Multi-performance collaborative optimization of existing residential building retrofitting in extremely arid and hot climate zone: A case study in Turpan, China

Multi-dimensional performance optimization of buildings is crucial to achieving comfort and realizing green and sustainable development. This study developed a multi-performance collaborative optimization framework (MPC_BPO) to analyze modern residential retrofitting in extremely arid and hot climate zone. The parametric environmental simulation (PES) demonstrated high consistency with actual operating results (R-2 > 0.92), and the multi-objective optimized artificial neural network (OPT-ANN) effectively established a high precision multi- performance prediction model (R-2 > 0.96). Various multi-objective algorithms exhibited a high convergence level in the high-dimensional complex optimization process. The entropy weight and subjective weight methods ranked the Pareto frontier solution set, where the optimization scheme dominated by energy use intensity (EUI) increased daylighting index (DLI) and thermal comfort hours (TCH) by 12.32 % and 6.67 %, respectively, while reducing EUI by over 8.47 %. Applying different scenarios to other household buildings verified the generalization characteristics, showing overall high consistency despite variations in performance indicators. This research provides a scientific basis for improving residential performance in special climate zones through an appropriate framework. Using scientific methods, it analyzes the intrinsic logic between high- dimensional parameters and multiple building performances.