An agent-based model of building occupant behavior during load shedding

Load shedding enjoys increasing popularity as a way to reduce power consumption in buildings during hours of peak demand on the electricity grid. This practice has well known cost saving and reliability benefits for the grid, and the contracts utilities sign with their “interruptible” customers often pass on substantial electricity cost savings to participants. Less well-studied are the impacts of load shedding on building occupants, hence this study investigates those impacts on occupant comfort and adaptive behaviors. It documents experience in two office buildings located near Philadelphia (USA) that vary in terms of controllability and the set of adaptive actions available to occupants. An agent-based model (ABM) framework generalizes the case-study insights in a “what-if” format to support operational decision making by building managers and tenants. The framework, implemented in EnergyPlus and NetLogo, simulates occupants that have heterogeneous thermal and lighting preferences. The simulated occupants pursue local adaptive actions such as adjusting clothing or using portable fans when central building controls are not responsive, and experience organizational constraints, including a corporate dress code and miscommunication with building managers. The model predicts occupant decisions to act fairly well but has limited ability to predict which specific adaptive actions occupants will select.

[1]  Uri Wilensky,et al.  NetLogo: A simple environment for modeling complexity , 2014 .

[2]  Junying Chu,et al.  Agent-Based Residential Water Use Behavior Simulation and Policy Implications: A Case-Study in Beijing City , 2009 .

[3]  K. Steemers,et al.  Time-dependent occupant behaviour models of window control in summer , 2008 .

[4]  Jin Wen,et al.  Simulating the human-building interaction: Development and validation of an agent-based model of office occupant behaviors , 2015 .

[5]  Jinlong Ouyang,et al.  Energy-saving potential by improving occupants’ behavior in urban residential sector in Hangzhou City, China , 2009 .

[6]  John E. Taylor,et al.  Modeling building occupant network energy consumption decision-making: The interplay between network structure and conservation , 2012 .

[7]  O. T. Masoso,et al.  The dark side of occupants’ behaviour on building energy use , 2010 .

[8]  Elie Azar,et al.  A comprehensive analysis of the impact of occupancy parameters in energy simulation of office buildings , 2012 .

[9]  Stéphane Ploix,et al.  Agent based Framework to Simulate Inhabitants' Behaviour in Domestic Settings for Energy Management , 2011, ICAART.

[10]  Tianzhen Hong,et al.  An ontology to represent energy-related occupant behavior in buildings. Part II: Implementation of the DNAS framework using an XML schema , 2015 .

[11]  Guy R. Newsham,et al.  The effect of utility time-varying pricing and load control strategies on residential summer peak electricity use: A review , 2010 .

[12]  Ardeshir Mahdavi,et al.  TOWARD EMPIRICALLY-BASED MODELS OF PEOPLE ' S PRESENCE AND ACTIONS IN BUILDINGS , 2009 .

[13]  John E. Taylor,et al.  Effects of real-time eco-feedback and organizational network dynamics on energy efficient behavior in commercial buildings , 2014 .

[14]  Ali Malkawi,et al.  Simulating multiple occupant behaviors in buildings: An agent-based modeling approach , 2014 .

[15]  Darren Robinson,et al.  Interactions with window openings by office occupants , 2009 .

[16]  Tianzhen Hong,et al.  Occupant behavior modeling for building performance simulation: Current state and future challenges , 2015 .

[17]  Jin Wen,et al.  Review of building energy modeling for control and operation , 2014 .

[18]  Clinton J. Andrews,et al.  An Agent Based Model of Household Water Use , 2013 .

[19]  Hak-Man Kim,et al.  Distributed Load-Shedding System for Agent-Based Autonomous Microgrid Operations , 2014 .

[20]  Joseph Andrew Clarke,et al.  Using results from field surveys to predict the effect of open windows on thermal comfort and energy use in buildings , 2007 .

[21]  Jennifer A. Senick,et al.  WHY ENERGY-SAVING MEASURES IN COMMERCIAL OFFICE BUILDINGS FAIL: DEEP VERSUS SHALLOW USE STRUCTURES , 2015 .

[22]  Pieter de Wilde,et al.  The gap between predicted and measured energy performance of buildings: A framework for investigation , 2014 .

[23]  Clinton J. Andrews,et al.  Designing Buildings for Real Occupants: An Agent-Based Approach , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[24]  J. F. Nicol Characterising occupant behaviour in buildings : towards a stochastic model of occupant use of windows, lights, blinds, heaters and fans , 2001 .

[25]  R. Faranda,et al.  Load Shedding: A New Proposal , 2007, IEEE Transactions on Power Systems.

[26]  Y. S. Haruna,et al.  Agent-based modeling and simulation of competitive electric power markets , 2014, 2014 Clemson University Power Systems Conference.

[27]  Elie Azar,et al.  Agent-Based Modeling of Occupants and Their Impact on Energy Use in Commercial Buildings , 2012, J. Comput. Civ. Eng..

[28]  P Pieter-Jan Hoes,et al.  User behavior in whole building simulation , 2009 .

[29]  A. Capozza,et al.  Load shedding and demand side management enhancements to improve the security of a national electrical system , 2005, 2005 IEEE Russia Power Tech.

[30]  R. Andersen,et al.  Occupant performance and building energy consumption with different philosophies of determining acceptable thermal conditions , 2009 .

[31]  Fu Xiao,et al.  Peak load shifting control using different cold thermal energy storage facilities in commercial buildings: A review , 2013 .

[32]  Michael J. North,et al.  Tutorial on agent-based modelling and simulation , 2005, Proceedings of the Winter Simulation Conference, 2005..

[33]  H. Burak Gunay,et al.  The contextual factors contributing to occupants' adaptive comfort behaviors in offices – A review and proposed modeling framework , 2014 .

[34]  P. Cappers,et al.  Demand Response in U.S. Electricity Markets: Empirical Evidence , 2010 .

[35]  Ernest Orlando Lawrence,et al.  An Ontology to Represent Energy- related Occupant Behavior in Buildings Part I: Introduction to the DNAs Framework , 2015 .

[36]  Darren Robinson,et al.  Multi agent simulation of occupants' presence and behaviour , 2011 .

[37]  Tianzhen Hong,et al.  Simulation of occupancy in buildings , 2015 .