## 5 Conclusions  
The present study provides a valuable illustration of the efficacy of automated machine learning, in conjunction with the NSGA-II algorithm, in the domain of multi-criteria decision making for the energy-saving retrofitting of residential buildings. The research results reveal that the utilization of a surrogate model can lead to a substantial reduction in optimization time, facilitating rapid and effective analysis and prediction of the effects of diverse energy-saving renovation strategies and identifying the optimal balance among total energy demand, life-cycle carbon emissions, and cost targets. Among the four machine learning algorithms evaluated, the light gradient boosting machine algorithm emerged as the most efficient and accurate model for predicting building energy performance. Through the metaheuristic algorithm and entropy-weighted decision-making mechanism, the obtained Pareto front results, compared to the original design of the case study building, which followed the national energy efficiency design code, showed significant improvements in all three objectives after optimization. The optimal solutions reduced energy consumption by 56.62%, carbon emissions by 51.60%, and life-cycle cost by 24.27%. This highlights the necessity of further retrofitting existing buildings that have already met the national energy efficiency standard from both an energy-saving and life-cycle environmental perspective. Compared to the ultra-low-energy building energy standard, all selected optimal solutions significantly reduced energy emissions by 2.60% and the life-cycle cost by 15.85%. The present study contributes significantly to building energy conservation in two respects: Firstly, it proposes a novel framework that integrates a machine learning surrogate model with a heuristic optimization algorithm and entropy-weighted decision-making mechanism. This novel framework introduces original methodologies for the multi-criteria evaluation process in energy-saving renovation for existing residential buildings. The novel methods simplify the steps of model selection and hyperparameter adjustment, enhancing the prediction model's accuracy. Secondly, the optimal solution can assist residents in optimizing building energy use, facilitating the transition to fossil fuel alternatives, and reducing dependency on traditional energy sources. From economic and environmental viewpoints, these optimal solutions are highly suitable for future renovation strategies that promote sustainable and cost-effective building performance improvements. This study also has limitations that are worth noting. As climate change progresses, long-term building renovation strategies must incorporate projections of future climatic conditions to ensure the durability and efficacy of implemented solutions. Rising temperatures, changing weather patterns, and shifting heating and cooling demands necessitate a climate-responsive approach to retrofitting. For example, in future climate scenarios (e.g., 2050 and 2080), indoor thermal comfort will likely become a critical factor in colder regions,necessitating a re-evaluation of existing retrofit standards and the integration of renewable energy systems, such as photovoltaic panels, to achieve net-zero-energy performance while mitigating carbon emissions. Future studies will focus on conducting urban-scale building energy analyses for residential neighborhoods under projected climate conditions. This broader scope will allow for the development of optimized retrofit strategies at both the individual building and community levels, ensuring that renovation efforts align with the evolving needs of urban areas in response to climate change. Such efforts will enhance energy efficiency and improve urban resilience and adaptability to long-term environmental challenges. Conceptualization, R.S. ; methodology, R.S. ; software, W.L. ; validation, W.L. and H.T. ; formal analysis, W.L. and H.T. ; investigation, W.L. and H.T. ; resources, W.G. ; data curation, W.L. and H.T.