Attipoe, David, Moulla, Donatien Koulla, Mnkandla, Ernest et Abran, Alain.
2025.
« Predicting residential energy consumption in South Africa using ensemble models ».
Applied Computational Intelligence and Soft Computing, vol. 2025, nº 1.
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Résumé
0is study presents ensemble machine learning (ML) models for predicting residential energy consumption in South Africa. By combining the best features of individual ML models, ensemble models reduce the drawbacks of each model and improve prediction accuracy. We present four ensemble models: ensemble by averaging (EA), ensemble by stacking each estimator (ESE), ensemble by boosting (EB), and ensemble by voting estimator (EVE). 0ese models are built on top of Random Forest (RF) and Decision Tree (DT). 0ese base predictor models leverage historical energy consumption patterns to capture temporal intricacies, including seasonal variations and rolling averages. In addition, we employed feature engineering methodologies to further enhance their predictive abilities. 0e accuracy of each ensemble model was evaluated by assessing various performance indicators, including the mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coe;cient of determination R2. Overall, the =ndings illustrate the e;ciency of ensemble learning models in providing accurate predictions for residential energy consumption. 0is study provides valuable insights for researchers and practitioners in predicting energy consumption in residential buildings and the bene=ts of using ensemble learning models in the building and energy research domains.
| Type de document: | Article publié dans une revue, révisé par les pairs |
|---|---|
| Professeur: | Professeur Abran, Alain |
| Affiliation: | Génie logiciel et des technologies de l'information |
| Date de dépôt: | 12 mai 2025 20:39 |
| Dernière modification: | 14 mai 2025 14:55 |
| URI: | https://espace2.etsmtl.ca/id/eprint/30926 |
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