深度学习在电力价格预测中的应用：对日前、日内和平衡电力市场的回顾
Deep Learning for Electricity Price Forecasting: A Review of Day-Ahead, Intraday, and Balancing Electricity Markets
AI总结：
本文综述了深度学习在日前、日内和平衡电力市场中的电力价格预测方法，提出统一的分类体系，揭示了概率、微观结构和市场感知设计的趋势，并指出现有研究的不足。
摘要：
电力价格预测（EPF）在电力系统运行和市场决策中起着关键作用。尽管现有综述研究提供了关于预测时间范围、市场机制和评估实践的有价值的见解，但深度学习的快速采用引入了越来越多的模型架构、输出结构和训练目标，这些仍缺乏深入分析。本文对深度学习在日前、日内和平衡市场中的EPF方法进行了结构化回顾。具体而言，我们介绍了一个统一的分类体系，将深度学习模型分解为骨干、头部和损失组件，为不同研究提供一致的评估视角。利用这一框架，我们分析了各市场中深度学习组件的最新趋势。本研究突出了向概率、微观结构导向和市场感知设计转变的趋势。我们进一步识别了文献中的关键不足，包括对日内和平衡市场关注有限以及需要市场特定的建模策略，从而帮助巩固和推进现有综述研究。
英文摘要：
Electricity price forecasting (EPF) plays a critical role in power system operation and market decision making. While existing review studies have provided valuable insights into forecasting horizons, market mechanisms, and evaluation practices, the rapid adoption of deep learning has introduced increasingly diverse model architectures, output structures, and training objectives that remain insufficiently analyzed in depth. This paper presents a structured review of deep learning methods for EPF in day-ahead, intraday, and balancing markets. Specifically, We introduce a unified taxonomy that decomposes deep learning models into backbone, head, and loss components, providing a consistent evaluation perspective across studies. Using this framework, we analyze recent trends in deep learning components across markets. Our study highlights the shift toward probabilistic, microstructure-centric, and market-aware designs. We further identify key gaps in the literature, including limited attention to intraday and balancing markets and the need for market-specific modeling strategies, thereby helping to consolidate and advance existing review studies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
