From occupancy to occupant behavior: An analytical survey of data acquisition technologies, modeling methodologies and simulation coupling mechanisms for building energy efficiency

Energy consumption and indoor environment of buildings are proved to be largely influenced by the presence and behaviors of occupants. The uncertainty caused by occupant behaviors accounts for a significant discrepancy between the predicted and actual energy usage. In a real world, building system operations and control will be directly affected by occupant behavior, which may lead to over thirty percent waste against building's designed performance. Therefore, the capability to seamlessly integrate occupant behavior in energy simulation tools and building management systems in the future is clearly important to optimize building energy use while maintaining the same level of services. However, research has not reached the phase that occupant behaviors could be effectively modeled. Thus, the traditional schedule based approach is not adequate to satisfy the needs of building efficiency. In this paper, a thorough survey of occupant behavior modeling and simulation state-of-the-art technologies and methodologies for building energy efficiency is conducted. The paper first identifies and discusses the significance and application scale of building occupant behavior model. Based on the information collected, some recent data acquisition technologies for behavior-related research and occupant behavior modeling approaches are summarized. The advantages and limitations of these modeling methods are compared and analyzed, as well as appropriate recommendations are made for the future research. The paper finally outlines the findings and potential development areas in the field of occupant behavior modeling for energy efficient buildings.

[1]  Stéphane Ploix,et al.  User Behavior Prediction in Energy Consumption in Housing Using Bayesian Networks , 2010, ICAISC.

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

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

[4]  Xiao-Ping Zhang,et al.  Real-time Energy Control Approach for Smart Home Energy Management System , 2014 .

[5]  Miguel Á. Carreira-Perpiñán,et al.  OBSERVE: Occupancy-based system for efficient reduction of HVAC energy , 2011, Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks.

[6]  Nan Li,et al.  Probability of occupant operation of windows during transition seasons in office buildings , 2015 .

[7]  Mark Luther,et al.  Energy efficient envelope design for high-rise apartments , 2005 .

[8]  Bing Dong,et al.  Building energy and comfort management through occupant behaviour pattern detection based on a large-scale environmental sensor network , 2011 .

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

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

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

[12]  Nursyarizal Mohd Nor,et al.  A review on optimized control systems for building energy and comfort management of smart sustainable buildings , 2014 .

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

[14]  Francesco Causone,et al.  Light switch behaviour: occupant behaviour stochastic models in office buildings , 2014 .

[15]  Tuan Anh Nguyen,et al.  Energy intelligent buildings based on user activity: A survey , 2013 .

[16]  Darren Robinson,et al.  A generalised stochastic model for the simulation of occupant presence , 2008 .

[17]  Tianzhen Hong,et al.  An ontology to represent energy-related occupant behavior in buildings. Part I: Introduction to the DNAs framework , 2015 .

[18]  Benjamin C. M. Fung,et al.  A methodology for identifying and improving occupant behavior in residential buildings , 2011 .

[19]  Jie Zhao,et al.  Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining , 2014 .

[20]  Tianzhen Hong,et al.  Data mining of space heating system performance in affordable housing , 2015, Building and Environment.

[21]  Gregor P. Henze,et al.  Stochastic control optimization for a mixed mode building considering occupant window opening behaviour , 2014 .

[22]  Bing Dong,et al.  A real-time model predictive control for building heating and cooling systems based on the occupancy behavior pattern detection and local weather forecasting , 2013, Building Simulation.

[23]  Henk Visscher,et al.  The effect of occupancy and building characteristics on energy use for space and water heating in Dutch residential stock , 2009 .

[24]  Tina Yu,et al.  Modeling Occupancy Behavior for Energy Efficiency and Occupants Comfort Management in Intelligent Buildings , 2010, 2010 Ninth International Conference on Machine Learning and Applications.

[25]  Veronica Soebarto,et al.  House energy rating schemes and low energy dwellings: the impact of occupant behaviours in Australia , 2015 .

[26]  Bjarne W. Olesen,et al.  Window opening behaviour modelled from measurements in Danish dwellings , 2013 .

[27]  Valentina Fabi,et al.  Effect of thermostat and window opening occupant behavior models on energy use in homes , 2014 .

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

[29]  Zheng Yang,et al.  Modeling personalized occupancy profiles for representing long term patterns by using ambient context , 2014 .

[30]  Stéphanie Minel,et al.  A Stochastic Activity-Based Approach for Forecasting Occupant-Related Energy Consumption in Residential Buildings , 2014 .

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

[32]  Rui Zhang,et al.  Occupancy detection through an extensive environmental sensor network in an open-plan office building , 2009 .

[33]  Bing Dong,et al.  The Impact of Occupancy Behavior on Energy Consumption in Low Income Residential Buildings , 2014 .

[34]  Tianzhen Hong,et al.  Occupancy schedules learning process through a data mining framework , 2015 .

[35]  Gerhard Zimmerman Modeling and simulation of individual user behavior for building performance predictions , 2007, SCSC.

[36]  Thomas Weng,et al.  Occupancy-driven energy management for smart building automation , 2010, BuildSys '10.

[37]  Astrid Roetzel,et al.  Occupant behaviour simulation for cellular offices in early design stages—Architectural and modelling considerations , 2014, Building Simulation.

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

[39]  Chenda Liao,et al.  An integrated approach to occupancy modeling and estimation in commercial buildings , 2010, Proceedings of the 2010 American Control Conference.

[40]  Rui Prada,et al.  Accurate Household Occupant Behavior Modeling Based on Data Mining Techniques , 2014, AAAI.

[41]  Pei Xu,et al.  Extracting Human Behavior Patterns from Appliance-level Power Consumption Data , 2015, EWSN.

[42]  Leonardo Bobadilla,et al.  Modeling and analyzing occupant behaviors in building energy analysis using an information space approach , 2015, 2015 IEEE International Conference on Automation Science and Engineering (CASE).

[43]  Prabir Barooah,et al.  Agent-based and graphical modelling of building occupancy , 2012 .

[44]  Abbas Elmualim Influencing energy efficient occupant behavior through improved building and control design , 2012 .

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

[46]  Kaan Ozbay,et al.  Guidelines for Life Cycle Cost Analysis , 2003 .

[47]  Rory V. Jones,et al.  Driving factors for occupant-controlled space heating in residential buildings , 2014 .

[48]  Benjamin C. M. Fung,et al.  A systematic procedure to study the influence of occupant behavior on building energy consumption , 2011 .

[49]  Darren Robinson,et al.  The impact of occupants' behaviour on building energy demand , 2011 .

[50]  Jaume Salom,et al.  Proceedings of the 12th Conferenece of The International Building Performance Simulation Association , 2011 .

[51]  Bing Dong,et al.  Sensor-based occupancy behavioral pattern recognition for energy and comfort management in intelligent buildings , 2009 .

[52]  Rune Vinther Andersen,et al.  Description of occupant behaviour in building energy simulation: state-of-art and concepts for their improvement , 2011 .

[53]  Da Yan,et al.  Quantitative description and simulation of human behavior in residential buildings , 2012 .

[54]  Stéphanie Minel,et al.  An Occupant-Based Energy Consumption Model for User-Focused Design of Residential Buildings , 2015 .

[55]  Zhenghua Chen,et al.  Modeling regular occupancy in commercial buildings using stochastic models , 2015 .

[56]  Emily M. Ryan,et al.  Validation of building energy modeling tools under idealized and realistic conditions , 2012 .

[57]  Rita Streblow,et al.  CO2 based occupancy detection algorithm: Experimental analysis and validation for office and residential buildings , 2015 .

[58]  Ian Beausoleil-Morrison,et al.  A critical review of observation studies, modeling, and simulation of adaptive occupant behaviors in offices , 2013 .

[59]  Burcin Becerik-Gerber,et al.  A systematic approach to occupancy modeling in ambient sensor-rich buildings , 2014, Simul..

[60]  Shuhui Li,et al.  Developing smart and real-time demand response mechanism for residential energy consumers , 2014, 2014 Clemson University Power Systems Conference.

[61]  Changbum R. Ahn,et al.  Assessing occupants’ energy load variation through existing wireless network infrastructure in commercial and educational buildings , 2014 .

[62]  Rui Neves-Silva,et al.  Stochastic models for building energy prediction based on occupant behavior assessment , 2012 .

[63]  Anna Laura Pisello,et al.  Influence of human behavior on cool roof effect for summer cooling , 2015 .

[64]  Alberto E. Cerpa,et al.  Energy efficient building environment control strategies using real-time occupancy measurements , 2009, BuildSys '09.

[65]  Darren Robinson,et al.  On the behaviour and adaptation of office occupants , 2008 .

[66]  Kartik Palani,et al.  Fusing Sensors for Occupancy Sensing in Smart Buildings , 2015, ICDCIT.

[67]  Ben Croxford,et al.  Using agent-based modelling to simulate occupants' behaviours in response to summer overheating , 2014, ANSS 2014.

[68]  Steve Greenberg,et al.  Window operation and impacts on building energy consumption , 2015 .

[69]  Ardeshir Mahdavi,et al.  Predicting people's presence in buildings: An empirically based model performance analysis , 2015 .

[70]  Nassim Masoudifar,et al.  Monitoring occupancy and office equipment energy consumption using real-time location system and wireless energy meters , 2014, Proceedings of the Winter Simulation Conference 2014.

[71]  Rune Vinther Andersen,et al.  Occupants' behaviour in office building: stochastic models for window opening , 2014 .

[72]  Thomas Bednar,et al.  Validation and evaluation of total energy use in office buildings: A case study , 2012 .

[73]  Burcin Becerik-Gerber,et al.  Understanding the Influence of Occupant Behavior on Energy Consumption Patterns in Commercial Buildings , 2012 .

[74]  Manfred Morari,et al.  Use of model predictive control and weather forecasts for energy efficient building climate control , 2012 .

[75]  Bauke de Vries,et al.  Methods for the prediction of intermediate activities by office occupants , 2010 .

[76]  Arno Schlueter,et al.  Occupant centered lighting control for comfort and energy efficient building operation , 2015 .

[77]  Milind Tambe,et al.  Coordinating occupant behavior for building energy and comfort management using multi-agent systems , 2012 .