Learning Occupancy Prediction Models with Decision-Guidance Query Language

Occupancy prediction is a relatively new domain of research. It has gained momentum over the past decade. Varying approaches have been proposed to profile occupancy of buildings or space. Smart occupancy patterns, once predicted, can be effectively used in modeling energy management systems to achieve energy savings. While doing so, we also take into consideration the potential for occupant discomfort. In this paper, we propose DOPM - an occupancy prediction model built by using Decision Guidance Query Language (DGQL) framework that can optimize prediction rules governing occupancy patterns in a domain. Motive of DOPM is to perform two actions: a) Maximize energy saved in a location and b) limit inconvenience caused to its occupants in the process. This paper presents a generic DOPM model. A case study is developed for occupancy prediction on a university campus setting and the results of running the model will be presented.