Uncertainty-Aware Transfer Learning for Cross-Building Energy Forecasting: Toward Robust and Scalable District-Level Energy Management
arXiv:2605.29733v1 Announce Type: new Abstract: Scaling data-driven energy forecasting to district level requires models that can be re-used across buildings with minimal target-domain data and honest uncertainty estimates. We present an uncertainty-aware transfer learning (TL) framework for cross-building energy forecasting based on the Temporal Fusion Transformer (TFT), evaluated on a newly released high-resolution real sub-meter dataset: an educational building at Aalborg University, Denmark (source) and the multi-typology NEST building at EMPA, Switzerland (target). We introduce the Transf
The increasing complexity of energy grids and the proliferation of smart building data necessitate more robust and scalable forecasting models, especially as AI capabilities mature.
District-level energy management is crucial for grid stability, decarbonization efforts, and optimizing energy costs, making advances in this area strategically significant.
This framework provides a more reliable method for integrating AI into energy management across diverse building types with less data, reducing deployment friction and improving accuracy.
- · Smart Grid Operators
- · Energy Management Software Providers
- · Urban Planners
- · AI/ML Research Firms
- · Traditional Energy Forecasting Methods
- · Energy Inefficient Building Operators
More widespread adoption of AI-driven energy forecasting at the district and city levels becomes feasible.
Improved energy efficiency and reduced carbon emissions through optimized energy distribution and consumption across urban areas.
Enhanced grid resilience and accelerated transition to renewable energy sources due to better predictive capabilities and demand-side management.
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Read at arXiv cs.AI