A generative approach to explore residential urban form potential based on environmental performance for energy efficiency

Abstract

The United Nations predicts that by 2050, 70% of people will live in cities, which will bring significant environmental challenges, including climate change and resource depletion. In India, the residential sector account for 30% of total energy consumption. Therefore, it is crucial to optimize energy use in residential structures to address urban population growth, density, and energy efficiency. This study explores the use of genetic algorithms for multi-objective optimization in the design of energy efficient residential urban forms linking urban population growth and environmental concerns. Urban form significantly influences energy consumption within neighborhoods. However, introducing genetic algorithms into urban planning, a field not typically associated with such computational methods, presents considerable challenges. This research addresses complexities such as simulating and optimizing energy use by focusing on the control of solar radiation and the enhancement of natural daylight within residential neighborhoods. It further seeks to enhance the quality of life in open spaces by improving the sky view factor and the sense of spaciousness, adding another layer of complexity to the optimization. In response to these complexities, a framework is developed using the Biomorpher plugin, integrating genetic algorithms in Grasshopper. This research, carried out in Ahmedabad, India, progresses in two primary phases. The experiment progresses in two phases: first, developing prototypes considering factors like Floor Space Index and building types; and second, refining these through multiple design iterations based on environmental criteria. The study generates a diverse range of optimized scenarios, such as low-rise, mid-rise, and high-rise densities, to understand the relationship between residential form and energy consumption. The research identifies energy-efficient configurations through analysis, offering adaptable solutions tailored to specific environmental and spatial contexts.Additionally, it offers a replicable methodology for urban designers and policymakers.