GenTL无需选源建筑，可通用微调提升热建模精度。
GenTL: A General Transfer Learning Model for Building Thermal Dynamics
- 用450栋建筑数据预训练LSTM，构建通用模型
- 微调144栋目标建筑，平均误差降低42.1%
- 适合缺乏源建筑数据的智能楼宇应用
迁移学习（TL）是建模建筑热动态的新兴领域，通过利用源建筑知识减少目标建筑的数据需求，从而实现高效的数据驱动模型，可用于先进控制与故障诊断。然而，该方法在不同源建筑间表现不一，源建筑选择至关重要却仍具挑战。本文提出GenTL，一种适用于中欧独栋住宅的通用迁移学习模型。该模型在包含450栋建筑数据的长短期记忆网络（LSTM）上进行预训练，可高效微调至多种目标建筑。其通用性消除了对特定源建筑选择的需求，作为统一源用于微调。与传统单源到单目标迁移学习相比，验证表明该通用预训练方法更具有效性和可靠性。在144个目标建筑上的测试显示，相比单源微调模型，预测误差（RMSE）平均降低42.1%。
原文摘要 · Abstract (English)
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. Consequently, it enables the creation of data-efficient models that can be used for advanced control and fault detection & diagnosis. A major limitation of the TL approach is its inconsistent performance across different sources. Although accurate source-building selection for a target is crucial, it remains a persistent challenge. We present GenTL, a general transfer learning model for single-family houses in Central Europe. GenTL can be efficiently fine-tuned to a large variety of target buildings. It is pretrained on a Long Short-Term Memory (LSTM) network with data from 450 different buildings. The general transfer learning model eliminates the need for source-building selection by serving as a universal source for fine-tuning. Comparative analysis with conventional single-source to single-target TL demonstrates the efficacy and reliability of the general pretraining approach. Testing GenTL on 144 target buildings for fine-tuning reveals an average prediction error (RMSE) reduction of 42.1 % compared to fine-tuning single-source models.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。