Simulating occupancy in office buildings with non-homogeneous Markov chains for Demand Response analysis

Demand Response (DR) is a promising solution to deal with supply/demand imbalances in the power systems. To appreciate the availability of DR, it is important to include end user behavior in the analysis. In this paper, a model that can generate representative occupancy profiles in single office rooms is presented. The used method is non-homogeneous Markov chain modeling, along with exploratory data analysis of occupancy data, and an estimation of occupancy levels for different months and weekdays. The Markov chain model is calibrated with occupancy sensor data from 24 single rooms collected from an office building floor in Sweden. The simulation results have been statistically compared with a test data set consisting of occupancy data in 23 other rooms on the same floor. It was shown that the model could reproduce key occupancy properties of the test data.