Enterprise AI Analysis
A machine learning ensemble framework based on a clustering algorithm for improving electric power consumption performance
This study presents an innovative machine learning ensemble framework that integrates clustering algorithms with ML models to enhance electric power consumption prediction accuracy in residential buildings. Focusing on Korean apartments, the framework uses four evaluation methods (Elbow-Method, Silhouette Score, Calinski-Harabasz Index, and Dunn Index) across five data collection intervals to identify optimal clustering conditions. Five ML models (CatBoost, Decision Tree, LightGBM, Random Forest, XGBoost) were evaluated for prediction performance across identified clusters. The study found that optimal clustering resulted in two clusters (142 houses for C0, 206 houses for C1) using monthly resampled power data, with CatBoost and LightGBM showing the highest average prediction performance. Four ensemble models were developed based on these top-performing models, and statistical analysis confirmed their significant outperformance of traditional ML approaches without clustering (p < 0.05 or 0.01). This methodology accounts for unique consumption patterns in each house, contributing to more accurate energy consumption prediction and supporting effective energy reduction strategies.
Executive Impact at a Glance
Our analysis highlights key performance indicators demonstrating the tangible benefits for enterprise energy management.
~0.95
Prediction Accuracy (R²)
55.6%
MAE Reduction
80.1%
MSE Reduction
59.3%
RMSE Reduction
Deep Analysis & Enterprise Applications
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The study utilized 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) to determine optimal clustering conditions. Monthly resampled data yielded the most stable and valid clusters. This robust approach ensures that the clustering effectively identifies unique consumption patterns.
2
Optimal Clusters Identified for Monthly Data
The rigorous validation process, using multiple metrics and stability checks, converged on 2 clusters as optimal for monthly aggregated data, balancing granularity and stability.
Clustering Optimization Process
Data Preprocessing & Resampling
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K-Means Clustering (K=2-10)
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Validity & Stability Assessment (4 Metrics)
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Optimal (Interval, K) Selection
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Cluster-Specific Model Training
Five ML models (CatBoost, Decision Tree, LightGBM, Random Forest, XGBoost) were assessed. CatBoost and LightGBM consistently showed the highest prediction performance within each cluster. These top models were then amalgamated into ensemble models, significantly outperforming traditional single-model approaches without clustering.
| Model | Traditional ML (R²) | Clustering-based Ensemble (R²) |
|---|---|---|
| CatBoost | 0.914 | 0.940 (CB-CB) |
| LightGBM | 0.926 | 0.948 (LGBM-LGBM) |
| XGBoost | 0.920 | 0.945 (CB-LGBM) |
| Random Forest | 0.885 | N/A (Excluded) |
| Decision Tree | 0.822 | N/A (Excluded) |
| Note: Ensemble models significantly improved R² scores compared to traditional ML without clustering (p < 0.05). | ||
The proposed framework provides a practical and accurate method for predicting energy consumption in residential buildings, specifically in Korean apartment complexes. By accounting for unique consumption patterns, it supports better energy management, demand-side management, and targeted interventions for energy reduction.
Optimized Energy Management for Korean Apartments
Challenge: Heterogeneous electricity consumption patterns in residential buildings in metropolitan Korea, influenced by distinct seasonal variations and diverse household behaviors, made accurate prediction difficult, hindering effective energy management and reduction efforts.
Solution: Implemented a clustering-based ML ensemble framework to identify unique consumption patterns and apply cluster-specific predictive models. This approach dynamically adapts to household variability and climatic conditions.
Outcome: Achieved a ~0.95 R² score in total energy consumption prediction, with significant reductions in MAE, MSE, and RMSE. This enabled more precise demand forecasting, optimized HVAC scheduling, and informed policy design for energy efficiency initiatives, leading to substantial energy cost savings and carbon emission reductions.
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Your AI Implementation Roadmap
A typical phased approach to integrate this advanced AI solution into your operations.
Phase 1: Data Acquisition & Preprocessing
Secure and integrate smart-meter data (10-min intervals) with external weather data. Perform robust quality audit, outlier removal, and two-stage imputation to ensure data completeness and integrity for analysis.
Duration: 1-2 Weeks
Phase 2: Clustering Optimization
Apply K-Means clustering across various time resolutions (10-min to 1-month) and 'K' values (2-10). Utilize multi-metric validation (Elbow, Silhouette, CHI, Dunn) and stability checks to identify optimal clustering conditions and generate distinct household segments.
Duration: 2-3 Weeks
Phase 3: Model Training & Ensemble Development
Train and tune multiple ML models (CatBoost, LightGBM, XGBoost, etc.) for each identified cluster using rolling-origin cross-validation. Construct ensemble models by combining the best-performing predictors from each cluster for complex-level forecasting.
Duration: 3-4 Weeks
Phase 4: Validation & Deployment
Evaluate ensemble model performance against non-clustered baselines using MAE, MSE, RMSE, and R² metrics with statistical tests. Integrate the validated model into existing energy management systems for real-time prediction and operational decision-making.
Duration: 1-2 Weeks
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