Occupancy modelling using data driven models - Teng, Sherlyn Xue Qi (Nanyang Technological University, 2023)
Heating, Ventilation and Air-conditioning (HVAC) systems are typically designed using static extreme values, resulting in them being over dimensioned for most of their operating time. To reduce the energy wastage, efficient control of the usage of HVAC can be done through occupancy sensing. Effective feature engineering methods will be used to estimate the accuracy of the occupancy in the building. Non-intrusive aspect of the environmental sensors such as the CO2, humidity, lighting, and temperature is used to collect data. The approach of visualising the different features and analyse its usefulness in estimating the occupancy in the enclosed space will be analysed. In this discussion, various machine learning methods are used to model the occupancy and estimate the accuracy. The models with the best accuracies will be presented and further discussed.