Agile and integrated workflow proposal for optimising energy use, solar and wind energy potential, and structural stability of high-rise buildings in early design decisions

Postponing performance analysis of buildings until the final stage of the design process often leads to unexpected results, introducing errors and disrupting the architect's workflow. The literature review identifies prior research limitations, motivating this study to develop a high-rise design optimisation workflow, automate design preparation, explore alternative airflow simulation methods, and compare Optimisation (Opt) algorithms with Genetic Algorithms (GAs) for broader design considerations. We thus propose an integrated computational workflow for sustainable building design optimisation with four objectives. This study applied it in the early design phase of a practical energy efficiency-oriented redevelopment project involving a cluster of seven highrise office buildings in Nanjing. The project aimed to decrease annual energy use, increase solar and wind energy potential, and reduce structural displacement. Our methodology primarily utilises the surrogate-model algorithm Radial Basis Function Muti-objective Optimisation (RBFMOpt) algorithm, enabling quick identification of self-and new-energy demands and cost-effective structural solutions for tall buildings while optimising energy performance. Different from traditional genetic algorithm-driven optimisation procedures or with a separated post-optimisation, our workflow follows a pre-design approach. Test results are automatically optimised in a loop without post-processing, aligning with the architect's research objectives and improving the decision-making process. Our study yielded a 12% increase in energy use efficiency and a 23% improvement in structural stability. However, achieving these gains resulted in a 32% decrease in solar energy potential. Furthermore, we provide a comparative study between RBFMOpt and Non-dominated Sorting Genetic Algorithm-II (NSGA-II), offering critical guidance for designers when selecting optimisation algorithms to address efficiency and precision challenges in energy-related design. These findings contribute to an integrated research-for-design workflow, providing data-driven evidence to support early design decisions.