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New GenTL model significantly improves building thermal dynamics prediction

By PulseAugur Editorial · [1 sources] ·

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 →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New GenTL model significantly improves building thermal dynamics prediction

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

  1. arXiv cs.LG TIER_1 English(EN) · Fabian Raisch, Thomas Krug, Christoph Goebel, Benjamin Tischler ·

    GenTL: A General Transfer Learning Model for Building Thermal Dynamics

    arXiv:2501.13703v2 Announce Type: replace-cross Abstract: Transfer Learning (TL) is an emerging field in modeling building thermal dynamics. This method reduces the data required for a data-driven model of a target building by leveraging knowledge from a source building. Conseque…

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