Machine-Learning-Enhanced Building Performance-Guided Form Optimization of High-Rise Office Buildings in China’s Hot Summer and Warm Winter Zone—A Case Study of Guangzhou

Abstract

Given their dominant role in energy expenditure within China’s Hot Summer and Warm Winter (HSWW) zone, high-fidelity performance prediction and multi-objective optimization framework during the early design phase are critical for achieving sustainable energy efficiency. This study presents an innovative approach integrating machine learning (ML) algorithms and multi-objective genetic optimization to predict and optimize the performance of high-rise office buildings in China’s HSWW zone. By integrating Rhino/Grasshopper parametric modeling, Ladybug Tools performance simulation, and Python programming, this study developed a parametric high-rise office building model and validated five advanced and mature machine learning algorithms for predicting energy use intensity (EUI) and useful daylight illuminance (UDI) based on architectural form parameters under HSWW climatic conditions. The results demonstrate that the CatBoost algorithm outperforms other models with an R2 of 0.94 and CVRMSE of 1.57%. The Pareto optimal solutions identify substantial shading dimensions, southeast orientations, high aspect ratios, appropriate spatial depths, and reduced window areas as critical determinants for optimizing EUI and UDI in high-rise office buildings of the HSWW zone. This research fills a gap in the existing literature by systematically investigating the application of ML algorithms to predict the complex relationships between architectural form parameters and performance metrics in high-rise building design. The proposed data-driven optimization framework provides architects and engineers with a scientific decision-making tool for early-stage design, offering methodological guidance for sustainable building design in similar climatic regions.

Topics & Concepts