A probabilistic-based approach to support the comfort performance assessment of existing buildings

Building performance is focused on maintaining and increasing sustainability while enhancing occupants' comfort levels. Occupants' comfort depends on uncertain, interrelated personal, social and building factors. However, the relationships between these factors are not covered by performance assessment tools based on linear analysis approaches. This study proposes a probabilistic model based on Bayesian networks (BNs) to assess the comfort performance of a building. An extensive review and evaluation of the causal factors of building performance, supported by the results of a satisfaction survey, are the basis for the BN model. Sensitivity analysis is used to verify the BN model, and the proposed approach is tested on an existing building. Findings from this research can guide decision-makers in the facility management industry to assess and understand causal factors of occupants’ comfort to properly establish sustainable and cleaner production strategies.

[1]  Ying Hua,et al.  Spatial mapping of occupant satisfaction and indoor environment quality in a LEED platinum campus building , 2014 .

[2]  Taehoon Hong,et al.  Establishment of an optimal occupant behavior considering the energy consumption and indoor environmental quality by region , 2017 .

[3]  Gail Brager,et al.  Post-occupancy evaluation: State-of-the-art analysis and state-of-the-practice review , 2018 .

[4]  Staf Roels,et al.  Probabilistic design and analysis of building performances: methodology and application example , 2014 .

[5]  Halil Zafer Alibaba,et al.  Determination of Optimum Window to External Wall Ratio for Offices in a Hot and Humid Climate , 2016 .

[6]  Mohammad S. Al-Homoud,et al.  Performance characteristics and practical applications of common building thermal insulation materials , 2005 .

[7]  Philomena M. Bluyssen,et al.  Comfort of workers in office buildings: The European HOPE project , 2010 .

[8]  Adrian Leaman,et al.  Are users more tolerant of ‘green’ buildings? , 2007 .

[9]  Judea Pearl,et al.  A Theory of Inferred Causation , 1991, KR.

[10]  Yu Wang,et al.  HVAC system design under peak load prediction uncertainty using multiple-criterion decision making technique , 2015 .

[11]  Ron S. Kenett,et al.  Bayesian networks of customer satisfaction survey data , 2009 .

[12]  Hasanuddin Lamit,et al.  User satisfaction adaptive behaviors for assessing energy efficient building indoor cooling and lighting environment , 2014 .

[13]  Andreas Wagner,et al.  Thermal comfort and workplace occupant satisfaction—Results of field studies in German low energy office buildings , 2007 .

[14]  George Wright,et al.  The Delphi technique as a forecasting tool: issues and analysis , 1999 .

[15]  Osman Balci,et al.  Verification, Validation And Accreditation Of Simulation Models , 1997, Winter Simulation Conference Proceedings,.

[16]  Lin Ma,et al.  A Bayesian Network-based customer satisfaction model: a tool for management decisions in railway transport , 2016, Decis. Anal..

[17]  Tiberiu Catalina,et al.  IEQ assessment on schools in the design stage , 2012 .

[18]  Avner Engel.  Verification, Validation, and Testing of Engineered Systems: Engel/Verification , 2010 .

[19]  Anca D. Galasiu,et al.  Occupant preferences and satisfaction with the luminous environment and control systems in daylit offices: a literature review , 2006 .

[20]  Qinpeng Wang,et al.  Uncertainty analysis of thermal comfort in a prototypical naturally ventilated office building and its implications compared to deterministic simulation , 2017 .

[21]  Carol C. Menassa,et al.  A framework to assess the role of stakeholders in sustainable building retrofit decisions , 2014 .

[22]  Elena Pérez-Miñana,et al.  Improving ecosystem services modelling: Insights from a Bayesian network tools review , 2016, Environ. Model. Softw..

[23]  Jacqueline C. Vischer,et al.  Towards an Environmental Psychology of Workspace: How People are Affected by Environments for Work , 2008 .

[24]  A. Raouf,et al.  Managing Maintenance Errors: A Practical Guide , 2005 .

[25]  Yingxin Zhu,et al.  A review of operating performance in green buildings: Energy use, indoor environmental quality and occupant satisfaction , 2019, Energy and Buildings.

[26]  Serena H. Chen,et al.  Good practice in Bayesian network modelling , 2012, Environ. Model. Softw..

[27]  Andreas Wagner,et al.  Personal control over indoor climate in office buildings in a Mediterranean climate - Amman, Jordan , 2018 .

[28]  Ali Reza Seifi,et al.  Probabilistic energy consumption analysis in buildings using point estimate method , 2018 .

[29]  P. Wargocki,et al.  Literature survey on how different factors influence human comfort in indoor environments , 2011 .

[30]  Philomena M. Bluyssen,et al.  Towards new methods and ways to create healthy and comfortable buildings , 2010 .

[31]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[32]  Adrian Leaman,et al.  Assessing building performance in use 4: the Probe occupant surveys and their implications , 2001 .

[33]  Rehan Sadiq,et al.  Improving the energy efficiency of the existing building stock: A critical review of commercial and institutional buildings , 2016 .

[34]  M Frontczak,et al.  Quantitative relationships between occupant satisfaction and satisfaction aspects of indoor environmental quality and building design. , 2012, Indoor air.

[35]  Edward Arens,et al.  Acoustical quality in office workstations, as assessed by occupant surveys , 2005 .

[36]  Richard de Dear,et al.  Impact of different building ventilation modes on occupant expectations of the main IEQ factors , 2012 .

[37]  Rodrigo Escobar,et al.  Thermal and lighting behavior of office buildings in Santiago of Chile , 2012 .

[38]  Robert G. Sargent,et al.  Verification and validation of simulation models , 2013, Proceedings of Winter Simulation Conference.

[39]  Sara Wilkinson,et al.  Using building adaptation to deliver sustainability in Australia , 2009 .

[40]  M. Reed,et al.  Integrating Methods for Developing Sustainability Indicators to Facilitate Learning and Action , 2005 .

[41]  Azlan Shah Ali,et al.  Improving occupants' satisfaction with effective maintenance management of HVAC system in office buildings , 2014 .

[42]  Norman Fenton,et al.  Risk Assessment and Decision Analysis with Bayesian Networks , 2012 .

[43]  Mohamed Al-Hussein,et al.  Monitoring building energy consumption, thermal performance, and indoor air quality in a cold climate region , 2014 .

[44]  Rafaela Bortolini,et al.  Facility managers’ perceptions on building performance assessment , 2018 .

[45]  Jin Wen,et al.  Modeling thermal comfort holistically: Bayesian estimation of thermal sensation, acceptability, and preference distributions for office building occupants , 2013 .

[46]  Robert F. Boehm,et al.  Passive building energy savings: A review of building envelope components , 2011 .

[47]  Ilknur Turkseven Dogrusoy,et al.  A field study on determination of preferences for windows in office environments , 2007 .

[48]  Andrius Keizikas,et al.  The relationship between the shape of a building and its energy performance , 2012 .

[49]  F. Bauman,et al.  Comparing temperature and acoustic satisfaction in 60 radiant and all-air buildings , 2017 .

[50]  Frederik Auffenberg,et al.  A Personalised Thermal Comfort Model Using a Bayesian Network , 2015, IJCAI.

[51]  Olalekan Shamsideen Oshodi,et al.  Educational building conditions and the health of users , 2016 .

[52]  J. Pearl.  Causality: Models, Reasoning and Inference , 2000 .

[53]  José Manuel Cejudo López,et al.  Uncertainties and sensitivity analysis in building energy simulation using macroparameters , 2013 .

[54]  Rafaela Bortolini,et al.  Building Inspection System for Evaluating the Technical Performance of Existing Buildings , 2018, Journal of Performance of Constructed Facilities.

[55]  Runa Tabea Hellwig,et al.  Perceived control in indoor environments: a conceptual approach , 2015 .

[56]  J. Rostron,et al.  Sick building syndrome: A review of causes, consequences and remedies , 2008 .

[57]  Abbas Elmualim,et al.  Post-occupancy studies of an office environment: Energy performance and occupants’ satisfaction , 2013 .

[58]  E. de Oliveira Fernandes,et al.  Perceived health and comfort in relation to energy use and building characteristics , 2006 .

[59]  Wolfgang F. E. Preiser,et al.  Assessing building performance , 2005 .

[60]  Chiara Aghemo,et al.  Building automation and control systems: A case study to evaluate the energy and environmental performances of a lighting control system in offices , 2014 .

[61]  N. A. Oseland,et al.  A comparison of the predicted and reported thermal sensation vote in homes during winter and summer , 1994 .