (February): 699–713. https://doi.org/10.1016/j.apenergy.2018.11.001.

Shi. Xing. and Wenjie Yang. 2013. “Performance-Driven Architectural Design and Optimization Technique from a Perspective of Architects.” Automation in Construction 32 (July): 125–35. https://doi.org/10.1016/j.autc... Urbanization Prospects - Population Division - United Nations.” n.d. Accessed March 25. 2019. https://population.un.org/wup/... Anxiao, Regina Bokel, Andy van den Dobbelsteen, Yanchen Sun, Qiong Huang, and Qi Zhang. 2017. “The Effect of Geometry Parameters on Energy and Thermal Performance of School Buildings in Cold Climates of China.” Sustainability 9 (10): 1708. https://doi.org/10.3390/su9101... Longwei, Lingling Zhang, and Yuetao Wang. 2016. “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.” Solar Energy 132 (July): 38–50. https://doi.org/10.1016/j.solener.2016.02.053.

Znouda, Essia. Nadia Ghrab-Morcos, and Atidel Hadj-Alouane. 2007. “Optimization of Mediterranean Building Design Using Genetic Algorithms.” Energy and Buildings 39 (2): 148–53. https://doi.org/10.1016/j.enbu... paper content here.