Urban block to district environmental performance optimization

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.

For almost two decades, the Zero Energy Buildings (ZEB) standard has epitomized a commitment to the high energy performance of buildings. Nevertheless, the applicability of ZEB in hot climates is currently limited and furthermore, in light of the current limitations of traditional building energy modeling methods, new methods are necessary to effectively evaluate the energy balance potential of larger districts. To help bridge this gap, this paper introduces solar-based (both sun-hours and solar irradiation) and geometry-based prediction metrics to use in optimization studies to evaluate the impact of urban morphology on the energy balance of residential blocks in hot urban contexts. These prediction metrics are derived from the simulated energy performance of 1,944 parametric variations of residential blocks in Tel Aviv, which is then followed by a regression analysis in which these metrics recorded high correlation with energy demand, energy supply and the balance between them. To test the applicability of these metrics for optimization, the RBFMOpt method is employed in a multi-objective optimization study of the energy supply and demand of a nine-block residential district in Tel Aviv. Detailed energy simulations are performed for the best non-dominated results from the solar and geometric optimization studies and compared to the non-dominated results from a full energy optimization run. The results indicate that these metrics - the solar and geometric area-weighted exposure and shading indices - can serve as effective energy performance indicators to inform early stage morphological decisions making. This workflow promotes urban energy optimization towards more harmonized energy supply and demand driven approaches.