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