标题： 基于时间序列基础模型的建筑能源系统概率预测 显示英文标题

标题： Probabilistic Forecasting for Building Energy Systems using Time-Series Foundation Models

摘要： 建筑能源系统中的决策严重依赖于相关时间序列模型的预测准确性。在缺乏目标建筑广泛数据的情况下，基础模型（FMs）代表了一种有前景的技术，能够利用从大规模和多样化的预训练数据集中获得的先验知识，构建用于决策工具的准确概率预测器。本文研究了时间序列基础模型（TSFMs）在建筑能耗预测中的适用性和微调策略。我们通过使用商业净零能耗建筑的真实数据，分析了全量微调和参数高效微调方法，特别是低秩适应（LoRA）。这些数据捕获了诸如房间占用、碳排放、插座负荷以及暖通空调能耗等信号。我们的分析表明，TSFMs的零样本预测性能通常不够理想。为了解决这一不足，我们证明了采用全量微调或参数高效微调可以显著提高预测精度，即使是在有限的历史数据情况下。值得注意的是，采用低秩适应（LoRA）进行微调大幅降低了计算成本，而不会牺牲准确性。此外，在各种建筑区域和季节条件下，微调后的TSFMs在准确性、鲁棒性和泛化能力方面始终优于最先进的深度预测模型（如时序融合变换器）。这些结果强调了TSFMs在实际且数据受限的建筑能源管理系统中的有效性，从而促进了在追求能源效率和可持续性方面的改进决策。 显示英文摘要

摘要： Decision-making in building energy systems critically depends on the predictive accuracy of relevant time-series models. In scenarios lacking extensive data from a target building, foundation models (FMs) represent a promising technology that can leverage prior knowledge from vast and diverse pre-training datasets to construct accurate probabilistic predictors for use in decision-making tools. This paper investigates the applicability and fine-tuning strategies of time-series foundation models (TSFMs) in building energy forecasting. We analyze both full fine-tuning and parameter-efficient fine-tuning approaches, particularly low-rank adaptation (LoRA), by using real-world data from a commercial net-zero energy building to capture signals such as room occupancy, carbon emissions, plug loads, and HVAC energy consumption. Our analysis reveals that the zero-shot predictive performance of TSFMs is generally suboptimal. To address this shortcoming, we demonstrate that employing either full fine-tuning or parameter-efficient fine-tuning significantly enhances forecasting accuracy, even with limited historical data. Notably, fine-tuning with low-rank adaptation (LoRA) substantially reduces computational costs without sacrificing accuracy. Furthermore, fine-tuned TSFMs consistently outperform state-of-the-art deep forecasting models (e.g., temporal fusion transformers) in accuracy, robustness, and generalization across varying building zones and seasonal conditions. These results underline the efficacy of TSFMs for practical, data-constrained building energy management systems, enabling improved decision-making in pursuit of energy efficiency and sustainability.