Adapting to Change: A Comparison of Continual and Transfer Learning for Modeling Building Thermal Dynamics under Concept Drifts

• URL: http://arxiv.org/abs/2508.21615v1
• Date: Fri, 29 Aug 2025 13:29:54 GMT
• Title: Adapting to Change: A Comparison of Continual and Transfer Learning for Modeling Building Thermal Dynamics under Concept Drifts
• Authors: Fabian Raisch, Max Langtry, Felix Koch, Ruchi Choudhary, Christoph Goebel, Benjamin Tischler,
• Abstract summary: Transfer Learning is the most effective approach for modeling building thermal dynamics when only limited data are available.<n>It remains unclear how to proceed after initial fine-tuning, as more operational measurement data are collected over time.<n>This study compares several CL and TL strategies, as well as a model trained from scratch, for thermal dynamics modeling during building operation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transfer Learning (TL) is currently the most effective approach for modeling building thermal dynamics when only limited data are available. TL uses a pretrained model that is fine-tuned to a specific target building. However, it remains unclear how to proceed after initial fine-tuning, as more operational measurement data are collected over time. This challenge becomes even more complex when the dynamics of the building change, for example, after a retrofit or a change in occupancy. In Machine Learning literature, Continual Learning (CL) methods are used to update models of changing systems. TL approaches can also address this challenge by reusing the pretrained model at each update step and fine-tuning it with new measurement data. A comprehensive study on how to incorporate new measurement data over time to improve prediction accuracy and address the challenges of concept drifts (changes in dynamics) for building thermal dynamics is still missing. Therefore, this study compares several CL and TL strategies, as well as a model trained from scratch, for thermal dynamics modeling during building operation. The methods are evaluated using 5--7 years of simulated data representative of single-family houses in Central Europe, including scenarios with concept drifts from retrofits and changes in occupancy. We propose a CL strategy (Seasonal Memory Learning) that provides greater accuracy improvements than existing CL and TL methods, while maintaining low computational effort. SML outperformed the benchmark of initial fine-tuning by 28.1\% without concept drifts and 34.9\% with concept drifts.

Related papers

• Modular Memory is the Key to Continual Learning Agents [100.09688599754465]
  We argue that combining the strengths of In-Weight Learning (IWL) and the newly emerged capabilities of In-Context Learning (ICL) through the design of modular memory is the missing piece for continual adaptation at scale.<n>We outline a conceptual framework for modular memory-centric architectures that leverage ICL for rapid adaptation and knowledge accumulation, and IWL for stable updates to model capabilities.
  arXiv  Detail & Related papers  (2026-03-02T11:40:05Z)
• From drift to adaptation to the failed ml model: Transfer Learning in Industrial MLOps [0.0]
  The flue gas differential pressure across the air preheater unit installed in a 660 MW thermal power plant is analyzed as a case study.<n> Updating the failed ANN model by three transfer learning techniques reveals that provides relatively higher predictive accuracy for the batch size of 5 days than those of LLTL and ALTL.
  arXiv  Detail & Related papers  (2026-02-01T01:32:26Z)
• Model Inversion with Layer-Specific Modeling and Alignment for Data-Free Continual Learning [19.12792297140574]
  Continual learning aims to incrementally train a model on a sequence of tasks while retaining performance on prior ones.<n> storing and replaying data is often infeasible due to privacy or security constraints.<n>We propose Per-layer Model Inversion (PMI), inspired by faster convergence in single-layer optimization.
  arXiv  Detail & Related papers  (2025-10-30T09:58:48Z)
• Round-trip Reinforcement Learning: Self-Consistent Training for Better Chemical LLMs [51.29260537017623]
  Large Language Models (LLMs) are emerging as versatile foundation models for computational chemistry.<n>These models often lack round-trip consistency.<n>We introduce Round-Trip Reinforcement Learning (RTRL), a novel framework that trains a model to improve its consistency.
  arXiv  Detail & Related papers  (2025-10-01T23:58:58Z)
• PTMs-TSCIL Pre-Trained Models Based Class-Incremental Learning [7.784244204592032]
  Class-incremental learning (CIL) for time series data faces challenges in balancing stability against catastrophic forgetting and plasticity for new knowledge acquisition.<n>We present the first exploration of PTM-based Time Series Class-Incremental Learning (TSCIL)
  arXiv  Detail & Related papers  (2025-03-10T10:27:21Z)
• What Do Learning Dynamics Reveal About Generalization in LLM Reasoning? [83.83230167222852]
  We find that a model's generalization behavior can be effectively characterized by a training metric we call pre-memorization train accuracy. By connecting a model's learning behavior to its generalization, pre-memorization train accuracy can guide targeted improvements to training strategies.
  arXiv  Detail & Related papers  (2024-11-12T09:52:40Z)
• SaRA: High-Efficient Diffusion Model Fine-tuning with Progressive Sparse Low-Rank Adaptation [52.6922833948127]
  In this work, we investigate the importance of parameters in pre-trained diffusion models.<n>We propose a novel model fine-tuning method to make full use of these ineffective parameters.<n>Our method enhances the generative capabilities of pre-trained models in downstream applications.
  arXiv  Detail & Related papers  (2024-09-10T16:44:47Z)
• SMILE: Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models [85.67096251281191]
  We present an innovative approach to model fusion called zero-shot Sparse MIxture of Low-rank Experts (SMILE) construction. SMILE allows for the upscaling of source models into an MoE model without extra data or further training. We conduct extensive experiments across diverse scenarios, such as image classification and text generation tasks, using full fine-tuning and LoRA fine-tuning.
  arXiv  Detail & Related papers  (2024-08-19T17:32:15Z)
• Analyzing and Exploring Training Recipes for Large-Scale Transformer-Based Weather Prediction [1.3194391758295114]
  We show that it is possible to attain high forecast skill even with relatively off-the-shelf architectures, simple training procedures, and moderate compute budgets. Specifically, we train a minimally modified SwinV2 transformer on ERA5 data, and find that it attains superior forecast skill when compared against IFS.
  arXiv  Detail & Related papers  (2024-04-30T15:30:14Z)
• Fast-Slow Test-Time Adaptation for Online Vision-and-Language Navigation [67.18144414660681]
  We propose a Fast-Slow Test-Time Adaptation (FSTTA) approach for online Vision-and-Language Navigation (VLN) Our method obtains impressive performance gains on four popular benchmarks.
  arXiv  Detail & Related papers  (2023-11-22T07:47:39Z)
• Consensus-Adaptive RANSAC [104.87576373187426]
  We propose a new RANSAC framework that learns to explore the parameter space by considering the residuals seen so far via a novel attention layer. The attention mechanism operates on a batch of point-to-model residuals, and updates a per-point estimation state to take into account the consensus found through a lightweight one-step transformer.
  arXiv  Detail & Related papers  (2023-07-26T08:25:46Z)
• Transfer Learning in Deep Learning Models for Building Load Forecasting: Case of Limited Data [0.0]
  This paper proposes a Building-to-Building Transfer Learning framework to overcome the problem and enhance the performance of Deep Learning models. The proposed approach improved the forecasting accuracy by 56.8% compared to the case of conventional deep learning where training from scratch is used.
  arXiv  Detail & Related papers  (2023-01-25T16:05:47Z)
• A Broad Ensemble Learning System for Drifting Stream Classification [3.087840197124265]
  We propose a Broad Ensemble Learning System (BELS) for stream classification with concept drift. BELS uses a novel updating method that greatly improves best-in-class model accuracy. We show that our proposed method improves on average 44% compared to BLS, and 29% compared to other competitive baselines.
  arXiv  Detail & Related papers  (2021-10-07T15:01:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.