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Automatic Evolutionary Settings of Machine Learning Methods for Buildings’ Thermal Loads Prediction

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Progress in Artificial Intelligence (EPIA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12981))

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Abstract

Due to climate change, buildings can consume 30% more energy by 2040, with energy performance being the critical element for achieving sustainable development in the civil construction sector. One way to solve this evaluation problem is by applying Machine Learning Methods that can assist specialists in civil construction in analyzing scenarios even in the initial phase of the project. The present work evaluates the application of the Elastic Net, Extreme Learning Machine, and Extreme Gradient Boosting models for the prediction of heating and cooling loads in residential buildings. The database used has 768 samples, with eight geometric input variables and two thermal output variables. Differential Evolution optimization algorithm was applied to select method parameters to find the sets of hyperparameters that reinforce the predictive capabilities of the models. The comparisons of the results occurred using the metrics MAE, MAPE, RMSE, and R\(^2\). The results showed that the Extreme Gradient Boosting method obtained a better performance among the tested methods than the literature, presenting the lowest values for the error metrics and significant differences in the statistical tests. Thus, combining Differential Evolution and Extreme Gradient Boosting methods, thermal loads can be predicted, assisting projects that aim at energy savings and sustainability

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Authors and Affiliations

  1. Federal University of Juiz de Fora, Juiz de Fora, Brazil

    Gisele Goulart Tavares, Priscila V. Z. Capriles & Leonardo Goliatt

Authors
  1. Gisele Goulart Tavares
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  2. Priscila V. Z. Capriles
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  3. Leonardo Goliatt
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Corresponding author

Correspondence to Gisele Goulart Tavares .

Editor information

Editors and Affiliations

  1. ISEP/GECAD, Polytechnic Institute of Porto, Porto, Portugal

    Goreti Marreiros

  2. IST/INESC-ID, University of Lisbon, Porto Salvo, Portugal

    Francisco S. Melo

  3. DETI/IEETA, University of Aveiro, Aveiro, Portugal

    Nuno Lau

  4. FEUP/LIACC, University of Porto, Porto, Portugal

    Henrique Lopes Cardoso

  5. FEUP/LIACC, University of Porto, Porto, Portugal

    Luís Paulo Reis

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Goulart Tavares, G., Capriles, P.V.Z., Goliatt, L. (2021). Automatic Evolutionary Settings of Machine Learning Methods for Buildings’ Thermal Loads Prediction. In: Marreiros, G., Melo, F.S., Lau, N., Lopes Cardoso, H., Reis, L.P. (eds) Progress in Artificial Intelligence. EPIA 2021. Lecture Notes in Computer Science(), vol 12981. Springer, Cham. https://doi.org/10.1007/978-3-030-86230-5_15

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  • DOI: https://doi.org/10.1007/978-3-030-86230-5_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86229-9

  • Online ISBN: 978-3-030-86230-5

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