用于电价预测与电池套利的基础模型：它们能否替代特定市场的预测模型？

中文摘要 AI 辅助

基础模型有望在极少或无需特定任务训练的情况下提供准确预测，但它们能否替代专门设计用于电价预测的模型仍不明确。我们对比了来自5个基础模型家族的9种变体，这些变体以零样本模式在2021-2025年期间的德国、波兰和西班牙三个市场上，与两种最先进的电价预测基准进行评估。我们从点预测与概率预测准确性，以及电池储能套利的经济价值这几个方面评估它们的性能。仅TabPFN模型在所有三个市场和所有统计指标上都始终显著优于基准。然而，这种统计优势并未直接转化为经济优势：TabPFN在无限制报价和风险较高的分位数策略下表现最佳，而Distributional Deep Neural Network基准在风险容忍度较低时盈利更高。因此，基础模型无法普遍替代特定市场的模型，其价值取决于模型架构和决策问题。

英文摘要

Foundation models promise accurate forecasts with little or no task-specific training, but whether they can replace models designed specifically for electricity price forecasting remains unclear. We compare nine variants from five foundation model families, evaluated in zero-shot mode, with two state-of-the-art electricity price forecasting benchmarks in Germany, Poland, and Spain over 2021-2025. Their performance is assessed in terms of point and probabilistic forecasting accuracy, as well as economic value in battery energy storage arbitrage. Only the TabPFN models consistently and significantly outperform the benchmarks across all three markets and all statistical measures. However, this statistical dominance does not translate directly into economic dominance: TabPFN performs best under unlimited bids and riskier quantile-based strategies, whereas the Distributional Deep Neural Network benchmark is more profitable when risk tolerance is lower. Thus, foundation models cannot universally replace market-specific models, and their value depends on both model architecture and the decision problem.

发表机构

• Wrocław University of Science and Technology（弗罗茨瓦夫科技大学）
• Aarhus University（奥胡斯大学）

机构由 AI 辅助整理，请以论文原文为准。