Occupancy estimation using environmental parameters

Abstract

In recent years, there has been a rising concern over global warming. Thus. there is an increase in global efforts on improving energy efficiency. Past related work indicated that Heating, Ventilation and Air Conditioning (HVAC) contributed to majority portion of the energy consumption in buildings sector. Therefore, there is a huge interest in automating the HVAC control in buildings based on occupancy pattern for efficient energy consumption. This final year project report shall discuss the findings of the relationship between occupancy and environmental parameters. Sensors were used in Process Instrumentation Laboratory at Nanyang Technological University (NTU), Singapore to measure temperature, humidity, Carbon Dioxide (CO2) with and without occupant. Based on the real data collected, analysis of the relationship between the occupancy and environmental parameters will be conducted. The author will be making use of Automated Machine Learning (AutoML) tool to determine the optimal machine learning model and the accuracy of the occupancy estimation.