Learning individual thermal comfort using robust locally weighted regression with adaptive bandwidth

Ensuring that the thermal comfort conditions in offices are in line with the preferences of the occupants, is one of the main aims of a heating/cooling control system, in order to save energy, increase productivity and reduce sick leave days. The industry standard approach for modelling occupant comfort is Fanger's Predicted Mean Vote (PMV). Although PMV is able to predict user thermal satisfaction with reasonable accuracy, it is a generic model, and requires the measurement of many variables (including air temperature, radiant temperature, humidity, the outdoor environment) some of which are difficult to measure in practice (e.g. activity levels and clothing). As an alternative, we propose Robust Locally Weighted Regression with Adaptive Bandwidth (LRAB) to learn individual occupant preferences based on historical reports. As an initial investigation, we attempt to do this based on just one input parameter, the internal air temperature. Using publicly available datasets, we demonstrate that this technique can be significantly more accurate in predicting individual comfort than PMV, relies on easily obtainable input data, and is fast to compute. It is therefore a promising technique to be used as input to adpative HVAC control systems.