OCCUPANT CENTRIC SCHEMES AND ENERGY SAVINGS

Abstract

Buildings in the world today make up 40% of the world’s global energy consumption, and Singapore is no exception, with 37% of its energy consumption coming from buildings. Thus, new measures need to be implemented to improve energy consumption of buildings in Singapore. The heating, ventilation and air-conditioning (HVAC) systems within buildings is one of the most significant contributors of energy consumption. Thus, the objectives of this study are to conduct a systematic literature review on how the different types of occupant centric controls (OCC) in buildings today can result in energy savings for HVAC systems, and to identify potential OCC options that could be effective for implementation in Singapore. A systematic literature review using the Salsa framework was used to analyse 40 articles from 7 different journals. The results show model predictive control, machine learning and reactive controls as the 3 different OCC options available, with all 3 being able to provide positive energy savings. When used in the context of Singapore, machine learning shows better prospects for implementation in Singapore due to more reliable energy saving results.