Abstract
Building retrofitting projects represent great opportunities to include new energy efficient technologies, already available on the market. However, for many of these technologies, the occupants’ behaviour within the building can weaken the expected return on investment. This is special true in intelligent technologies that try to compensate the lack of user awareness of energy consumption problems. This chapter describes a model for occupant behaviour within the building in relation to energy consumption, along with a building energy consumption model (ECM) is proposed based on stochastic Markov models. The ECM is used to predict possible energy saving gains from building retrofitting projects. The obtained results demonstrate that the proposed ECM learns occupant behavioural patterns from the building. Additionally, it reliably reproduces them, predicts the building energy consumption and identifies potential areas of energy waste. The ultimate objective of the proposed models is the integration in Decision Support Tools to advise the investor on the selection of technologies and evaluate the merits of the investment.
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References
Abreu JM, Ferrão P, Pereira FC (2012) Using pattern recognition to identify habitual behavior in residential electricity consumption. Energy Buildings 49:479–487
Spinar R et al (2008) Efficient building management with ip-based wireless sensor network. Cork
Atallah L, Yang G (2009) The use of pervasive sensing for behaviour profiling—a survey. Pervasive Mobile Comput 5:447–464
Borgeson S, Brager G (2008) Occupant control of windows: accounting for human behaviour in building simulation. University of California, Berkeley
Bourgeois D, Reinhart C, Macdonald I (2006) Adding advanced behavioural models in whole building energy simulation: a study on the total energy impact of manual and automated lighting control. Energy Buildings 38:814–823 (special issue on daylighting buildings)
Chen J, Taylor JE, Wei H–H (2012) Modeling building occupant network energy consumption decision-making: the interplay between network structure and conservation. Energy Buildings 47:515–524
Ching W, Ng M (2005) Markov Chains: models, algorithms and applications, 1st edn. Springer
Crawley D, Hand J, Kummert M, Griffith B (2008) Contrasting the capabilities of building energy performance simulation programs. Build Environ 43:661–673 (building performance simulation)
Frederic H, Darren R (2009) A comprehensive stochastic model of window usage: theory and validation. Glasgow, pp 545–552
Frontczak M, Wargocki P (2011) Literature survey on how different factors influence human comfort in indoor environments. Build Environ 46:922–937
Herkel S, Knapp U, Pfafferott J (2008) Towards a model of user behaviour regarding the manual control of windows in office buildings. Build Environ 43:588–600
Hoes P et al (2009) User behaviour in whole building simulation. Energy Buildings 41:295–302
Jang W, Healy W (2010) Wireless sensor network performance metrics for building applications. Energy Buildings 42:862–868
Lopes M, Antunes CH, Martins N (2012) Energy behaviours as promoters of energy efficiency: a 21st century review. Renew Sustain Energy Rev 16:4095–4104
Ma Z, Cooper P, Daly D, Ledo L (2012) Existing building retrofits: Methodology and state-of-the-art. Energy Buildings 55:889–902
Mahdavi A, Pröglhöf C (2009) User behaviour and energy performance in buildings
Masini A, Menichetti E (2012) The impact of behavioural factors in the renewable energy investment decision making process: conceptual framework and empirical findings. Energy Policy 40:28–38
Masoso O, Grobler L (2010) The dark side of occupants’ behaviour on building energy use. Energy Buildings 42:173–177
Menzel K et al (2008) Towards a wire-less sensor platform for energy efficient building operation. Tsinghua Sci Technol 13:381–386
Midden C, Kaiser F, McCalley LT (2007) Technology’s four roles in understanding individuals’ conservation of natural resources. J Soc Issues 63:155–174
Midden C, Ham T, Zaalberg R (2008) Pervasive persuasive technology and environmental sustainability. Sydney
Murray C (2009) New insights into rebound effects: theory and empirical evidence. Master’s Thesis, School of Economics and Finance
Nicol J (2001) Characterising occupant behaviour in buildings: towards a stochastic model of occupant use of windows, lights, blinds, heaters and fans
Ockwell D (2008) Energy and economic growth: grounding our understanding in physical reality. Energy Policy 36:4600–4604
Oikonomou V, Becchis F, Steg L, Russolillo D (2009) Energy saving and energy efficiency concepts for policy making. Energy Policy 37:4787–4796
Page J, Robinson D, Morel N, Scartezzini J (2008) A generalised stochastic model for the simulation of occupant presence. Energy Buildings 40:83–98
Pollard D (1984) Convergence of stochastic processes. Springer, New York
Rabiner L (1989) A tutorial on hidden Markov models and selected applications in speech recognition, pp 257–286
Reinhart C (2004) Lightswitch-2002: a model for manual and automated control of electric lighting and blinds. Sol Energy 77:15–28
Rijal H et al (2007) Using results from field surveys to predict the effect of open windows on thermal comfort and energy use in buildings. Energy Buildings 39:823–836
Robinson D (2006) Some trends and research needs in energy and comfort prediction. Windsor
Tabak V (2009) User simulation of space utilization. Eindhoven
Tabak V, Vries B (2010) Methods for the prediction of intermediate activities by office occupants. Build Environ 45:1366–1372
Team E (2010) Tips & tricks for using EnergyPlus: insider secrets to using Energy-Plus. U.S. DOE Energy Efficiency and Renewable Energy (EERE)
Tindale A (2010) Designbuilder and energyplus. Designbuilder software ltd. Building Simulation User News 25
Virote J (2010) Algorithms and methodologies for decision support in energy efficiency on buildings. Universidade Nova de Lisboa, Lisboa
Virote J, Neves-Silva R (2010) User behaviour modeling of buildings energy consumption using hidden Markov models. Coimbra
Virote J, Neves-Silva R (2012) Stochastic models for building energy prediction based on occupant behaviour assessment. Energy Buildings 53:183–193
Wada T, Matsuyama T (2000) Multiobject behaviour recognition by event driven selective attention method. IEEE Trans Pattern Anal Mach Intell 22:873–887
Yalcintas M (2008) Energy-savings predictions for building-equipment retrofits. Energy Buildings 40:2111–2120
Yu Z et al (2011) A systematic procedure to study the influence of occupant behavior on building energy consumption. Energy Buildings 43(6):1409–1417
Zimmerman G (2007) Modeling and simulation of individual user behaviour for building performance predictions. San Diego, pp 913–920
Acknowledgments
The presented work has been partly supported by the European Commission through ICT Project EnPROVE: Energy consumption prediction with building usage measurements for software-based decision support (G. A. FP7-248061).
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Virote, J., Neves-Silva, R. (2013). Modelling the Occupant Behaviour Impact on Buildings Energy Prediction. In: Pacheco Torgal, F., Mistretta, M., Kaklauskas, A., Granqvist, C., Cabeza, L. (eds) Nearly Zero Energy Building Refurbishment. Springer, London. https://doi.org/10.1007/978-1-4471-5523-2_5
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DOI: https://doi.org/10.1007/978-1-4471-5523-2_5
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