用大模型实现零样本与少样本负荷预测，无需训练即可准确预测。
Zero and Few Shot Load Forecasting with Large Language Models
- 基于预训练大模型Chronos，直接用于负荷预测，无需微调。
- 在5个真实数据集上，误差降低7.34%至84.30%（RMSE等指标）。
- 适合数据稀缺场景，对新区域或新时段预测特别有效。
深度学习模型在负荷预测中表现优异，但通常需要大量数据训练后才能应用，限制了其在数据稀疏场景下的有效性。受预训练语言模型在自然语言处理中成功启发，本文提出一种基于先进大模型框架Chronos的零样本与少样本负荷预测方法。利用其丰富的预训练知识，Chronos在数据稀缺场景下仍可实现高精度预测。在五个真实世界数据集上的仿真结果表明，无论确定性还是概率性预测，在1至48小时的不同预测时长下，Chronos均显著优于九种主流基线模型。值得注意的是，相较于基线模型，Chronos将均方根误差（RMSE）、连续排名概率评分（CRPS）和分位数评分（QS）分别降低了7.34%–84.30%、19.63%–60.06%和22.83%–54.49%。这些结果凸显了Chronos模型的优越性与灵活性，使其成为数据稀缺场景下的有效解决方案。
原文摘要 · Abstract (English)
Deep learning models have shown strong performance in load forecasting, but they generally require large amounts of data for model training before being applied to new scenarios, which limits their effectiveness in data-scarce scenarios. Inspired by the great success of pre-trained language models (LLMs) in natural language processing, this paper proposes a zero and few shot load forecasting approach using an advanced LLM framework denoted as the Chronos model. By utilizing its extensive pre-trained knowledge, the Chronos model enables accurate load forecasting in data-scarce scenarios. Simulation results across five real-world datasets demonstrate that the Chronos model significantly outperforms nine popular baseline models for both deterministic and probabilistic load forecasting with various forecast horizons (e.g., 1 to 48 hours), even though the Chronos model is neither tailored nor fine-tuned to these specific load datasets. Notably, Chronos reduces root mean squared error (RMSE), continuous ranked probability score (CRPS), and quantile score (QS) by approximately 7.34%-84.30%, 19.63%-60.06%, and 22.83%-54.49%, respectively, compared to baseline models. These results highlight the superiority and flexibility of the Chronos model, positioning it as an effective solution in data-scarce scenarios.
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