Computational Assessment of the Impact of Occupant-Related Assumptions on the Thermal Performance of an Office Building

Federica Soncini , Sara Torabi Moghadam

Computational Assessment of the Impact of Occupant-Related Assumptions on the Thermal Performance of an Office Building.

Rel. Stefano Paolo Corgnati, Valentina Fabi, Ardeshir Mahdavi , Farhang Tahmasebi . Politecnico di Torino, Corso di laurea magistrale in Architettura Per Il Progetto Sostenibile, 2014

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Abstract:	With the decline of fossil energy resources, the necessity to change our way of energy usage is apparent. Building sector, indeed, account for 40% of the total energy consumption in EU member states (EC, 2004). Consequently, in recent years, attention has focused increasingly towards the realization of sustainable buildings with the aim of reducing thè global energy consumption and environmental impacts of thè construction sector. Therefore, building performance simulation tools have been used to help designers achieve their goals in designing energy-efficient buildings. However, buildings' actual energy performance frequently does not meet thè expectations at thè design phase. One of the potential reasons for thè discrepancy between expected and actual energy performance are the uncertainties associated with building occupants' presence and behavior, as shown in previous literatures. Existing dynamic energy simulation tools, developed in thè last decade, exceed thè static size of thè simplified methods through a better and more accurate prediction of energy use; however, they are stili unable to replicate the actual dynamics that govern energy uses within buildings. Energy performance is dramatically affected by thè behavior of users inside buildings: each architectural project is proposed on thè base of a singular spatial organization and occupier’s usability, but once thè building-plant system has been realized, this could be used differently from the assumptions made in the design phase. Moreover, the use of imprecise and standardized occupant behavior can blur the effect of other energy saving solutions. The inappropriateness of deterministic assumptions about occupants’ interactions with building's Controls is proved by the field evidence: despite on-going improvements in the building envelope thermal performances, energy demand has not decreased in the expected way. Moreover, recent studies compare deterministic simulation results to the simulation results where stochastic models are implemented, showing a significant energy use increase when switching from one approach to another [(Fabi et ai, 2012), (D’Oca, 2012), (Buso, 2012)]. For these reasons, a stochastic simulation of the designed edifice, in which statistical methods can be used to generate individual occupancy patterns, indeed of fixed schedules, is needed. Several investigations have been conducted inliterature in order to comprehend thè most influencing drivers related to occupant behavior in an office building. Accordingly to previous researches (Annex 53, ECBCS), is possible to highlight that users principally restare their comfort condition by opening and closing windows. It is therefore important to take window opening behavior into account, when designing energy efficient buildings. Given this background, the main purpose of thè presented research is to investigate the implications of different assumptions with regard to window operation in a mechanically and naturally ventilated office building for thè energy use and located in different locations, i.e. Turin and Athens, with the aim to evaluate the influence of climate on the predicted energy consumption. Toward this end, a dynamic numeric simulation application, which necessarily takes into account thè possibility that an action is carried out through statistical input parameters, was deployed to simulate the building thermal performance. This new method defines behavioral models by using an algorithm based on a statistical data set built on monitoring occupants’ real interaction within the building-plant system, published by Haldi and Robinson (2009). Then, an individual behavioral model approach, taken from Parys’ (2013) PhD thesis, was developed by implementing the logistic regression models for ac live and passive users with the purpose to compare an aggregated behavioral model to a single one, where the differences between users are displayed. The method of the research developed in this master thesis is based on the assumption that only switching from an automated approach to a manual one, it will be feasible to obtain energy consumption prediction closer to reality, already in the design phase.
Relatori:	Stefano Paolo Corgnati, Valentina Fabi, Ardeshir Mahdavi , Farhang Tahmasebi
Tipo di pubblicazione:	A stampa
Soggetti:	A Architettura > AJ Edifici e attrezzature per l'amministrazione, il commercio e la difesa S Scienze e Scienze Applicate > SG Fisica SS Scienze Sociali ed economiche > SSF Scienze sociali
Corso di laurea:	Corso di laurea magistrale in Architettura Per Il Progetto Sostenibile
Classe di laurea:	NON SPECIFICATO
Aziende collaboratrici:	NON SPECIFICATO
URI:	http://webthesis.biblio.polito.it/id/eprint/3630
Capitoli:	TITLE SHEET ACKNOWLEDGEMENT ABSTRACT SOMMARIO LIST OF FIGURES LIST OF TABLES INTRODUCTORY CHAPTERS... CHAPTER loverview & Problem Statement 1.1 Introduction 1.1.1 Sustainability in Energy and Building 1.1.2 Energy Efficiency Policies in thè EU 1.2 Motivation 1.3 Background 1.3.1 Occupant Behavior 1.4 Research Objectives 1.5 Thesis Outline PARTI RESEARCH APPLICATION CHAPTER 2_Reference Office Building 2.1 Introduction 2.2 Model Description 2.2.1 Envelope’s Characteristic 2.2.2 Thermal Zoning 2.3 Internal Boundary Conditions 2.4 External Boundary Conditions: Weather CHAPTER 3 Modelling Tools 3.1 Introduction 3.2 Building Performance Simulation 3.3 Dynamic Simulation Software IDA Ice 3.4 Uncertainty Analysis in Building Simulation CHAPTER 4_Modelling Approach 4.1 Introduction 4.2 Methodology 4.3 Occupant Control of Windows 4.3.1 Literature Review 4.3.2 Implemented Model 4.5 Building Model 4.5.1 Fully-automatic VS Manual Approach PARTII RESULTS CHAPTER 5_Uncertainty Analysis of Occupant Behavior: thè Climate of Turin 5.1 Simulations Performed for thè Climate of Turin 5.2 Building Level-Based Approaches 5.2.1 Annual Analyses in Turin’s Climate 5.2.2 Monthly Analysis in Turin’s Climate 5.3 Zone Level-Based Approaches 5.3.1 Annual Analysis in Turin’s Climate 5.4 Discussion CHAPTER 6_EvaIuating thè Influence of Occupant Behavior in Different Climates: Comparison Turin and Athens’s Results 6.1 The Reference Office Building in Different Climates 6.2 Building Level-Based Approaches 6.2.1 Annual Analysis in Athens’ Climate 6.2.2 Monthly Analysis in Athens’ Climate 6.3 Zone Level-Based Approaches 6.3.1 Annual Analysis in Athens’ Climate 6.4 Discussion CHAPTER 7_Active & Passive Users for Dynamic Building Simulation 7.1 Development of Individual Behavioral Model 7.2 Building Level-Based Approach 7.2.1 Annual Analyses in Turin’s Climate 7.3 Discussion CONCLUD1NG CHAPTERS CHAPTER 8_Conclusions & Recommendations... 8.1 Introduction 8.2 Comparison with Literature 8.3 Recommendation 8.3.1. Prospective for Future Research 8.4 Conclusions PAPER I PAPER II PAPER III LIST OF REFERENCES APPENDICES
Bibliografia:	• Abushakra, B., Sreshtaputra, A., Haberl, J.S., Claridge, D.E. 2001. Compilation of Diversity Factors and Schedules for Energy and Cooling Load Calculations, ASHRAE research project 1093. • Bedford, T., Warner, C.G., Chrenko, F. A. 1943. Observations on thè Natural Ventilation of Dwellings, Journal of thè royal institute of British architects. • Bordass, B. 1999. Productivity in Buildings: thè “Killer” Variables, Building Research and Information 27, pp. 4-20. • Bordass, B., Bromley, K., Leaman, A. February, 1993. User and Occupant Controls in Office Buildings, Proceedings of thè conference: Building Design, Technology and Occupant Well-being in Temperate Climates, Brussels, BE. • Bordass, B., Cohen, R., and Field, J. 2004. Energy Performance of Non-Domestic Buidings. Closing thè Credibility Gap, Building Performance Congress. • Bourgeois, D. 2005. Detailed Occupant Prediction, Occupancy-Sensing Control and Advanced Behavioural Modeling within Whole-Building Energy Simulation, Ph.D Thesis, Faculté des Etudes Supérieures Université Lavai Québec. • Brager, G.S., de Dear, R. 1998. Developing an Adaptive Model of Thermal Comfort and Preference, ASHRAE Transactions Vol. 104 (I), pp. 145-167. • Buso, T. 2012. Robustness of Building Design with Respect to Occupant Behavior, Master Thesis. • Clarke, J.A. 2001. Energy Simulation in Building Design, Second Edition, Butterworth- Heinemann, Oxford. • Corgnati, S.P., Fabrizio, E., Filippi, M. 2008. The Impact of Indoor Thermal Conditions, System Controls and Building Types on thè Building Energy Demand, Energy and Buildings 40, pp. 627-636. • Corgnati, S.P., Fabrizio, E.,Raimondo, D., Filippi, M. June 2011. Categories of Indoor Environmental Quality and Building Energy Demand for Heating and Cooling, Building simulation 2. • Crawley, D. B. 2003. Presentation at ASHRAE Meeting, Chicago, USA. • D’Oca, S. 2012. Influence of Occupants’ Behavior on Heating Energy Consumption and Thermal Comfort in Residential Buildings, Master Thesis. • De Wilde, J. 2004. Computational support for thè selection of energy saving building components, DUP Science, Delft, Netherlands. • De Wit, M.S. 2001. Uncertainty in Predictions of Thermal Comfort in Buildings, Ph.D. thesis. • EC 2004. European Union Energy and Transport in Figures, 2006 ed. Part 2, Energy, Directorate General for Energy and Transport, European Commission, Brussels. • EN 15251:2006. • Fabi, V., Andersen, R.V., Corgnati, S.P., Olesen, B.W., Filippi, M. November, 2011. Description of Occupant Behavior in Building Energy Simulation: State of Art and Concepts for their Improvement, Proceedings of Conference: Building Simulation, Sydney, Australia. • Fabi, V., Andersen, V.R., Corgnati, S.P. 12-15 Aprii, 2012. Window Opening Behavior: Simulations of Occupant Behavior in Residential Buildings Using Models Based on a Field Survey, Proceedings of thè 7th Windsor Conference: The changing context of comfort in an unpredictable world, Windsor, UK. • Fritsch, R., Kohler, A., Nygàrd-Ferguson, M., Scartezzini, JL. 1990. A Stochastic Model of User Behavior Regarding Ventilation, Building and Environment 25, pp. 173- 81. • Fujii, H. and Lutzenhiser, L. 1992. Japanese Residential Air-Conditioning: Natural Cooling and Intelligent Systems, Energy and Buildings 18, pp. 221-33. • Gail, S., Brager, G.S., Paliaga, G., de Dear, R. 2004. Operable Windows, Personal Control, and Occupant Comfort, Ashrae Transaction 2004, Vol. 110, Part 2. • Guerra Santin, O. 2011. Behavioural Patterns and Users Profiles Related to Energy Consumption for Heating, Energy and Building 43, pp. 2662-2672. • Haldi, F. and Robinson, D. 2008. On thè Behaviour and Adaptation of Office Occupants, Building and Environment 43, pp. 2163-2177. • Haldi, F. and Robinson, D. 2010. Adaptive Actions on Shading Devices in Response to Local Visual Stimuli, Journal of Building Performance Simulation 3, pp. 135—53. • Haldi, F., Robinson, D. 2009. Interactions with Window Openings by Office Occupants, Building and Environment 44, pp. 2378-2395. • Hensen, J.L.M. 2004. Towards More Effective Use of Building Performance Simulation in Design, Developments in Design & Decision Support Systems in Architecture and Urban Planning, p. 291-306, Eindhoven, Netherlands. • Herkel, S., Knapp, U., Pfafferott, j. 2005. A Preliminary Model of User Behaviour Regarding thè Manual Control of Windows in Office Buildings, In Building Simulation 2005, Ninth International IBPSA Conference, Montréal, Canada. • Flerkel, S., Knapp, U., Pfafferott, J. 2008. Toward a Model of User Behavior Regarding thè Manual Control of Windows in Office Buildings, Building and Environment 43, pp. 588-600. • Floes, P., Hensen, J.L.M., Loomans, M.G.L.C., de Vries, B., Bourgeois, D. 2009. User Behavior in Whole Building Simulation, Energy and Buildings 41, pp. 295-302. • Hopfe, C.J. and Hensen, J. 2006. An Approach to Use Building Performance Simulation to Support Design Optimization, Proceedings of thè forum bauinformatik. • Howard-Reed and Wallace. 2002. The Effect of Opening Windows on Air Change Rates in Two Homes. • Humphreys, M.A., Nicol, JF. 1998. Understanding thè Adaptive Approach to Thermal Comfort, ASHRAE Transactions 104, pp. 991-1004. • Hunt, D. 1979. The Use of Artificial Lighting in Relation to Daylight Levels and Occupancy, Building and environment 14, p. 21-33. • IDA ICE 4.0 (September 2009), Manual version 4.0 EQUA Simulation AB. • Johnson, T. and Long, T. 2005. Determining thè Frequency of Open Windows in Residences: A Pilot Study in Durham, North Carolina during Varying Temperature Conditions, Journal of exposure analysis and environmental epidemiology 15, pp. 329- 349. • Levine, M. et al. 2007, Residential and Commercial Buildings; in: Metz, B., Davidson, O.R., Bosch, P.R., Dave, R. and Meyer, L.A. (Eds.), Climate Change 2007: Mitigation. Contribution of Working Group III to thè Fourth Assessment Report of thè Intergovernmental Panel on Climate Change', Cambridge University Press, Cambridge and New York. • Linden, A. L., Carlsson-Kanyama, A., Eriksson, B. 2006. Efficient and Inefficient Aspects of Residential Energy Behaviour: What Are thè Policy Instruments for Change? Energy Policy 34, pp. 1918-1927. • Macdonald, IA. 2002. Quantifying thè Effects of Uncertainty in Building Simulation, Ph.D. thesis. • Mahdavi, A., and Pròglhof, C. 2008. A Model-Based Approach to Natural Ventilation, Building and Environment 43, pp. 620-627. • Mahdavi, A., Kabir, E., Mohammad, A., Pròglhof, C. 2008. User Based Window Operation in an Office Building, Indoor Air 2008, Copenhagen, Denmark. • Mahdavi, A., Mohammadi, A., Kabir, E., Lambeva, L. 2008. Occupants’ Operation of Lighting and Shading Systems in Office Buildings, Journal of Building Performance Simulation 1, pp. 57-65. • Nicol J.F. August 13-15, 2001. Characterizing Occupant Behavior In Buildings: Toward a Stochastic Model of Occupant Use of Windows, Lights, Blind Heaters and Fans, Proceedings of the7th InternationalIBPSA Conference, Rio de Janeiro, Brazil. • Nicol, J.F. and Humphreys, M.A. 1972. Thermal comfort as part of a self-regulating system, Proceedings of thè CIB Symposium on Thermal Comfort (Watford), Building Research Establishment. • Nicol, J.F. and Humphreys, M.A. 2004. A Stochastic Approach to Thermal Comfort - Occupant Behavior and Energy Use in Buildings, ASHRAE Transactions vol. 110 (II), pp. 554-568. • Offermann, F., Brennan, S., Flodgson, A., Jenkins, P. 2008. Window Usage, Ventilation, and Formaldehyde Concentrations in New California Homes, Proceedings of The llth International Conference on Indoor Air Quality and Climate, Indoor Air 2008, Copenhagen, Denmark. • Paciuk, M. 1989. The Role of Personal Control of thè Environment in Thermal Comfort and Satisfaction at thè Workplace, Ph.D Thesis, University of Wisconsin, Lehman. • Pallot, A.C. 1962. Window Opening in an Office Building, Yearbook of thè Heating and Ventilating Industry, pp. 4-12. • Parys, W. 2013. Cost Optimization of Cellular Office Buildings Based on Building Energy Simulation, PhD Thesis. • Parys, W., Saelens, D., Hens, H. July 27-30 2009. Impact of Occupant Behaviour on Lighting Energy Use, Eleventh International IBPSA Conference Glasgow, Scotland. • Price, P.N. and M.H. Sherman. 2006. Ventilation Behavior and Household Characteristics in New California Houses. • Raja, I.A., Nicol, J.F., McCartney, K.J., Humphreys, M.A. 2001. Thermal Comfort: Use of Controls in Naturally Ventilated Buildings, Energy and Buildings 33, pp. 235-244. • Raymond, S. and Cunliffe, R. 1997. Tomorrow's Office: Creating Effective and Humane Interiors, Spon, London, UK. • Reinhart, C.F. 2004. Lightswitch-2002: A Model for Manual and Automated Control of Electric Lighting and Blinds, Solar Energy 77, pp. 15-28. • Rijal, H. B., Humphreys, M. A., Nicol, J. F. 2009. Understanding Occupant Behaviour: thè Use of Controls in Mixed-Mode Office Building, Building Research & Information 37, pp. 381-396. • Rijal, H.B., Tuohy, P., Humphreys, M.A., Nicol, J.F., Samuel, A., Clarke, J. 2007. Using Results from Field Surveys to Predict thè Effect of Opening Windows on Thermal Comfort and Energy Use in Buildings, Energy and Buildings 39, pp. 823-836. • Robinson, D. 2006. Some Trends and Research Needs in Energy and Comfort Prediction, Proceedings of thè Windsor Conference 2006. • Schweiker, M. 2010. Occupant Behaviour and thè Related Reference Levels for Heating and Cooling. Analysis of thè Factors Causing Individual Differences together with thè Evaluation of their Effect on thè Exergy Consumption within thè Residential Built Environment, PhD Thesis. • Schweiker, M., Haldi, F., Shukuya, M., Robinson, D. 2012. Verification of Stochastic Models of Window Opening Behaviour for Residential Buildings, Journal of Building Performance Simulation 5, pp. 55-74. • Schweiker, M., Shukuya, M. 2010. Comparative Effects of Building Envelope Improvements and Occupant Behavioural Changes on thè Exergy Consumption for Heating and Cooling, Energy Policy 38, pp. 2976-2986. • Socolow, R.H. 1978. Saving Energy in thè Home: Princeton’s Experiments at Twin Rivers, Ballinger Press, Cambridge. • Sorensen, B.J. 2012. Impact of Alternating Window Opening Behaviour on Different Building Envelope Designs, Master Thesis. • Struck, C., Kotek, P., Hensen, J. 2007. On Incorporating Uncertainty Analysis in Abstract Building Performance Simulation Tools, Proceedings of thè 12th symposium for buildingphysics, pp. 193-205. • Tabak, V. 2009. User Simulation of Space Utilisation, System for Office Building Usage Simulation, PhD Thesis. • Tabak, V. and de Vries, B. 2010. Methods for thè Prediction of Intermediate Activities by Office Occupants, Building and Environment 45, pp. 1366-1372. • Tabak, V., de Vries, B., Dijkstra, J., Jessurun, J. 2006. Interaction in Activity Location Scheduling, Proceedings of thè llth Conference on Travel Behavior Research, Kyoto, Japan. • Toftum, J., Andersen, R.V., Jensen, K.L. 2009. Occupant Performance and Building Energy Consumption with Different Philosophies of Determining Acceptable Thermal Conditions, Building and Environment 44, pp. 2009-2016. • Van Meel, J. 2000. The European Office: Office Design and National Context, 010 Publishers, Rotterdam, Netherlands. • Venezia, F. 2011. Occupants’ Action Scenarios. Influence on Indoor Environmental Quality and Energy Consumptions, Master Thesis. • Wagner, A., Gossauer, E., Moosmann, C., Gropp, Th., Leonhart, R. 2007. Thermal Comfort at Workplace Occupant Satisfaction - Results of Field Studies in German Low Energy Office Buildings, Energy and Buildings 39, pp. 758-769. • Warren, P.R., Parkins, L.M. 1984. Window-Opening Behavior in Office Buildings, Building Services Engineering Research and Technology 5, pp. 85—101. • Wilson, S., Hedge, A. 1987. The Office Environment Survey: A study of Building Sickness, Building Use Studies, London. • Yun, G.Y, Steemers, K. 2008. Time-Dependent Occupant Behaviour Models of Window Control in Summer, Building and Environment 43, pp. 1471-1482. • Yun, G.Y. and Steemers, K. 2010. Night-Time Naturally Ventilated Offìces: Statistical Simulations of Window-Use Patterns from Field Monitoring, Solar Energy 84, pp. 1216-31. • Yun, G.Y., Tuohy, P., Steemers, K. 2009. Thermal Performance of a Naturally Ventilated Building Using a Combined Algorithm of Probabilistic Occupant Behavior and Deterministic Heat and Mass Balance Models, Energy and Buildings 41, pp. 489- 499. • Zimmermann, G. 2006. Modeling and Simulation of Dynamic User Behavior in Buildings - A Lighting Control Case Study, eWork and eBusiness in architecture, engineering and construction: Proceedings of thè 6th European conference on product andprocess modelling, p. 309-316.

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