Abstract
In this paper, we present a simple yet effective algorithm to automatically assign reference temperature set-points based
on the occupancy information. Both the binary and detailed occupancy estimation cases are considered. In the first
case study, we assume the schedule involves only binary states (occupied or not occupied), i.e. the room is invariant.
With long-term observations occupancy levels can be estimated using statistical tools. In the second case study, two
techniques are introduced. Firstly, we propose an identification-based approaches. More precisely, we identify the
models via Expectation Maximization (EM) approach. The statistical state space model is built in linear form for
the mapping between the occupancy measurements and real occupancy states with noise considered. Secondly, we
introduce a novel finite state automata (FSA) which is successfully reconstructed by general systems problem solver
(GSPS). As far as we know, no studies have used the finite state machine or general system theory to estimate occupancy
in buildings. All above estimates can be used to adaptively update the temperature set-points for Heating, Ventilation,
and Air-Conditioning (HVAC) control strategy.
