Modelling and Comparison of Machine-Learning Algorithms for Energy Consumption Prediction in Smart Buildings

Authors

  • Luis Arturo Ortiz-Suarez Universidad Politécnica de Pachuca
  • Fernando Perez-Tellez Technological University Dublin
  • Jorge A. Ruiz-Vanoye Universidad Politécnica de Pachuca
  • Francisco Rafael Trejo-Macotela Universidad Politécnica de Pachuca
  • Eric Simancas-Acevedo Universidad Politécnica de Pachuca
  • Jazmín Rodríguez Flores Universidad Politécnica de Pachuca
  • Ocotlán Diaz-Parra Universidad Politécnica de Pachuca
  • Miguel Liceaga Ortiz de la Peña Universidad Politécnica de Pachuca

DOI:

https://doi.org/10.13053/cys-29-2-5663

Keywords:

Energy consumption prediction, smart buildings, energy optimization, predictive models, machine learning

Abstract

One-third of global energy demand is attributed to consumption in buildings, with HVAC and lighting systems as the primary contributors. This study presents the development and comparison of several machine-learning algorithms for predicting energy consumption in a building simulated using EnergyPlus and following the Team Data Science Process (TDSP) methodology. Feature-selection techniques (feature selection and feature importance) were applied to identify the most influential variables. Five predictive models were trained: MLP, SVR, XGBoost, Random Forest and Keras Regressor. Results demonstrate that the MLP model achieved the highest accuracy, while XGBoost showed greater stability. Additionally, traditional statistical models (ARIMA and SARIMAX) were compared to machine-learning models for multi-horizon prediction.

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Published

2025-06-28

Issue

Vol. 29 No. 2 (2025): 29(2) 2025

Section

Articles

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