In 2011, the US building sector accounted for 41% of the total energy consumed, outperforming the transportation and industrial sectors by 28% and 31%, respectively.* Prior studies show that occupancy pattern detection can reduce the energy consumption of lighting by 50% and air conditioning by 20%.** The prediction of occupancy patterns is particularly important in Heating Ventilation and Air Conditioning (HVAC) control systems because the process of reaching the appropriate temperature is time consuming and costly***, however, the current standard from the American Society of Heating Refrigerating & Air-conditioning Engineers (ASHRAE) is a fixed model assuming identical occupancy patterns across buildings with different uses (e.g. commercial, retail). The objective of this study was to develop an AI-based model that predicted the occupancy pattern of a commercial building (for e.g. Mitsubishi Electric Research Laboratory (MERL)) in order to anticipate the optimal temperature for the HVAC system one hour from the present. We then compare our model of occupancy patterns with current standards from ASHRAE and provide an analysis of energy savings by adopting this model.
Date: December 2014
Course: CS6601 Artificial Intelligence
Skills: MATLAB, Python, HMMs