AI framework enhances cross-building energy forecasting with transfer learning

Researchers have developed a new transfer learning framework for energy forecasting across different buildings, utilizing the Temporal Fusion Transformer (TFT). This approach aims to improve scalability and robustness for district-level energy management by requiring minimal target-domain data and providing reliable uncertainty estimates. The framework demonstrated strong performance, with a specific fine-tuning strategy outperforming full model retraining and Monte Carlo Dropout effectively estimating prediction intervals. AI

IMPACT This research could lead to more efficient and scalable energy management systems by enabling AI models to adapt to new buildings with less data.

RANK_REASON The cluster contains a research paper detailing a novel AI framework for energy forecasting. [lever_c_demoted from research: ic=1 ai=1.0]

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• Aalborg University
• Monte Carlo Dropout
• Shadmehr Zaregarizi
• Temporal Fusion Transformer
• Transfer Robustness Index

• paper
• infra

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