The good indoor light environment and comfort of the teaching space are very important for students’ physical and mental health. Meanwhile, China advocates energy conservation and emission reduction policies. However, in order to obtain lower building energy consumption, higher thermal comfort, and daylighting, architects use performance simulation software to repeatedly simulate and refine, which is time-consuming and difficult to obtain the best results from three performances. Given this problem, we constructed the design framework in the early stage of the architectural design of the teaching building. In the first stage of the framework, architects optimized the performance objectives of lighting, thermal comfort, and energy consumption, and performed a cluster analysis on the optimized non-dominated solution to provide a reference for the architect. In the second stage of the framework, architects used the data generated in the optimization process to train the BP neural network and use the trained BP neural network to predict the performance of the building. In this paper, we selected Nanjing Donglu Middle School as a case study. The optimization of the building performance was assessed by a genetic algorithm, generating 3000 sets of sample data during the optimization iteration. Then, we analyzed the non-dominated solution of the sample data through the method of cluster analysis and trained the BP neural network with the sample data as a data set. The prediction model with R-values of 0.998 in the training set and test set was obtained by repeatedly debugging the number of neurons in the BP neural network. Finally, five groups of design parameters were randomly selected and brought into the trained BP neural network, and the predictive value was close to the simulated value. The construction of the framework provides design ideas for architects in the early teaching of building design and helps designers to make better decisions.

The construction sector’s significant energy consumption poses a substantial challenge to achieving global “Carbon Peak and Carbon Neutrality” goals. This study addresses this challenge by proposing a sustainable design framework to optimize atrium spaces in commercial complexes within severe cold regions, where the conflict between high heating energy demands and the pursuit of high-quality spatial experiences is acute. Our climate-adaptive method integrates parametric modeling (Grasshopper) with building performance simulation (Ladybug Tools and Honeybee) to form a multi-objective optimization process using the NSGA-II algorithm. The goal is to simultaneously minimize operational energy (by reducing the seasonal solar heat gain difference, D-RAD) and enhance occupant well-being (by improving useful daylight illuminance, SUMUDI, and thermal discomfort, SUMPPD). Results demonstrate that our framework generated design solutions that significantly improve environmental performance compared to a baseline model: aggregate useful daylight illuminance (SUMUDI) increased by 90.2%, the solar heat gain difference (D-RAD) was reduced by 40.8%, and thermal discomfort (SUMPPD) decreased by 22.7%. This research provides a quantifiable and replicable methodology for sustainable architectural design, contributing directly to the measurement and monitoring of sustainability in the built environment by balancing energy conservation with human-centric design.