- Multi-point temperature or humidity prediction for office building indoor environment based on CGC-BiLSTM deep neural network. Dans: Building and Environment, v. 267 (janvier 2025). (2025):
- Enhancing multi-scenario data-driven energy consumption prediction in campus buildings by selecting appropriate inputs and improving algorithms with attention mechanisms. Dans: Energy and Buildings, v. 311 (mai 2024). (2024):
- Improving building energy consumption prediction using occupant-building interaction inputs and improved swarm intelligent algorithms. Dans: Journal of Building Engineering, v. 73 (août 2023). (2023):
- Tensile properties of 2D-C/SiC composites at temperatures up to 1873 K at wide-ranging strain rates. Dans: Composite Structures, v. 319 (septembre 2023). (2023):
- Methods on reflecting electricity consumption change characteristics and electricity consumption forecasting based on clustering algorithms and fuzzy matrices in buildings. Dans: Building Services Engineering Research and Technology, v. 43, n. 6 (août 2022). (2022):
- Effect of incorporation of rice husk ash and iron ore tailings on properties of concrete. Dans: Construction and Building Materials, v. 338 (juillet 2022). (2022):
- Quantitative correlation models between electricity consumption and behaviors about lighting, sockets and others for electricity consumption prediction in typical campus buildings. Dans: Energy and Buildings, v. 253 (décembre 2021). (2021):
- Data-driven correlation model between human behavior and energy consumption for college teaching buildings in cold regions of China. Dans: Journal of Building Engineering, v. 38 (juin 2021). (2021):
- A monitoring data based bottom-up modeling method and its application for energy consumption prediction of campus building. Dans: Journal of Building Engineering, v. 35 (mars 2021). (2021):