Skip to main content

Advertisement

SpringerLink
Log in

Impact of wall discretization on the modeling of heating/cooling energy consumption of residential buildings

  • Original Article
  • Published:
Energy Efficiency Aims and scope Submit manuscript

Abstract

Software tools able to predict heating and cooling energy demand can effectively support the improvement of energy efficiency in buildings. The latest development of available technologies, such as free cooling and phase change materials, which exploit the building inertia effects, cannot be analyzed through the commonly used steady-state approaches, where the thermal inertia of the building envelope is neglected and monthly averaged climatic data are taken into account. Furthermore, the need to implement innovative regulation criteria for heating and cooling systems and the coupled study of plant and building dynamics push towards the use of dynamic tools with low computational costs. The present paper investigates the simulation of the thermal performance of a benchmark residential building using a self-developed dynamic code implemented in the dedicated software called Building Energy Performance Simulator (BEPS), validated in a previous authors’ work. To investigate the dynamic characteristics of a building in different working conditions, several simulations have been performed for different European localities with different mathematical approaches. In particular, different levels of wall discretization have been considered, highlighting the importance of the inertia of the building envelope. The results show that the use of a simplified description of the entire building leads to good predictions of its energy demand in dynamic conditions with low computational costs. However, only heating demand prediction can be done if the wall thermal capacitance is lumped in a single node, while at least two nodes are needed to correctly predict the building cooling energy demand during the hot season.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Abbreviations

α :

Absorbance coefficient

C :

Thermal capacitance or heat capacity (J K−1)

CDD:

Cooling degree day (°C)

HDD:

Heating degree day (°C)

j :

Wall index

\( \dot{q} \) :

Heat flux (W)

R :

Thermal resistance (K W−1)

S :

Surface (m2)

t :

Time (s, h)

T :

Temperature (°C)

V :

Volume (m3)

b :

Base temperature

cs:

Cooling system

d :

Days

e:

External

h :

Hour

hs:

Heating system

i:

Internal

is:

Internal sources

j :

Wall index for vertical walls and roof

s:

Solar

sg:

Solar gain

v:

Ventilation

x1, x2, x3:

Wall layers

w:

Wall

win:

Windows

References

  • ASHRAE (1980) Standard 90A-1980. Energy conservation in new building design. Atlanta: American Society of Heating Refrigerating and Air Conditioning Engineers

  • Baetens, R., Jelle, B. P., & Gustavsen, A. (2010). Phase change materials for building applications: a state-of-the-art review. Energy and Buildings, 42, 1361–1368.

    Article  Google Scholar 

  • Bianco, V., De Rosa, M., Scarpa, F., & Tagliafico, L. A. (2014). Analysis of energy demand in residential buildings for different climates by means of dynamic simulation. International Journal of Ambient Energy. doi:10.1080/01430750.2014.907207.

    Google Scholar 

  • Boyer, H., Chabriat, J. P., Grondin-Perez, B., Tourrand, C., & Brau, J. (1994). Thermal building simulation and computer generation of nodal models. Building and Environment, 31, 207–214.

    Article  Google Scholar 

  • BPIE (2011). Buildings Performance Institute Europe. Europe’s buildings under the microscope.

  • CIBSE (2006). Degree-days: theory and application. TM41. Chartered Institution of Building Services Engineering. London (UK).

  • Crawley, D. B., Hand, J. W., Kummert, M., & Griffith, B. T. (2008). Contrasting the capabilities of building energy performance simulation programs. Building and Environment, 43, 661–673.

    Article  Google Scholar 

  • Dargahi-Zaboli, M., & Alizadeh, R. (2013). Using dynamic model for determination of heat losses in cold weather from a typical house in Tabriz constructed by lightweight concrete blocks. Energy Efficiency. doi:10.1007/s12053-013-9243-5.

    Google Scholar 

  • De Rosa, M., Bianco, V., Scarpa, F., & Tagliafico, L. A. (2014). Heating and cooling building energy demand evaluation; a simplified model and a modified degree days approach. Applied Energy. doi:10.1016/j.apenergy.2014.04.067.

    Google Scholar 

  • Dormand, J. R., & Prince, P. J. (1980). A family of embedded Runge-Kutta formulae. Journal of Computational and Applied Mathematics, 6, 19–26.

    Article  MathSciNet  MATH  Google Scholar 

  • Energy Plus (2012). The reference to EnergyPlus calculations.

  • EU (2002). On the energy performance of buildings. Directive 2002/91/EC of the European Parliament and of the Council. Official Journal of the European Communities.

  • Ghiaus, C., & Allard, F. (2006). Potential for free-cooling by ventilation. Solar Energy, 80, 402–413.

    Article  Google Scholar 

  • Harvey, L. D. D. (2009). Reducing energy use in the buildings sector: measures, costs, and examples. Energy Efficiency, 2, 139–163.

    Article  Google Scholar 

  • Isaac, M., & van Vuuren, D. P. (2009). Modeling global residential sector energy demand for heating and air conditioning in the context of climate change. Energy Policy, 37, 507–521.

    Article  Google Scholar 

  • ISO (2008). UNI EN ISO 13790:2008. Energy performance of buildings—calculation of energy use for space heating and cooling.

  • Lechtenböhmer, S., & Schüring, A. (2011). The potential for large-scale savings from insulating residential buildings in the EU. Energy Efficiency, 4, 257–270.

    Article  Google Scholar 

  • Li DHW, Yang L., Lam, J.C. (2012) Impact of climate change on energy use in the built environment in different climate zones –a review. Energy 42, 103–112

  • Mourshed, M. (2012). Relationship between annual mean temperature and degree-days. Energy and Building, 54, 418–425.

    Article  Google Scholar 

  • Nielsen, T. R. (2005). Simple tool to evaluate energy demand and indoor environment in the early stages of building design. Solar Energy, 78, 73–83.

    Article  Google Scholar 

  • Tagliafico, L. A., Scarpa, F., & De Rosa, M. (2014). Dynamic thermal models and CFD analysis for flat-plate thermal solar collectors—a review. Renewable and Sustainable Energy Reviews, 30, 526–537.

    Article  Google Scholar 

  • UNI EN 15251 (2008). Indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor air quality, thermal environment, lighting and acoustics.

  • USDOE (2013). U.S. Department of Energy. Weather data for EnergyPlus. http://apps1.eere.energy.gov/buildings/energyplus/weatherdata_about.cfm. Accessed 11 May 2014.

  • Vaidya, P., Greden, L., Eijadi, D., McDougall, T., & Cole, R. (2009). Integrated cost-estimation methodology to support high-performance building design. Energy Efficiency, 2, 69–85.

    Article  Google Scholar 

Download references

Acknowledgments

The authors want to express their gratitude to the three anonymous reviewers for their interesting comments, which were useful to improve the quality of the present paper.

Author information

Authors and Affiliations

  1. DIME/TEC - Division of Thermal Energy and Environmental Conditioning, University of Genoa, Via All’Opera Pia 15A, 16145, Genoa, Italy

    Mattia De Rosa, Vincenzo Bianco, Federico Scarpa & Luca A. Tagliafico

Authors
  1. Mattia De Rosa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vincenzo Bianco
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Federico Scarpa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Luca A. Tagliafico
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vincenzo Bianco.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

De Rosa, M., Bianco, V., Scarpa, F. et al. Impact of wall discretization on the modeling of heating/cooling energy consumption of residential buildings. Energy Efficiency 9, 95–108 (2016). https://doi.org/10.1007/s12053-015-9351-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12053-015-9351-5

Keywords

Search

Navigation