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©2019 This excerpt taken from the article of the same name which appeared in ASHRAE Journal, vol. 61, no. 2, February 2019.
By William O’Brien, Ph.D., P.Eng., Associate Member ASHRAE; Sara Gilani, Ph.D., Associate Member ASHRAE; Mohamed Ouf, Ph.D., P.Eng., Associate Member ASHRAE
About the Authors
William O’Brien, Ph.D., P.Eng., Sara Gilani, Ph.D., and Mohamed Ouf, Ph.D., P.Eng., are employed at the Human-Building Interaction Lab, Department of Civil and Environmental Engineering, Carleton University in Ottawa, ON, Canada.
As building codes become more stringent and there is a market push for higher energy performance through superior envelopes, HVAC equipment, lighting, and controls, the relative energy-related impact of occupants and their presence, habits, and actions increases. Occupants make equipment purchasing decisions; generate heat, moisture, and contaminants; adjust operable windows, window blinds, thermostats, electric lighting; and, use appliances. Their behaviors may result in a difference in energy use by a factor of two or more. Even in buildings with a high degree of automation, occupants may use buildings in ways never conceived of by the designer, or complain to the operator at the possible cost of a permanent and energy-intensive override on building controls.
Despite industry’s migration towards increasingly-sophisticated building performance simulation (aka, building energy modeling) to demonstrate code or standard compliance and to guide design, occupants are treated in very simple and generally inadequate ways. They are seldom sufficiently discussed—let alone quantified—with regards to their schedules, activities, comfort needs, and so on. Contrary to nearly every other aspect of buildings, occupants remain remarkably absent in design documentation, considering buildings are designed for people.
In pursuit of more accurate energy predictions, the last decade has seen a surge in research activity in occupant modeling for building performance simulation. In contrast to the most common occupant models (in the form of fixed schedules), the next generation of occupant models generally has four key traits: dynamic, stochastic, agent-based, and data-driven.
In brief, dynamic models recognize that occupants are not passive boundary conditions for buildings, but rather active participants. Stochasticity in this context means that occupants are not treated as robotic and perfectly consistent in nature but rather behave in a diverse and seemingly random nature. Agent-based models treat occupants as independent decision-making/action-taking beings. Data-driven means that these models are systematically and rigorously built directly from data that is collected from the field or a laboratory.
This article expands on these definitions that are inherent in the next generation of occupant models. It is the first of a three-part series of articles that aims to inform the building design and energy modeling community about the latest research developments in occupant modeling. Part 2 of this series will present a detailed office design optimization study to illustrate the additional benefits of detailed occupant modeling. Part 3 will conclude with our recommendations on occupant modeling for future building energy codes and standards development as well as design practice.
Background
The ever-growing field of occupant modeling and simulation now includes hundreds of researchers, as evidenced by the recently-concluded International Energy Agency-Energy in Buildings and Communities (IEA-EBC) Annex 66, “Definition and simulation of occupant behavior in buildings”6 and its follow-up Annex 79, “Occupant-centric building design and operation.”
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