Smart Design for Sustainable Neighborhood Development. (February 2019)

• Record Type: Journal Article
• Authors: Quan, Steven Jige
• Is Part Of: Energy procedia. Volume 158(2019)
• Page Start: 6515
• Page End: 6520
• Publication Date: 2019-02
• Subjects: Smart Design -- Sustainable Neighborhood Development -- Design Decision Making -- Multi-objective Optimization -- Genetic Algorithms -- Pareto optimal; Power resources -- Congresses; Power resources -- Periodicals; Power resources; Conference proceedings; Periodicals; 333.7905
• DOI: 10.1016/j.egypro.2019.01.108
• Languages: English
• ISSNs: 1876-6102

Abstract

Abstract: This study proposes the Smart Design method to support the design decision making in the sustainable neighborhood development with multiple objectives. Instead of the "creative design" approach in the scenario making in traditional PSS and recent Geodesign frameworks, the Smart Design method applies the optimization algorithms to search for optimal design solutions in the design space. It integrates the design thinking, computational performance modeling and optimization techniques to efficiently and effectively approximate optimal designs. This method is applied to a hypothetical residential neighborhood design case study with three sustainability objectives: to maximize FAR, to minimize building energy use, and to minimize outdoor human discomfort. Based on the form parameterization, the Nondominated Sorting Genetic Algorithm II (NSGA-II) algorithm is utilized to guide the evolution of the neighborhood design throughout 80 generations, with neighborhood performance modeling tools. The Smart Design method is able to identify 38 representative design solutions as Pareto optimal which are equally optimal. Those solutions set a basis for discussions and negotiations among stake holders to make design decisions with the three objectives. Further research will be focused on addressing the challenges such as recursive objective definitions, parametrization of complex forms, quantification of performances and optimization uncertainties, from simple cases to more realistic and complex designs for sustainable neighborhood development.