Personalized Energy Optimization in Smart Homes Using Adaptive Machine Learning Models: A Feature-Driven Approach

Matthew Oyeniran; Adekunle J.D.; Sule H.S.; Folorunso O.; Alagbe S.A; Anifowoshe T. J.; Robert C. O.; Ebonyem B. N.; Ideh E. G.; Oyelakin S. O.; Ogu C. K.

doi:10.63158/IJAIS.v2.i1.20

Authors

Matthew Oyeniran Federal University of Agriculture Author
Adekunle J.D. Federal University of Agriculture Author https://orcid.org/0009-0005-8348-1114
Sule H.S. Federal University of Agriculture Author
Folorunso O. Federal University of Agriculture Author
Alagbe S.A Federal University of Agriculture Author
Anifowoshe T. J. Fisher of Men Technology Academy Author
Robert C. O. Topdel Engineering Limited Author
Ebonyem B. N. Lagos State Co-operative College Author
Ideh E. G. Federal University of Agriculture Author
Oyelakin S. O. Bayero University Author
Ogu C. K. Medipolis GmbH Author

DOI:

https://doi.org/10.63158/IJAIS.v2.i1.20

Keywords:

Personalized energy optimization, Smart homes, Machine learning, LightGBM, Energy consumption prediction, Regression analysis

Abstract

The increase in demand for efficient energy smart homes has necessitates the personalized optimization strategies to have a reduction in energy consumption while maintaining user comfort. This research develops a Personalized Energy Optimization System using adaptive machine learning models to analyze household energy patterns and predict consumption in real time. Leveraging the Appliances Energy Prediction Dataset from the UCI repository, we applied supervised learning algorithms such as Gradient Boosting, XGBoost, CatBoost, LightGBM, and Random Forest to identify key factors influencing energy use, including occupancy patterns, appliance usage, and environmental conditions. Through feature engineering, normalization, and one-hot encoding, we enhanced model performance and interpretability. Among the evaluated models, LightGBM achieved the highest accuracy (R²: 0.999573, RMSE: 0.013526), outperforming others in predicting energy consumption. The findings offer data-driven insights for dynamic energy management, optimizing household efficiency, and promoting sustainability.

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