Introduction to Constructal Law Analysis for a Simplified Hourly Energy Balance Model of Residential Buildings at District Scale

Introduction to Constructal Law Analysis for a Simplified Hourly Energy Balance Model of Residential Buildings at District Scale

Guglielmina MutaniValeria Todeschi Giulia Grisolia Umberto Lucia  

Dipartimento Energia "Galileo Ferraris", Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy

Corresponding Author Email:
22 January 2019
| |
20 March 2019
| | Citation



The energy consumption of buildings is related to multiple factors, such as construction and geometric characteristics, occupancy, climate and microclimate conditions, solar exposure, and urban morphology. Therefore, the interaction between buildings and their surroundings should be taken into consideration.

The aim of this work is to create a bottom-up model at urban scale to evaluate the energy balance of residential buildings starting from their hourly consumption data. The model of hourly energy consumption presents some simplifications to be applied on an urban scale and introduces some variables at block of buildings scale (e.g. sky view factor and canyon height to width ratio). A Geographical Information System was used to localize the data and to provide information on how the shape of the city affects buildings consumption.

The new model was verified on about 50 residential buildings for two consecutive heating seasons in the city of Turin (Italy). The results show that a simplified model can be powerful in evaluation of the energy demand and supply of buildings at urban scale. Moreover, the analogy between buildings and cooling fins allows to point out that the buildings shape is fundamental in the heat exchanges.


buildings morphology, constructal law, residential buildings, space heating consumption model, urban scale

1. Introduction
2. State of the art
3. Materials and Methods
4. Case Study
5. Results
6. Conclusions

[1]    Harish VSKV, Kumar A. (2016). A review on modelling and simulation of building energy systems. Renewable and Sustainable Energy Reviews 56: 1272-1292.

[2]    Nageler P, Koch A, Mauthner F, Leusbrock I, Mach T, Hochenauer C, Heimrath R. (2018). Comparison of dynamic urban building energy models (UBEM): Sigmoid energy signature and physical modelling approach. Energy & Buildings 179: 333-343.

[3]    Castaldo VL, Pisello AL. (2018). Uses of dynamic simulation to predict thermal-energy performance of buildings and districts: A review. WIREs Energy Environ 7: e269.

[4]    Mutani G, Giaccardi F, Martino M, Pastorelli M. (2017). Modeling hourly profile of space heating energy consumption for residential buildings. Proceedings of INTELEC 2017, Gold Coast, Australia.

[5]    Mutani G, Giaccardi F, Martino M, Pastorelli M. (2018). Modeling hourly variations in space heating energy consumption for office buildings. Proceedings of INTELEC 2018, Torino (IT): 7-11th October 2018.

[6]    Lundström L, Akander J, Zambrano J. (2019). Development of a space heating model suitable for the automated model generation of existing multifamily buildings - a case study in Nordic climate. Energies 12: 485.

[7]    Gao H, Koch C, Wu Y. (2019). Building information modelling based building energy modelling: A review. Applied Energy 238: 320-343.

[8]    Biserni C, Garai M. (2016). Energy balance and second law analysis applied to buildings: An opportunity for Bejan theory. International Journal of Heat and Technology 34(Special Issue 1): S185-S187.

[9]    Streicher KN, Padey P, Parra D, Bürer MC, Schneider S, Patel MK. (2019). Analysis of space heating demand in the Swiss residential building stock: Element-based bottom-up model of archetype buildings. Energy & Buildings 184: 300-322.

[10]    Verbekea S, Audenaert A. (2018). Thermal inertia in buildings: A review of impacts across climate and building use. Renewable and Sustainable Energy Reviews 82: 2300-2318.

[11]    Li W, Putra SY, Yang PP. (2004). GIS analysis for the climatic evaluation of 3D urban geometry. GISDECO (2004). %20li.pdf

[12]    Sola A, Corchero C, Salom J, Sanmarti M. (2018). Simulation tools to build urban-scale energy models: A review. Energies 11: 3269.

[13]    Ali U, Shamsi MH, Hoare C, O’Donnell J. (2018). GIS-based residential building energy modeling at district scale. 4th Building Simulation and Optimization Conference, Cambridge, UK: 11-12th September 2018.

[14]    Hamanah WMA, Kassas M, Mokheimer EMA, Ahmed CB, Said SAM. (2018). Comparison of energy consumption for residential thermal models with actual measurements. J. Energy Resour. Technol 141(3): 032002 (18-1723).

[15]    Dodoo A, Tettey UY, Gustavsson L. (2017). Influence of simulation assumptions and input parameters on energy balance calculations of residential buildings. Energy 120: 718-730.

[16]    Mutani G, Todeschi V. Energy balance at neighbourhood scale for residential buildings. Applied Energy (under revision), Elsevier.

[17]    Mutani G, Todeschi V. (2017). Space heating models at urban scale for buildings in the city of Turin (Italy). Energy Procedia, PII: S1876-6102 (17) 33400.

[18]    Carozza M, Mutani G, Coccolo S, Kaempf J. (2017). Introducing a hybrid energy-use model at the urban scale: The case study of Turin (Italy), Building Simulation Applications Conference, 2-s2.0-85050366851.

[19]    Duffie JA, Beckman WA. (2013). Solar engineering of thermal processes. Fourth Edition, John Wiley & Sons, ISBN 9780470873663.

[20]    Lucia U, Grisolia G. (2019). Exergy inefficiency: An indicator for sustainable development analysis. Energy Reports 5: 62-69.

[21]    Lucia U, Grisolia G. (2017). Unavailability percentage as energy planning and economic choice parameter. Renew. Sustainable Energy Rev. 75: 197-204.