用不确定性感知的迁移学习，实现跨建筑节能预测，低数据需求下仍精准可靠。
Uncertainty-Aware Transfer Learning for Cross-Building Energy Forecasting: Toward Robust and Scalable District-Level Energy Management
- 基于TFT框架设计不确定性感知迁移学习，仅微调输出层参数即可高效跨建筑迁移。
- 在目标建筑上仅用少量数据，预测区间覆盖率接近95%（实际93.2%）。
- 提出通用的泛化质量指标TRI，适用于不同架构，指导实际城市级能源管理部署。
将数据驱动的节能预测扩展到区域层面，需要模型能在不同建筑间复用，且仅需极少目标域数据并提供可靠的不确定性估计。本文提出一种基于时序融合变换器（TFT）的不确定性感知迁移学习框架，用于跨建筑节能预测，在丹麦奥尔堡大学教育建筑（源域）和瑞士EMPA的多类型NEST建筑（目标域）的新发布高分辨率真实子计量数据集上进行评估。提出无需依赖具体架构的泛化质量度量指标——迁移鲁棒性指数（TRI）。四组无层冻结消融实验表明，仅微调455个输出层参数（总参数806K中），即可达到最佳迁移性能（TRI = 3,097），优于全量微调，说明TFT编码器能学习可迁移的时序表示。蒙特卡洛丢弃法实现93.2%的预测区间覆盖率，接近目标值95%。数据稀缺性分析显示，随着目标域数据增加，性能持续提升，为区域能源部署提供了实用指导。
原文摘要 · Abstract (English)
Scaling data-driven energy forecasting to district level requires models that can be re-used across buildings with minimal target-domain data and honest uncertainty estimates. We present an uncertainty-aware transfer learning (TL) framework for cross-building energy forecasting based on the Temporal Fusion Transformer (TFT), evaluated on a newly released high-resolution real sub-meter dataset: an educational building at Aalborg University, Denmark (source) and the multi-typology NEST building at EMPA, Switzerland (target). We introduce the Transfer Robustness Index (TRI), an architecture-agnostic metric for quantifying generalization quality across domain gaps. A four-strategy layer-freezing ablation shows that Probe-Only fine-tuning, updating only 455 output-layer parameters out of 806K, achieves the best transfer quality (TRI = 3,097), outperforming full fine-tuning and suggesting that TFT encoders learn transferable temporal representations. Monte Carlo Dropout yields a prediction interval coverage probability of 93.2%, close to the nominal 95% target. A data-scarcity analysis further shows monotonic improvement with increasing target-domain data, providing practical guidance for district energy deployment.
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。