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

Abstract

Abstract-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.

Comprehensive energy, economic and thermal comfort assessments for the passive energy retrofit of historical buildings - A case study of a late nineteenth-century Victorian house renovation in the UK

“…The occupancy profile for a typical academic office building [44][45][46] is assumed and applied to conduct the simulation. As shown in Fig.…”

Occupancy Prediction Using Differential Evolution Online Sequential Extreme Learning Machine Model

“…Most studies related to the measurement and modelling of occupancy patterns usually focus on the simulation of occupant presence and behaviour, like the time the user starts working, finishes work, goes to lunch, and returns in the afternoon [24][25][26][27][28]. Some studies presented algorithms for occupancy number detection based on the analysis of environmental data (i.e., CO 2 , CO, total volatile organic compounds (TVOC), relative humidity (RH), outside temperature, dew point, small particulates, motion, acoustics) captured from sensors [29][30][31][32][33]. Most often, studies combine the approaches of environmental data measurements, energy consumption data and the observations of occupants' presence [34,35].…”

Literature Review for Sensor Impact Evaluation and Verification Use Cases - Building Controls and Fault Detection and Diagnosis (FDD)

“…Typically, occupancy sensors work in tandem with the temperature and airflow sensors in the HVAC system to control the cooling or heating energy delivered and amount of fresh air brought into the building based on the occupancy status. In research by Sandels et al [111], occupancy sensor data was used to develop Hidden Markov Models that simulate end-user behavior, which was further used to devise demand response strategies.…”