Developing a Machine Learning Model for a Smart Home System
https://doi.org/10.35596/1729-7648-2025-31-3-22-32
Abstract
Machine learning improves the efficiency of smart home systems, allows home automation systems to regulate heating and cooling systems, lighting, room temperature and other parameters. Data variability and improvement of such systems require constant expansion of data sets, retraining or additional training of machine learning models, modification of algorithms and architectures. The article presents models for predicting heating and cooling loads of a house based on machine learning methods. The results of exploratory data analysis, construction of regression models for predicting the load of heating and cooling systems are presented. The efficiency of selecting hyperparameter values based on the grid search method is shown. A neural network model is considered that allows simultaneous prediction of the load of heating and cooling systems. The accuracy is assessed and the models are compared based on regression quality metrics.
About the Author
M. M. Lukashevich
Belarusian State University;
Belarusian State University of Informatics and Radioelectronics
Belarus
Belarus
Lukashevich M. M., Cand. Sci. (Tech.), Associate Professor at the Belarusian State University, Doctoral Student of the Belarusian State University of Informatics and Radioelectronics
220013, Minsk, Platonova St., 39
Tel.: +375 17 293-86-17
Lukashevich Marina Mikhailovna
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Review
For citations:
Lukashevich M.M. Developing a Machine Learning Model for a Smart Home System. Digital Transformation. 2025;31(3):22-32. (In Russ.) https://doi.org/10.35596/1729-7648-2025-31-3-22-32
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