New GenTL model significantly improves building thermal dynamics prediction

Researchers have developed GenTL, a novel transfer learning model designed to improve the accuracy of predicting building thermal dynamics. This model, pre-trained on a Long Short-Term Memory network using data from 450 buildings, aims to eliminate the need for selecting specific source buildings for fine-tuning. GenTL has demonstrated an average prediction error reduction of 42.1% across 144 target buildings compared to traditional single-source transfer learning methods. AI

IMPACT This research could lead to more data-efficient models for building control and fault detection, potentially reducing energy consumption.

RANK_REASON The cluster contains an academic paper detailing a new model and its performance evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• Fabian Raisch
• Hugging Face
• Long Short-Term Memory

• paper
• model release

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