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

2026-05-28 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Energy Forecasting · Depth: Advanced, quick

Summary

A new uncertainty-aware transfer learning (TL) framework has been developed for cross-building energy forecasting, leveraging the Temporal Fusion Transformer (TFT). This framework addresses the need for scalable district-level energy management by enabling model reuse across buildings with minimal target-domain data and providing reliable uncertainty estimates. Evaluated using high-resolution sub-meter datasets from an educational building at Aalborg University, Denmark (source) and the multi-typology NEST building at EMPA, Switzerland (target), the framework introduces the Transfer Robustness Index (TRI) to quantify generalization quality. A layer-freezing ablation study revealed that Probe-Only fine-tuning, which updates only 455 out of 806K output-layer parameters, achieved the optimal transfer quality with a TRI of 3,097. Furthermore, Monte Carlo Dropout delivered a prediction interval coverage probability of 93.2%, closely aligning with the 95% nominal target, and a data-scarcity analysis confirmed improved performance with increased target-domain data.

Key takeaway

For Machine Learning Engineers scaling energy forecasting models across diverse buildings, consider implementing uncertainty-aware transfer learning with Temporal Fusion Transformers. Your team should prioritize Probe-Only fine-tuning, updating only 455 output-layer parameters, to achieve optimal transfer quality and reduce data requirements. Additionally, integrate Monte Carlo Dropout to provide robust prediction intervals, enhancing the reliability of your district-level energy management systems.

Key insights

Uncertainty-aware transfer learning with TFT enables robust cross-building energy forecasting using minimal target data.

Principles

• TFT encoders learn transferable temporal representations.
• Minimal fine-tuning (Probe-Only) can optimize transfer quality.

Method

The framework uses an uncertainty-aware transfer learning approach with Temporal Fusion Transformer (TFT). It employs Probe-Only fine-tuning, updating only the output layer, and Monte Carlo Dropout for uncertainty quantification.

In practice

• Apply Probe-Only fine-tuning for efficient TFT transfer.
• Use Monte Carlo Dropout for reliable uncertainty estimates.

Topics

• Energy Forecasting
• Transfer Learning
• Temporal Fusion Transformer
• Uncertainty Quantification
• District Energy Management
• Probe-Only Fine-tuning

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.