非常激动人心：基于激励的广义迁移学习模型实现建筑零样本模型预测控制
Very Exciting: Zero-Shot Model Predictive Control of Buildings via Excitation-Based Generalized Transfer Learning Models
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- Technical University of Applied Sciences Rosenheim（罗森海姆应用技术大学）
- Technical University of Munich（慕尼黑工业大学）
- Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
- Alan Turing Institute（艾伦·图灵研究所）
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中文总结 AI 辅助
针对数据驱动建筑模型预测控制需大量数据训练的问题，提出基于激励数据的广义迁移学习模型，实现零样本控制，性能优于在线线性模型和PI控制器。
中文摘要 AI 辅助
数据驱动的节能模型预测控制（MPC）在建筑中的广泛采用，仍受制于为每栋建筑收集数据和训练模型的大量工作。因此，迁移学习（TL）在目标建筑建模中日益受到关注，因为它通过重用预训练的源模型，减少了数据需求和建模工作量。然而，这些TL模型通常仅在目标建筑上评估预测精度，而未测试下游控制性能。为弥补这一空白，我们将一种最先进的TL方法——使用标准运行数据在多个源建筑上预训练广义模型——应用于目标建筑的MPC设置中。我们表明，该方法不足以实现满意的控制性能。作为解决方案，我们引入了基于激励运行源数据（即有意探测输入以探索建筑状态-动作空间）预训练的广义模型。为进行评估，我们将广义模型零样本（即不进行微调）应用于32个模拟目标建筑，并评估MPC性能。结果表明，基于激励的广义模型在所有基准中实现了最强的控制性能，分别比基于在线线性模型的MPC和PI控制器高出6.4%和36.9%。通过将强大的控制性能与跨多建筑泛化能力相结合，且无需任何目标特定数据，我们的方法降低了MPC设置成本，并简化了其在建筑领域的广泛部署。
英文摘要
The widespread adoption of data-driven, energy-efficient model predictive control (MPC) in buildings remains hindered by substantial effort to collect data and train models for individual buildings. Transfer learning (TL) has consequently gained increasing attention for target building modeling, as it reduces data requirements and modeling effort by reusing pretrained source models. However, these TL models are typically evaluated only on prediction accuracy in the target, without testing downstream control performance. To address this gap, we apply a state-of-the-art TL approach - pretraining a generalized model on multiple source buildings using standard operational data - within an MPC setup in a target building. We show that this approach is insufficient to achieve satisfactory control performance. As a solution, we introduce generalized models pretrained on excitation-based operational source data - purposefully probed inputs that explore the building's state-action space. For evaluation, we apply the generalized models via zero-shot (i.e., without fine-tuning) to 32 simulated target buildings and assess MPC performance. Our results show that excitation-based generalized models achieve the strongest control performance among all benchmarks, outperforming an online linear model-based MPC and a PI controller by 6.4% and 36.9%, respectively. By combining strong control performance with the ability to generalize across multiple buildings, without requiring any target-specific data, our approach reduces MPC setup cost and simplifies its widespread deployment in the building sector.
