Architectural Shape Optimization

Low-energy typical housing typologies in humid temperate climate

Overview

Authors

Photo of Patricia Edith Camporeale, PhD

Patricia Edith Camporeale, PhD

Posdoc researcher

National University of La Plata

pcamporeale@fau.unlp.edu.ar


Keywords


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.

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

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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

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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

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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

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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

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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

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Acknowledgements

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

Rights and Permissions

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