A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand
Because of extensive variations in occupancy patterns around office space environments and their use of electrical equipment, accurate occupants’ behaviour detection is valuable for reducing the building energy demand and carbon emissions. Using the collected occupancy information, building energy m...
Main Authors: | Paige Wenbin Tien, Shuangyu Wei, John Calautit |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-12-01
|
Series: | Energies |
Subjects: | |
Online Access: | https://www.mdpi.com/1996-1073/14/1/156 |
Similar Items
-
Occupancy heat gain detection and prediction using deep learning approach for reducing building energy demand
by: Paige Wenbin Tien, et al.
Published: (2021-09-01) -
Machine Learning and Deep Learning Methods for Enhancing Building Energy Efficiency and Indoor Environmental Quality – A Review
by: Paige Wenbin Tien, et al.
Published: (2022-11-01) -
The Effect of Airflow Velocity through a Laminar Airflow Ceiling (LAFC) on the Assessment of Thermal Comfort in the Operating Room
by: Pavol Mičko, et al.
Published: (2023-04-01) -
Utility of BIM-CFD Integration in the Design and Performance Analysis for Buildings and Infrastructures of Architecture, Engineering and Construction Industry
by: Ki-Yeob Kang, et al.
Published: (2022-05-01) -
Occupancy detection in non-residential buildings - A survey and novel privacy preserved occupancy monitoring solution
by: J. Ahmad, et al.
Published: (2021-04-01)