Abstract
Numerous aspects influence the passive energy performance of residential stocks. Besides building characteristics, urban planning is considered a key factor. This study analyses the influence of five covariates on both the building scale [shape factor (S/V), year of construction (Y)] and the urban scale [urban block (UB), street H/W ratio, and orientation (O)] on two response variables that assess the passive energy performance of residential stocks: energy demand for cooling (EDc) and for heating (EDh). By modelling the energy performance of a set of buildings in a neighbourhood of Castellón de la Plana (Spain) by conducting dynamic simulation with the EnergyPlus software, values for response variables can be obtained. Prediction models for response variables have been previously developed by considering a bottom-up approach and a multivariate analysis based on the Integrated Nested Laplace Approximation (INLA) methodology. The statistical analysis allowed the order of covariates to be found by level of significance: S/V, Y, H/W, UB and O. Despite the greater significance of building aspects, urban aspects also acquire notable relevance. Based on the results obtained herein, a set of design strategies is established and a new urban layout is proposed. The energy assessment of the new urban layout concludes that 57.12% of savings in energy demand can be made compared to the actual energy demand in the existing neighbourhood.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Blangiardo M, Camaletti M (2015) Spatial and spatio-temporal Bayesian models with R-INLA. Wiley, Chichester, West Sussex
Braulio-Gonzalo M (2016) Propuesta metodológica para la caracterización del comportamiento energético pasivo del parque edificatorio residencial existente considerando su contexto urbano. Universitat Jaume I
Braulio-Gonzalo M, Juan P, Bovea MD, Ruá MJ (2016) Modelling energy efficiency performance of residential building stocks based on Bayesian statistical inference. Environ Model Softw 83:198–211. doi:10.1016/j.envsoft.2016.05.018
DesignBuilder Software v.4 (2015) Retrieved from http://www.desingbuilder.co.uk
Higueras E (2006) Urbanismo bioclimático (vol 1a edición). Gustavo Gili, Barcelona. ISBN 978-84-252-2071-5
Olgyay V (1963) Design with climate: bioclimatic approach to architectural regionalism. Princeton University Press, Princeton
Swan LG, Ugursal VI (2009) Modeling of end-use energy consumption in the residential sector: a review of modeling techniques. Renew Sustain Energy Rev 13(8):1819–1835. doi:10.1016/j.rser.2008.09.033
U.S. Department of Energy (2013) Energy plus software, Berkeley. Retrieved from http://apps1.eere.energy.gov/buildings/energyplus/energyplus_about.cfm
Acknowledgements
The authors wish to thank economic support from the Spanish Ministry of Economy and Competitiveness, through Project BIA2013-44001-R, entitled: Protocolo de Diseño Integrado para la Rehabilitación de la Vivienda Social y Regeneración Urbana.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Braulio-Gonzalo, M., Ruá Aguilar, M.J., Bovea Edo, M.D. (2017). Analysis of the Influence of Variables Linked to the Building and Its Urban Context on the Passive Energy Performance of Residential Stocks. In: Mercader-Moyano, P. (eds) Sustainable Development and Renovation in Architecture, Urbanism and Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-51442-0_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-51442-0_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-51441-3
Online ISBN: 978-3-319-51442-0
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)