A Hybrid Two-Stage Ensemble Framework for Detecting and Quantifying Energy Flexibility in Buildings: A Solution to the FlexTrack Challenge
A Hybrid Two-Stage Ensemble Framework for Detecting and Quantifying Energy Flexibility in Buildings: A Solution to the FlexTrack Challenge
Abstract: Accurate Measurement and Verification (M&V) of demand response (DR) is essential for integrating flexible building loads into the power grid. Addressing the FlexTrack 2025 Challenge, this paper details a hybrid two-stage ensemble framework to classify DR events and quantify their energy impact. The first stage uses a gradient boosting ensemble to predict the Demand Response Flag, identifying the building’s operational state. This classification is then fed as a key feature into a second-stage regression ensemble that estimates the Demand Response Capacity. The solution’s novelty lies in its hierarchical structure, which combines a general-purpose global model with specialized models trained on data-driven site archetypes for robust generalization. This methodology is underpinned by extensive feature engineering to capture complex temporal and weather-related dynamics. On the private test set, the solution achieved a Geometric-Mean Score of 0.618 for classification and a normalized Mean Absolute Error (nMAE) of 0.991 and normalized Root Mean Square Error (nRMSE) of 1.223 for regression. These results demonstrate the effectiveness of a decoupled, multi-model approach in tackling the complex challenge of DR baselining and provide a scalable framework for automated M&V systems. --- About this Record: This Zenodo record provides a permanent, citable archive of the work described in the abstract. It contains: The full academic paper (Paper.pdf). The reproducible Python source code (flextrack_pipeline.py) and Jupyter Notebook. The code is also available on GitHub: DanGlChris - FlexTrack A Kaggle Notebook implementation is available here: FlexTrack 2025 Hybrid Ensemble Solution Data Access: The required data files are not included in this archive but can be downloaded directly from the FlexTrack Challenge on the AICrowd platform.
Machine Learning, Building Energy Flexibility, FOS: Electrical engineering, electronic engineering, information engineering, Building Engineering, Energy Engineering, Measurement and Verification, Electrical engineering, electronic engineering, information engineering, Demand Response, Ensemble Methods, Building Energy Management
Machine Learning, Building Energy Flexibility, FOS: Electrical engineering, electronic engineering, information engineering, Building Engineering, Energy Engineering, Measurement and Verification, Electrical engineering, electronic engineering, information engineering, Demand Response, Ensemble Methods, Building Energy Management
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