Modelling the energy demand of a residential building using an artificial neural network (ANN) approach

Boukarta Soufiane; Hourakhsh Ahmad Nia

doi:10.38027/jsalutogenic_vol2no1_7

Authors

Boukarta Soufiane Assoc. Prof. In Institute of architecture and urban planning, Blida university, ETAP laboratory , Algeria. Author https://orcid.org/0000-0003-3094-7740
Hourakhsh Ahmad Nia Assoc. Prof. in Faculty of Engineering and Natural Sciences, Department of Architecture, Alanya University, Turkey Author https://orcid.org/0000-0002-1083-280X

DOI:

https://doi.org/10.38027/jsalutogenic_vol2no1_7

Keywords:

Energy demand, Artificial neural network , Architectural Design , passive approach , reliability test

Abstract

The consumption of fossil fuels accelerates and accentuates the formation and development of the climate change phenomenon. Understanding the energy demand in the early-stage design could lead to more energy savings. There are several methods for predicting buildings energy demand and they differ overall in terms of prediction quality. The multicollinearity of variables, linearity, and other conditions limit the predictive quality of standard models such as linear regression modeling. This paper is interested in developing an energy demand prediction tool based on artificial neural network modeling ANN to test its limitations and its predictive quality. For this purpose, and based on the scientific literature, a panel of parameters often used by architects at the time of architectural design was selected, which are, the thermal resistance of the external walls, the type, and rate of glazing, the orientation, the shading devices the set point cooling PMV and natural ventilation rate schedule. A campaign of 600 dynamic thermal simulations is then run under energy plus using the Latin Hypercube Sampling (LHS) approach. The best ANN model obtained after testing several activating functions gave a prediction potential of over 99.7%. The model also ranks each parameter according to its importance in the equation identifying the energy demand. It can therefore be assumed that the artificial neural network technique is effective and the ANN outperforms the other prediction methods.

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References

Aprue, (2021). Final report, “Consommation Energétique Finale de l’Algérie, Chiffres clés Année 2019”.

ASHRAE Guideline 14-2014. (2014). Measurement of Energy, Demand and Water Savings.

Baker, N., & Steemers, K. (2003). Energy and environment in architecture: a technical design guide. Taylor & Francis. https://doi.org/10.4324/9780203223017

Boukarta, S. (2021). Predicting energy demand of residential buildings: A linear regression-based approach for a small sample size. Selected Scientific Papers-Journal of Civil Engineering, 16(2), 67-85. https://doi.org/10.2478/sspjce-2021-0017

Boukarta, S., & Berezowska-Azzag, E. (2017). “URBAN ISLAND” AS AN ENERGY ASSESSMENT TOOL. THE CASE OF MOUZAIA, AL ALGERIA. Journal of Applied Engineering Science, 15(2). https://doi.org/10.5937/jaes15-12951

Boukarta, S., & Berezowska-Azzag, E. (2018). Assessing households’ gas and electricity consumption: A case study of Djelfa, Algeria. Quaestiones Geographicae, 37(4), 111-129. https://doi.org/10.2478/quageo-2018-0034

Bourdeau, M., qiang Zhai, X., Nefzaoui, E., Guo, X., & Chatellier, P. (2019). Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society, 48, 101533. https://doi.org/10.1016/j.scs.2019.101533

Dall'O', A., Galante, A., & Torri, M. (2012). A methodology for the energy performance classification of residential building stock on an urban scale. Energy and Buildings, 48, 211-219. https://doi.org/10.1016/j.enbuild.2012.01.034

Dogan, T., & Reinhart, C. (2017). Shoeboxer: An algorithm for abstracted rapid multi-zone urban building energy model generation and simulation. Energy and Buildings, 140, 140-153. https://doi.org/10.1016/j.enbuild.2017.01.030

Dascalaki, E. G., Droutsa, K., Gaglia, A. G., Kontoyiannidis, S., & Balaras, C. A. (2010). Data collection and analysis of the building stock and its energy performance—An example for Hellenic buildings. Energy and Buildings, 42, 1231–1237. https://doi.org/10.1016/j.enbuild.2010.02.014

Escandón, R., Ascione, F., Bianco, N., Mauro, G. M., Suárez, R., & Sendra, J. J. (2019). Thermal comfort prediction in a building category: Artificial neural network generation from calibrated models for a social housing stock in southern Europe. Applied Thermal Engineering, 150, 492-505. https://doi.org/10.1016/j.applthermaleng.2019.01.013

Bartiaux, F. (2003). A socio-anthropological approach to energy-related behaviors and innovations at the household level. In Summer Study – Time to Turn Down Energy Demand: Dynamics of Consumption (pp. 1239-1250).

Ghedamsi, R., Settou, N., Gouareh, A., Khamouli, A., Saifi, N., Recioui, B., & Dokkar, B. (2016). Modeling and forecasting energy consumption for residential buildings in Algeria using bottom-up approach. Energy and Buildings, 121, 309-317. https://doi.org/10.1016/j.enbuild.2015.12.030

Hassan, H. S., Abdel-Gawwad, H. A., Vásquez-García, S. R., Israde-Alcántara, I., Flores-Ramirez, N., Rico, J. L., & Mohammed, M. S. (2019). Cleaner production of one-part white geopolymer cement using pre-treated wood biomass ash and diatomite. Journal of Cleaner Production, 209, 1420-1428. https://doi.org/10.1016/j.jclepro.2018.11.137

Kaoula, D. (2021). Towards a morpho-environmental neighbourhood optimization method: MENOM. Sustainable Cities and Society, 70, 102880. https://doi.org/10.1016/j.scs.2021.102880

Kaoula, D., & Bouchair, A. (2022). The pinpointing of the most prominent parameters on the energy performance for optimal passive strategies in ecological buildings based on bioclimatic, sensitivity and uncertainty analyses. International Journal of Ambient Energy, 43(1), 685-712. https://doi.org/10.1080/01430750.2019.1665583

Kitous, N. (2013). Forme urbaine et environnement thermo aéraulique en climat chaud est sec : Cas du ksar Ghardaïa dans le Sahara algérien (Doctoral dissertation, Ecole Polytechnique d'Architecture et d'Urbanisme-Hocine Aït Ahmed).

McQuiston, F. C., Parker, J. D., & Spitler, J. D. (2004). Heating, ventilating, and air conditioning: analysis and design. John Wiley & Sons.

Popescu, M. C., Balas, V. E., Perescu-Popescu, L., & Mastorakis, N. (2009). Multilayer perceptron and neural networks. WSEAS Transactions on Circuits and Systems, 8(7), 579-588.

Rogers, R. G., & Gumuchdjian, P. (2008). Des Villes durables pour une petite planète. Moniteur.

Saffari, M., de Gracia, A., Ushak, S., & Cabeza, L. F. (2017). Passive cooling of buildings with phase change materials using whole-building energy simulation tools: A review. Renewable and Sustainable Energy Reviews, 80, 1239-1255. https://doi.org/10.1016/j.rser.2017.05.139

Semahi, S., Zemmouri, N., Singh, M. K., et al. (2019). Comparative bioclimatic approach for comfort and passive heating and cooling strategies in Algeria. Building and Environment, 161, 106271. https://doi.org/10.1016/j.buildenv.2019.106271

Singh, M. K., Mahapatra, S., & Teller, J. (2013). An analysis on energy efficiency initiatives in the building stock of Liege, Belgium. Energy policy, 62, 729-741. https://doi.org/10.1016/j.enpol.2013.07.138

Ye, Z., Cheng, K., Hsu, S. C., Wei, H. H., & Cheung, C. M. (2021). Identifying critical building-oriented features in city-block-level building energy consumption: A data-driven machine learning approach. Applied Energy, 301, 117453. https://doi.org/10.1016/j.apenergy.2021.117453

Zhao, H. X., & Magoulès, F. (2012). A review on the prediction of building energy consumption. Renewable and Sustainable Energy Reviews, 16(6), 3586-3592. https://doi.org/10.1016/j.rser.2012.02.049