The Interaction Effect of Occupant Behavior-Related Factors in Office Buildings Based on the DNAS Theory

Occupant behavior is acknowledged as a main contribution to building energy consumption. Many efforts have been devoted to identifying the impact of occupant behaviors on building energy consumption. However, the lack of understanding of the interaction effects among occupant behavior-related factors, to some extent, can lead to inaccurate results. To decode these complex interactions, this study was conducted to investigate the interaction effects of occupant behavior-related factors. A survey based on the Drive-Need-Action-System (DNAS) theory was used to describe the occupant behaviors. Then, based on the survey, a simulation model of an office building was applied for estimating the energy consumption led by different occupant behaviors. Finally, an orthogonal design of experiments (DOE) method combined with Pareto analysis was used to quantify the interactions of occupant behavior-related factors on energy consumption. Results show that factor combinations with strong interaction effects include: (1) lighting control and lighting fixture type and (2) computer control and tolerance of temperature range. The results provide important reference for building designers and facility managers toward a better understanding of the influences of occupant behaviors on building energy consumption.

[1]  Jian Kang,et al.  A stochastic model of integrating occupant behaviour into energy simulation with respect to actual energy consumption in high-rise apartment buildings , 2016 .

[2]  Kathryn B. Janda,et al.  Buildings don't use energy: people do , 2011 .

[3]  Thierry Duforestel,et al.  Energy saving and environmental resources potentials: Toward new methods of building design , 2012 .

[4]  Dirk Saelens,et al.  Energy and comfort performance of thermally activated building systems including occupant behavior , 2011 .

[5]  T. Sharpe,et al.  Occupant Interactions and Effectiveness of Natural Ventilation Strategies in Contemporary New Housing in Scotland, UK , 2015, International journal of environmental research and public health.

[6]  Kyle Konis,et al.  Evaluating daylighting effectiveness and occupant visual comfort in a side-lit open-plan office building in San Francisco, California , 2013 .

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

[8]  Ligang Liu,et al.  Correlation analysis of building plane and energy consumption of high-rise office building in cold zone of China , 2015 .

[9]  Jörn von Grabe,et al.  A preliminary cognitive model for the prediction of energy-relevant human interaction with buildings , 2017, Cognitive Systems Research.

[10]  Leonardo Bobadilla,et al.  Modeling Occupant-Building-Appliance Interaction for Energy Waste Analysis , 2016 .

[11]  Peng Gao,et al.  A hybrid decision support system for sustainable office building renovation and energy performance improvement , 2010 .

[12]  Jiabao Tang,et al.  Influence of envelope insulation materials on building energy consumption , 2017 .

[13]  Erica Cochran Hameen,et al.  Protocol for Post Occupancy Evaluation in Schools to Improve Indoor Environmental Quality and Energy Efficiency , 2020, Sustainability.

[14]  Zhengwei Li,et al.  An Empirical Study of Influencing Factors on Residential Building Energy Consumption in Qingdao City, China , 2016 .

[15]  Andrew N. Baldwin,et al.  Multi-model prediction and simulation of residential building energy in urban areas of Chongqing, South West China , 2014 .

[16]  Holly Wasilowski Samuelson,et al.  The impact of window opening and other occupant behavior on simulated energy performance in residence halls , 2017 .

[17]  J. Jokisalo,et al.  Development of weighting factors for climate variables for selecting the energy reference year according to the EN ISO 15927-4 standard , 2012 .

[18]  Enrico Fabrizio,et al.  Categories of indoor environmental quality and building energy demand for heating and cooling , 2011 .

[19]  Sebastian Herkel,et al.  Towards a model of user behaviour regarding the manual control of windows in office buildings , 2008 .

[20]  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 .

[21]  Yi-Ming Wei,et al.  China's energy consumption in the building sector: A life cycle approach , 2015 .

[22]  Marcel Schweiker,et al.  On uses of energy in buildings : Extracting influencing factors of occupant behaviour by means of a questionnaire survey , 2018 .

[23]  Yongjun Sun,et al.  Modeling energy consumption in residential buildings: A bottom-up analysis based on occupant behavior pattern clustering and stochastic simulation , 2017 .

[24]  Yi Jiang,et al.  A novel approach for building occupancy simulation , 2011 .

[25]  Zhaoxia Wang,et al.  An occupant-based energy consumption prediction model for office equipment , 2015 .

[26]  Andrea Costa,et al.  Building operation and energy performance: Monitoring, analysis and optimisation toolkit , 2013 .

[27]  Qinpeng Wang,et al.  Meta-modeling of occupancy variables and analysis of their impact on energy outcomes of office buildings , 2016 .

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

[29]  Koen Steemers,et al.  Corrigendum to “Thermal performance of a naturally ventilated building using a combined algorithm of probabilistic occupant behaviour and deterministic heat and mass balance models” [Energy Buildings 41 (2009) 489–499] , 2009 .

[30]  Tianzhen Hong,et al.  A simulation approach to estimate energy savings potential of occupant behavior measures , 2017 .

[31]  Jin Zhou,et al.  Correlation analysis of energy consumption and indoor long-term thermal environment of a residential building , 2017 .

[32]  Tianzhen Hong,et al.  A data-mining approach to discover patterns of window opening and closing behavior in offices , 2014 .

[33]  Stelios Zerefos,et al.  Examining the Impact of Daylighting and the Corresponding Lighting Controls to the Users of Office Buildings , 2020 .

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

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

[36]  Miriam A. M. Capretz,et al.  An ensemble learning framework for anomaly detection in building energy consumption , 2017 .

[37]  Christopher Meek,et al.  Lighting energy consumption in ultra-low energy buildings: Using a simulation and measurement methodology to model occupant behavior and lighting controls , 2017 .

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

[39]  S. Gauthier,et al.  Effect of Thermal, Acoustic and Air Quality Perception Interactions on the Comfort and Satisfaction of People in Office Buildings , 2021, Energies.

[40]  Jin Wen,et al.  Quantifying the human–building interaction: Considering the active, adaptive occupant in building performance simulation , 2016 .

[41]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[42]  Zhang Guoqiang,et al.  A novel methodology for identifying associations and correlations between household appliance behaviour in residential buildings , 2015 .

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

[44]  Tarek M. Hassan,et al.  Impact of occupant behaviour on the energy-saving potential of retrofit measures for a public building in the UK , 2017 .

[45]  Cao Xiang Case Analysis of Large Public Office Building Energy Saving , 2012 .

[46]  Cristina Becchio,et al.  Occupant behavior lifestyles in a residential nearly zero energy building: Effect on energy use and thermal comfort , 2016 .

[47]  Lazaros G. Papageorgiou,et al.  Efficient energy consumption and operation management in a smart building with microgrid , 2013 .

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

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

[50]  Jing Zhao,et al.  The analysis of energy consumption of a commercial building in Tianjin, China , 2009 .

[51]  Richard Hyde,et al.  Quantifying the ‘human factor’ in office building energy efficiency: a mixed-method approach , 2011 .

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

[53]  Tianzhen Hong,et al.  Advances in research and applications of energy-related occupant behavior in buildings ☆ , 2016 .

[54]  Jian Yao Prediction of Building Energy Consumption at Early Design Stage Based on Artificial Neural Network , 2010 .

[55]  Karsten Menzel,et al.  Mining building performance data for energy-efficient operation , 2011, Adv. Eng. Informatics.

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

[57]  Cristina Carpino,et al.  Energy consumption of residential buildings and occupancy profiles. A case study in Mediterranean climatic conditions , 2017 .

[58]  Ziwei Li,et al.  An ANN-based fast building energy consumption prediction method for complex architectural form at the early design stage , 2019, Building Simulation.

[59]  Kwonsik Song,et al.  Predicting hourly energy consumption in buildings using occupancy-related characteristics of end-user groups , 2017 .

[60]  Christian Inard,et al.  A new methodology for the design of low energy buildings , 2009 .

[61]  Thomas Olofsson,et al.  Overall heat loss coefficient and domestic energy gain factor for single-family buildings , 2002 .

[62]  Marcel Schweiker,et al.  The effect of occupancy on perceived control, neutral temperature, and behavioral patterns , 2016 .

[63]  Zhengdong Chen,et al.  Effect of Indoor Thermal Environment on Building Energy Consumption , 2012 .

[64]  Wei Feng,et al.  China's energy consumption in the building sector: A Statistical Yearbook-Energy Balance Sheet based splitting method , 2018, Journal of Cleaner Production.

[65]  A. Emery,et al.  A long term study of residential home heating consumption and the effect of occupant behavior on homes in the Pacific Northwest constructed according to improved thermal standards , 2006 .

[66]  J. C. Lam,et al.  Impact of climate change on building energy use in different climate zones and mitigation and adaptation implications , 2012 .

[67]  Gang Du,et al.  Life cycle analysis of energy consumption and CO2 emissions from a typical large office building in Tianjin, China , 2017 .

[68]  Pengyuan Shen,et al.  Impacts of climate change on building heating and cooling energy patterns in California , 2012 .

[69]  Jörn von Grabe,et al.  The systematic identification and organization of the context of energy-relevant human interaction with buildings—a pilot study in Germany , 2016 .