SimAUD2020 Multi-objective Optimization for Zero-Energy Urban Design in China: A Benchmark
Channel: Thomas Wortmann
Date: 2020-05-17

Environmental simulation supports the design of more sustainable, zero-energy neighborhoods, especially when leveraged with multi-objective optimization. This study explores the tradeoff between urban density and energy balance—specifically, monthly load match between energy usage and generation—in terms of courtyard, slab, and tower typologies for a hypothetical neighborhood in Shanghai. Using this problem as a multi-objective optimization benchmark, the study compares the evolutionary algorithms HypE and NSGA-II with RBFMOpt, a novel, machine learning-related algorithm. The study concludes that RBFMOpt finds slightly better Pareto fronts and is much more robust, and that courtyard typologies are the most efficient for both low- and high-density neighborhoods.

Here's the paper on ResearchGate: https://www.researchgate.net/publication/341592609_Multi-Objective_Optimization_for_Zero-Energy_Urban_Design_in_China_A_Benchmark