- Developing an integrated prediction model for daylighting, thermal comfort, and energy consumption in residential buildings based on the stacking ensemble learning algorithm. In: Building Simulation. :
- Utilizing interpretable stacking ensemble learning and NSGA-III for the prediction and optimisation of building photo-thermal environment and energy consumption. In: Building Simulation, v. 17, n. 5 (January 2024). (2024):
- Data-driven prediction and optimization of residential building performance in Singapore considering the impact of climate change. In: Building and Environment, v. 226 (December 2022). (2022):
- Optimization and prediction in the early design stage of office buildings using genetic and XGBoost algorithms. In: Building and Environment, v. 218 (June 2022). (2022):
- The influence of the spatial characteristics of urban green space on the urban heat island effect in Suzhou Industrial Park. In: Sustainable Cities and Society, v. 40 (July 2018). (2018):