Uncertainty-Aware Transfer Learning for Cross-Building Energy Forecasting: Toward Robust and Scalable District-Level Energy Management

Researchers developed an uncertainty-aware transfer learning framework using Temporal Fusion Transformers to enable energy forecasting models trained on one building to work effectively on different buildings with minimal retraining. The approach achieved 93.2% prediction interval coverage and demonstrated that freezing most model parameters while fine-tuning only output layers produces superior cross-building generalization compared to full model retraining.

This research addresses a critical bottleneck in scaling smart grid and district energy management systems: the cost and complexity of deploying machine learning models across heterogeneous building portfolios. Traditional approaches require retraining models extensively for each new building, consuming time and computational resources. The study demonstrates that modern transformer architectures encode reusable temporal patterns that transfer effectively across different building types and operational contexts, suggesting a path toward plug-and-play energy forecasting solutions.

The introduction of the Transfer Robustness Index provides the industry with a quantifiable metric for evaluating model generalization—a previously unstructured evaluation domain. By testing on real sub-meter data from different European buildings, the authors move beyond synthetic benchmarks to validate practical applicability. The finding that Probe-Only fine-tuning (adjusting just 455 of 806K parameters) outperforms full model retraining challenges conventional machine learning wisdom and indicates that energy consumption patterns follow universal temporal principles exploitable across domains.

For utilities and energy management firms, this research accelerates deployment timelines and reduces implementation costs. District-level energy management systems can now leverage pre-trained models developed on any building, requiring only minimal calibration data from target sites. The Monte Carlo Dropout uncertainty quantification at 93.2% coverage probability provides operators with statistically reliable confidence intervals for operational decisions, reducing forecast-driven risks.

The monotonic improvement with increasing target-domain data offers practical guidance for phased deployments, allowing organizations to implement systems incrementally while gathering data to refine predictions. Future work should explore model performance across diverse climate zones and building archetypes to validate generalization boundaries.

• →Probe-Only fine-tuning outperforms full retraining by achieving Transfer Robustness Index of 3,097 while updating only 0.06% of parameters
• →Monte Carlo Dropout uncertainty quantification achieves 93.2% prediction interval coverage, meeting reliability standards for operational deployment
• →Transfer learning effectiveness increases monotonically with target-domain data, enabling phased district-level energy system rollouts
• →Temporal Fusion Transformers learn universally transferable temporal patterns applicable across different building types and regions
• →New Transfer Robustness Index metric enables standardized evaluation of model generalization across domain gaps in energy forecasting