This study develops an approach consisting of a stacking model integrated with a multi-objective optimisation algorithm aimed at predicting and optimising the ecological performance of buildings. The integrated model consists of five base models and a meta-model, which significantly improves the prediction performance. Specifically, the R² value was improved by 9.19% and the error metrics MAE, MSE, MAPE, and CVRMSE were reduced by 69.47%, 79.88%, 67.32%, and 57.02%, respectively, compared to the single prediction model. According to the research on interpretable machine learning, adding the SHAP value gives us a deeper understanding of the impact of each architectural design parameter on the performance. In the multi-objective optimisation part, we used the NSGA-III algorithm to successfully improve the energy efficiency, daylight utilisation and thermal comfort of the building. Specifically, the optimal design solution reduces the energy use intensity by 31.6 kWh/m², improves the useful daylight index by 39%, and modulated the thermal comfort index, resulting in a decrement of 0.69 °C for the summer season and an enhancement of 0.64 °C for the winter season, respectively. Overall, this study provides building designers and decision makers with a tool to make better design decisions at an early stage to achieve a better combination of energy efficiency, daylight utilisation and thermal comfort optimisation in an integrated manner, providing an important support for achieving sustainable building design.

本研究开发了一种由堆叠模型与多目标优化算法相结合组成的方法，旨在预测和优化建筑物的生态性能。集成模型由五个基础模型和一个元模型组成，显着提高了预测性能。具体而言，与单一预测模型相比， R ²值提高了9.19%，误差指标MAE、MSE、MAPE和CVRMSE分别降低了69.47%、79.88%、67.32%和57.02%。根据可解释机器学习的研究，添加SHAP值可以让我们更深入地了解每个架构设计参数对性能的影响。在多目标优化部分，我们使用NSGA-III算法成功提高了建筑的能源效率、日光利用率和热舒适度。具体来说，优化设计方案可降低能源使用强度31.6 kWh/m ²，有效日光指数提高39%，并调节热舒适指数，夏季降温0.69℃，提高冬季分别为 0.64 °C。总体而言，本研究为建筑设计师和决策者提供了一个在早期阶段做出更好的设计决策的工具，以综合的方式实现能源效率、日光利用和热舒适性优化的更好结合，为实现可持续建筑提供了重要支持设计。