Abstract
Several equitable approaches have been proposed to reduce world energy consumption against a backdrop of a growing global climate crisis. Among these, we can mention the attempts to improve the energy use of household appliances and utilities, such as air conditioners. One of the strategies used to reduce these devices’ unnecessary energy consumption is estimating the thermal variation in the environments, especially still during their design phase. One of the most advanced methods for this estimation uses computer simulations, which require a high level of technical knowledge. For that, a relatively simple alternative is the creation of metamodels. This work compares two machine learning approaches for developing a metamodel capable of estimating the thermal load in single-family buildings. The metamodels evaluated were the Artificial Neural Networks and the Gradient Boosting Machine. The results obtained made it possible to observe a better performance in the Gradient Boosting Machine approach indicators in relation to Artificial Neural Networks. The negative point is that Gradient Boosting Machine requires a relatively long training time, making its use in routine projects less feasible.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
(2018a) The Future of Cooling. Tech. rep., International Energy Agency
Alves T, Machado L, de Souza RG, de Wilde P (2018) Assessing the energy saving potential of an existing high-rise office building stock. Energy Build 173:547–561. https://doi.org/10.1016/J.ENBUILD.2018.05.044, https://www.sciencedirect.com/science/article/pii/S0378778817335752
Belleri A, Lollini R, Dutton SM (2014) Natural ventilation design: An analysis of predicted and measured performance. Build Environ 81:123–138. https://doi.org/10.1016/J.BUILDENV.2014.06.009, https://www.sciencedirect.com/science/article/pii/S0360132314001954
Corgnati SP, Fabrizio E, Filippi M, Monetti V (2013) Reference buildings for cost optimal analysis: Method of definition and application. Applied Energy 102:983–993. https://doi.org/10.1016/J.APENERGY.2012.06.001, https://www.sciencedirect.com/science/article/pii/S0306261912004394
DOE (2018) EnergyPlus 8.9. https://energyplus.net/
IEA (2018b) World Energy Outlook 2018: The Future is Electrifying. Oecd/Iea. https://www.iea.org/workshops/world-energy-outlook-2018-the-future-is-electrifying.html
Melo A, Fossati M, Versage R, Sorgato M, Scalco V, Lamberts R (2016) Development and analysis of a metamodel to represent the thermal behavior of naturally ventilated and artificially air-conditioned residential buildings. Energy Build 112:209–221. https://doi.org/10.1016/J.ENBUILD.2015.12.021, https://www.sciencedirect.com/science/article/pii/S0378778815304588?via%3Dihub
Østergård T, Jensen RL, Maagaard SE (2018) A comparison of six metamodeling techniques applied to building performance simulations. Appl Energy 211:89–103. https://doi.org/10.1016/J.APENERGY.2017.10.102, https://www.sciencedirect.com/science/article/pii/S0306261917315489
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: Machine learning in Python. J Mach Learn Res 12:2825–2830
Picco M, Lollini R, Marengo M (2014) Towards energy performance evaluation in early stage building design: A simplification methodology for commercial building models. Energy Build 76:497–505. https://doi.org/10.1016/j.enbuild.2014.03.016
Python (2018) Python software foundation. https://www.python.org/
Rackes A, Melo AP, Lamberts R (2016) Naturally comfortable and sustainable: Informed design guidance and performance labeling for passive commercial buildings in hot climates. Appl Energy 174:256–274. https://doi.org/10.1016/J.APENERGY.2016.04.081, https://www.sciencedirect.com/science/article/pii/S0306261916305426
Roetzel A, Tsangrassoulis A, Dietrich U (2014) Impact of building design and occupancy on office comfort and energy performance in different climates. Build Environ 71:165–175. https://doi.org/10.1016/J.BUILDENV.2013.10.001, https://www.sciencedirect.com/science/article/pii/S0360132313002862
Sobol’ IM (1993) Sensitivity estimates for nonlinear mathematical models and their Monte Carlo estimation. Math Model Comput Exp 1(4):407–417
Triana MA, Lamberts R, Sassi P (2018) Should we consider climate change for brazilian social housing? assessment of energy efficiency adaptation measures. Energy Build 158:1379–1392. https://doi.org/10.1016/j.enbuild.2017.11.003, https://www.sciencedirect.com/science/article/pii/S0378778817315578
Versage R (2015) Metamodelo para estimar a carga térmicade edificaċȯes condicionadas artificialmente. PhD thesis , Universidade Federal de Santa Catarina
Yigit S (2021) A machine-learning-based method for thermal design optimization of residential buildings in highly urbanized areas of Turkey. J Build Eng 38:102225. https://doi.org/10.1016/j.jobe.2021.102225, https://www.sciencedirect.com/science/article/pii/S2352710221000814
Zhao H X, Magoulès F (2012) A review on the prediction of building energy consumption. Renew Sustain Energy Rev 16(6):3586–3592. https://doi.org/10.1016/j.rser.2012.02.049
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Olinger, M.S., de Araújo, G., Dutra, M.L. et al. Metamodel Development to Predict Thermal Loads for Single-family Residential Buildings. Mobile Netw Appl 27, 1977–1986 (2022). https://doi.org/10.1007/s11036-022-01968-w
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11036-022-01968-w