对比不同建筑类型数据对能耗预测模型的影响，发现多样性比参数量更重要。
From RNNs to Foundation Models: An Empirical Study on Commercial Building Energy Consumption
- 用相同规模但建筑类型不同的子集测试多种模型
- 模型架构和数据多样性影响远大于参数数量
- 微调大模型在性能上可媲美从零训练的基线模型
商业建筑短期能耗精准预测对智能电网运行至关重要。尽管智能电表和深度学习模型可利用多栋建筑的历史数据进行预测，但不同建筑间的数据异质性会降低模型性能。在保持数据集大小和模型规模不变的前提下，研究数据异质性对时间序列预测的影响尚不充分。本文基于ComStock数据集，该数据集提供美国商业建筑的合成能耗数据。采用两个规模与区域相同但建筑类型多样性不同的子集，评估多种时间序列预测模型的表现，包括微调的开源基础模型（FMs）。结果表明，数据异质性和模型架构对微调后预测性能的影响，远超过参数量的影响。此外，尽管计算成本更高，微调的基础模型在性能上仍可媲美从头训练的基线模型。
原文摘要 · Abstract (English)
Accurate short-term energy consumption forecasting for commercial buildings is crucial for smart grid operations. While smart meters and deep learning models enable forecasting using past data from multiple buildings, data heterogeneity from diverse buildings can reduce model performance. The impact of increasing dataset heterogeneity in time series forecasting, while keeping size and model constant, is understudied. We tackle this issue using the ComStock dataset, which provides synthetic energy consumption data for U.S. commercial buildings. Two curated subsets, identical in size and region but differing in building type diversity, are used to assess the performance of various time series forecasting models, including fine-tuned open-source foundation models (FMs). The results show that dataset heterogeneity and model architecture have a greater impact on post-training forecasting performance than the parameter count. Moreover, despite the higher computational cost, fine-tuned FMs demonstrate competitive performance compared to base models trained from scratch.
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