All Research Papers Published In Energy and Built Environment

Research Article
10.1016/j.enbenv.2026.03.003

Built environment control based on multi-objective optimization algorithm: Optimizing thermal comfort, indoor air quality and HVAC energy efficiency

Mar 01, 2026
Energy and Built Environment
Wei Li + 5 more +5

Research Article
10.1016/j.enbenv.2025.10.002

Predicting hourly thermal demand of buildings for digital twin-based energy network modeling using a steady-state model capturing thermal inertia

Oct 01, 2025
Energy and Built Environment
Henrik Alexander Nissen Søndergaard + 4 more +4

Research Article
2
10.1016/j.enbenv.2025.08.002

District heating system assisted by thermochemical sorption heat storage

Aug 01, 2025
Energy and Built Environment
Yongxiang Zhu + 6 more +6

Research Article
10.1016/j.enbenv.2025.06.004

Deciphering domestic energy demand through household electricity and gas consumption

Jun 01, 2025
Energy and Built Environment
Sari Khdhaer Mukhlif + 7 more +7

Research Article
10.1016/j.enbenv.2024.12.004

Self-powered and self-feedback wind energy harvester for intelligent metro air conditioning

Dec 01, 2024
Energy and Built Environment
Congcong Zhang + 7 more +7

• A wind energy harvesting sensor applied to underground air outlets is proposed. • The wind energy harvesting sensor involves four modules for energy efficiency. • The device generates up to 0.708W of power from a single turbine. • The recognition rate of the system reaches 99.92% on deep learning networks. Metro is an essential component of urban transport and has received attention for its intelligibility and sustainability. In this paper, a self-feedback wind energy harvester (SWEH) based on a piston wind pavilion is designed consisting of four modules, namely: a wind energy harvesting module (WEHM), an energy conversion module (ECM), an energy storage module (ESM), a machine learning module (MLM). The SWEH collects the wind energy passing through the piston wind kiosk and gathers information about the voltage generated by the inlet rotor. The MLM module uses a deep learning model based on Convolutional Neural Networks (CNN) to enable SWEH to recognize real-time ventilation status. Experiments show that the maximum output power of the prototype is 0.7 W and the maximum average power is 781 mW. Feedback to get the predicted airflow information and feedback system device of the electrical signal on the deep learning network can reach a 99.92% recognition rate. The experimental results show that the SEWH can collect redundant piston wind energy passing through the metro piston kiosk for application to the appropriate sensors, and the feedback from the SWEH possesses the potential to provide a form of identifying information for energy saving in metro air conditioning.

Research Article
15
10.1016/j.enbenv.2024.11.001

Numerical investigation of urban heat island effect in various urban forms

Nov 05, 2024
Energy and Built Environment
Tingzhen Ming + 6 more +6

Research Article
7
10.1016/j.enbenv.2024.11.003

Enhancing energy efficiency in shipping container house: A novel approach using hybrid louver systems

Nov 01, 2024
Energy and Built Environment
Adnan Ibrahim + 6 more +6

Research Article
4
10.1016/j.enbenv.2024.11.006

Energy consumption characteristics and evaluation of public buildings in Tianjin, China

Nov 01, 2024
Energy and Built Environment
Dandan Ma + 3 more +3

Research Article
2
10.1016/j.enbenv.2024.08.003

Effect of initial temperature and relative humidity on VHP penetration during HEPA in-situ fumigation disinfection

Aug 07, 2024
Energy and Built Environment
Yuan Ye + 2 more +2

Research Article
2
10.1016/j.enbenv.2024.05.004

Multi-domain approach for quality evaluation of building designed to be nearly zero energy: Case study in Mediterranean climate

May 14, 2024
Energy and Built Environment
Rosa Francesca De Masi + 4 more +4