A machine learning ensemble framework based on a clustering algorithm for improving electric power consumption performance.

ABSTRACT: Accurate prediction of electric energy consumption is critical for both user convenience and supplier efficiency. This study introduces an ensemble approach that integrates clustering algorithms with machine learning (ML) models to enhance prediction accuracy by identifying consumption patterns within buildings. The research focused on residential apartments in the metropolitan area of Korea, utilizing four evaluation methods (Elbow-Method, Silhouette Score, Calinski-Harabasz Index, and Dunn Index) across five data collection intervals (10 min, 1 h, 1 day, 1 week, and 1 month). Five ML models (CatBoost, Decision Tree, LightGBM, Random Forest, XGBoost) were assessed for their prediction performance across clusters. Additionally, ML models that exhibited high performance within each cluster

SUBMITTER: Sim T 

PROVIDER: S-EPMC12623421 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature