用迁移学习提升建筑热模型参数估计精度，无需初始猜测。
Transfer Learning for Neural Parameter Estimation applied to Building RC Models
- 先预训练后微调，利用跨系统知识提升参数估计
- 仅需12天数据即提升18.6%-24.0%，72天达49.4%性能增益
- 适用于建筑热模型等动态系统，适合工程与研究者参考
动态系统参数估计因非凸性和对初值敏感而困难。近年深度学习方法虽实现快速高精度估计，却未利用系统间可迁移知识。为此，本文提出基于预训练-微调范式的迁移学习神经参数估计框架，显著提升精度并消除初值依赖。在建筑RC热模型上评估，对比遗传算法与从零训练的神经基线，在八座模拟建筑、一座真实建筑、两种RC模型配置及四种训练数据长度下进行测试。结果表明，仅需12天训练数据即可实现18.6%-24.0%性能提升，72天时最高达49.4%。该方法为动态系统参数估计提供了新范式。
原文摘要 · Abstract (English)
Parameter estimation for dynamical systems remains challenging due to non-convexity and sensitivity to initial parameter guesses. Recent deep learning approaches enable accurate and fast parameter estimation but do not exploit transferable knowledge across systems. To address this, we introduce a transfer-learning-based neural parameter estimation framework based on a pretraining-fine-tuning paradigm. This approach improves accuracy and eliminates the need for an initial parameter guess. We apply this framework to building RC thermal models, evaluating it against a Genetic Algorithm and a from-scratch neural baseline across eight simulated buildings, one real-world building, two RC model configurations, and four training data lengths. Results demonstrate an 18.6-24.0% performance improvement with only 12 days of training data and up to 49.4% with 72 days. Beyond buildings, the proposed method represents a new paradigm for parameter estimation in dynamical systems.
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