Occupancy estimation using environmental parameters

• Main Author: Tan, Shannan Jing Xiang
• Other Authors: Soh Yeng Chai
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2022
• Subjects: Engineering::Electrical and electronic engineering::Computer hardware, software and systems
• Online Access: https://hdl.handle.net/10356/158096
• Institution: Nanyang Technological University

Occupancy information is important for energy efficient operations of Air Conditioning and Mechanical Ventilation (ACMV) systems. To predict the occupancy in a room, environmental sensors are increasingly used as cost effective and non-intrusive ways to obtain the occupancy information. This requires extraction of environmental parameters such as carbon dioxide, temperature and humidity which are used to provide a non-intrusive representation of occupancy in the room. These parameters are extracted by a data collection system consisting of a temperature sensor, humidity sensor and a gas sensor. This paper shows prediction using Logistic Regression to predict the occupancy of the room whether it is occupied or not occupied. Experiments are done in a small room in a building with few occupants.