Prediction of the energy performance of buildings helps designers with decision-making during the design process in new construction, as well as in renovation projects. Simulation software is used as a prediction tool to calculate the energy performance of buildings. However, numerous studies question its reliability due to the existing discrepancy (gap) between calculated and actual energy performance. Although occupant behaviour is identified as a factor of major impact on the energy performance of buildings, the complex stochastic nature of user behaviour makes it difficult to define actual occupancy patterns. As a result, standard and normative data are usually used as input in energy simulation models. The aim of this research is to test the effect of the use of actual presence profiles on energy demand simulations compared to the use of international normative presence profiles. A study on energy demand has therefore been developed, using dynamic simulation and monitoring campaigns. The results show that the heating and cooling energy demand may differ by up to 15% depending on whether actual or standard presence profiles are used. Therefore, presence profiles should be considered as a significant factor in the adjustment of input data in renovation projects. The final aim of this investigation is to determine the effect of using more accurate building and occupancy simulation parameters when assessing the feasibility of building renovation (payback period calculation for example). This paper focuses on the effect of presence profiles.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
ASHRAE (2013). ASHRAE 90.1 Prototype Building Models Mid-rise Apartment. Available at https://www.energycodes.gov/901-prototype-building-models-mid-rise-apartment. Accessed 18 May 2018.
Aste N, Angelotti A, Buzzetti M (2009). The influence of the external walls thermal inertia on the energy performance of well insulated buildings. Energy and Buildings, 41: 1181–1187.
Barbosa JA, Mateus R, Bragança L (2016). Occupancy patterns and building performance—Developing occupancy patterns for Portuguese residential buildings. Paper presented at the SBE16 Brazil & Portugal-Sustainable Urban Communities towards a Nearly Zero Impact Built Environment, Vitoria, Brazil.
BOE (2016). Código de la vivienda de la Comunidad de Madrid. Boletín Oficial del Estado. (in Spanish)
Branco G, Lachal B, Gallinelli P, Weber W (2004). Predicted versus observed heat consumption of a low energy multifamily complex in Switzerland based on long-term experimental data. Energy and Buildings, 36: 543–555.
Carpino C, Mora D, Arcuri N, De Simone M (2017). Behavioral variables and occupancy patterns in the design and modeling of Nearly Zero Energy Buildings. Building Simulation, 10: 875–888.
Carpino C, Fajilla G, Gaudio A, Mora D, De Simone M (2018). Application of survey on energy consumption and occupancy in residential buildings. An experience in Southern Italy. Energy Procedia, 148: 1082–1089.
CTE (2016). DB HE Ahorro de energía. Código Técnico de Edificación. Available at https://www.codigotecnico.org/. (in Spanish)
Cuerda E, Pérez M, Neila J (2014). Facade typologies as a tool for selecting refurbishment measures for the Spanish residential building stock. Energy and Buildings, 76: 119–129.
Cuerda E, Guerra-Santin O, Neila González F. J, Romero Herrera N (2015). Post-occupancy monitoring of two flats in Madrid: Development and assessment of a mixed methods methodology. Paper presented at the Passive and Low Energy Architecture (PLEA), Bologna, Italy.
Cuerda E, Guerra-Santin O, Neila González F. J, Romero Herrera N (2016). Evaluation and comparison of building performance in use. In: Proceedings of the 3rd IBPSA-England Conference (BSO 2016), Newcastle, UK.
Cuerda E, Guerra-Santin O, Neila González FJ (2017). Definiendo patrones de ocupación mediante la monitorización de edificios existentes. Informes de la Construcción, 69(548): e223. (in Spanish)
D’Oca S, Hong T (2015). Occupancy schedules learning process through a data mining framework. Energy and Buildings, 88: 395–408.
De Wilde P (2014). The gap between predicted and measured energy performance of buildings: A framework for investigation. Automation in Construction, 41: 40–49.
Decreto-Lei 79/2006 (2006). O regulamento dos Sistemas Energéticos de Climatização em Edifícios-RSECE C.F.R. (in Portuguese)
Dong B, Yan D, Li Z, Jin Y, Feng X, Fontenot H (2018). Modeling occupancy and behavior for better building design and operation—A critical review. Building Simulation, 11: 899–921.
Crawley DB, Lawrie LK, Winkelmann FC, Buhl WF, Huang YJ, et al. (2001). EnergyPlus: Creating a new-generation building energy simulation program. Energy and Buildings, 33: 319–331.
Eurostat (2000). HETUS. Harmonised European Time Use Survey. Available at https://www.h6.scb.se/tus/tus/default.htm
Gaetani I, Hoes P-J, Hensen JLM (2016). Occupant behavior in building energy simulation: Towards a fit-for-purpose modeling strategy. Energy and Buildings, 121: 188–204.
Goldstein DB, Eley C (2014). A classification of building energy performance indices. Energy Efficiency, 7: 353–375.
Gram-Hanssen K (2010). Residential heat comfort practices: understanding users. Building Research & Information, 38: 175–186.
Guerra-Santin O (2011). Behavioural patterns and user profiles related to energy consumption for heating. Energy and Buildings, 43: 2662–2672.
Guerra-Santin O, Tweed Aidan C (2015). In-use monitoring of buildings: An overview and classification of evaluation methods. Energy and Buildings, 86: 176–189.
Guerra-Santin O, Herrera NR, Cuerda E, Keyson D (2016). Mixed methods approach to determine occupants’ behaviour—Analysis of two case studies. Energy and Buildings, 130: 546–566.
Guerra-Santin O, Boess S, Konstantinou T, Romero Herrera N, Klein T, Silvester S (2017). Designing for residents: Building monitoring and co-creation in social housing renovation in the Netherlands. Energy Research & Social Science, 32: 164–179.
Guerra-Santin O, Bosch H, Budde P, Konstantinou T, Boess S, Klein T, Silvester S (2018). Considering user profiles and occupants’ behaviour on a zero energy renovation strategy for multi-family housing in the Netherlands. Energy Efficiency, 11: 1847–1870.
Hong T, Langevin J, Sun K (2018). Building simulation: Ten challenges. Building Simulation, 11: 871–898.
IEA (2016). EBC Annex 53. Total Energy Use in Buildings: Analysis and Evaluation Methods (summary report). International Energy Agency.
INE (2013). Características de los hogares. Ministerio de Economía y Competitividad. (in Spanish)
INE (2015). Continuous Household Survey.
ISO (1998). ISO 7726:1998. Ergonomics of the thermal environment. Instruments for measuring physical quantities.
ISO (2017). ISO/TR 17772-1:2017. Energy Performance of Buildings. Available at https://www.iso.org/standard/60498.html. Accessed 18 May 2018.
Jones RV, de Wilde P, Fuertes A (2015). The gap between simulated and measured energy performance: A case study across six identical new-build flats in the UK. In: Proceedings of the 14th International IBPSA Building Simulation Conference, Hyderabad, India.
Kim J-H (2016). The impact of occupant modelling on energy outcomes of building energy simulation. PhD Thesis, Georgia Institute of Technology, USA.
Legge 90/13 (2015). Decreti Legge attuative di questa legge: (i) Decreto 26 giugno 2015 — DM requisiti minimi, (ii) Decreto 26 giugno 2015 — Certificazione energetica, e (iii) Decreto 26 giugno 2015 — Relazione tecnica; C.F.R. (in Italian)
Menezes AC, Cripps A, Bouchlaghem D, Buswell R (2012). Predicted vs. actual energy performance of non-domestic buildings: Using post-occupancy evaluation data to reduce the performance gap. Applied Energy, 97: 355–364.
Mora D, Carpino C, De Simone M (2018). Energy consumption of residential buildings and occupancy profiles. A case study in Mediterranean climatic conditions. Energy Efficiency, 11: 121–145.
Motuziene V, Vilutiene T (2013). Modelling the effect of the domestic occupancy profiles on predicted energy demand of the energy efficient house. Procedia Engineering, 57: 798–807.
Ouf MM, O’Brien W, Gunay HB (2018). Improving occupant-related features in building performance simulation tools. Building Simulation, 11: 803–817.
Santangelo A, Tondelli S (2017). Occupant behaviour and building renovation of the social housing stock: Current and future challenges. Energy and Buildings, 145: 276–283.
Stevenson F, Leaman A (2010). Evaluating housing performance in relation to human behaviour: New challenges. Building Research & Information, 38: 437–441.
Sun K, Hong T (2017). A simulation approach to estimate energy savings potential of occupant behavior measures. Energy and Buildings, 136: 43–62.
Th-BCE (2012). Th-BCE.2012.Méthode de la réglementation thermique. Available at http://ec.europa.eu/growth/tools-databases/tris/fr/index.cfm/search/?trisaction=search.detail&year=2011&num=159&fLang=FR&dNum=1. Accessed 18 May 2018. (in French)
UNE-EN (2000). UNIE-EN13829. Aislamiento térmico. Determinación de la estanqueidad al aire en edificios. Método de presurización por medio de ventilador. (in Spanish)
Zhang Y, Bai X, Mills FP, Pezzey JCV (2018). Rethinking the role of occupant behavior in building energy performance: A review. Energy and Buildings, 172: 279–294.
This research has been funded by Universidad Politécnica de Madrid and partially by INTERREC IVB and the Building Technology Accelerator (BTA) — Climate Kic. We would also like to show our gratitude to TEP 130 research group with special thanks to Jessica Fernandez-Agüera for assistance with the Blower Door test.
About this article
Cite this article
Cuerda, E., Guerra-Santin, O., Sendra, J.J. et al. Comparing the impact of presence patterns on energy demand in residential buildings using measured data and simulation models. Build. Simul. 12, 985–998 (2019). https://doi.org/10.1007/s12273-019-0539-z
- building energy performance
- occupant patterns
- occupant schedules
- occupant behaviour
- occupancy monitoring
- post-occupancy evaluation