OrderFusion：用于端到端概率日内电价预测的订单簿编码
OrderFusion: Encoding Orderbook for End-to-End Probabilistic Intraday Electricity Price Forecasting
AI总结：
本文提出OrderFusion方法，通过端到端概率模型学习订单簿的交互结构，改进日内电价预测，通过分层量化估计避免量化交叉问题。
摘要：
概率日内电价预测对短时电力系统运行日益重要。随着可再生能源发电、需求侧灵活性和储能资产的增长，市场参与者需要在不确定性下更接近交付时间调整位置。连续日内（CID）市场通过提供更新的价格信号支持这一过程，帮助参与者管理不平衡暴露和运营风险。不同于拍卖市场，许多司法管辖区的CID交易以持续发布买入和卖出订单为特征。这种动态订单簿的微观结构对价格预测提出了特殊挑战。传统方法通过聚合买卖交易的领域特征或将其视为多变量时间序列来表示订单簿，但这些表示方法忽略了订单簿的完整买卖交互结构。因此，本文开发了一种新的订单融合方法，这是一种端到端且参数高效的概率预测模型，学习了交互感知的买卖动态表示。此外，由于概率预测中量化交叉问题常常存在，该方法通过非交叉约束分层估计量化。在高流动性与低流动性欧洲市场上的CID价格指数上进行的广泛实验显示，与传统基线相比，有持续改进，消融研究突显了主要组件的贡献。该方法的实现详见：https://runyao-yu.github.io/OrderFusion/.
英文摘要：
Probabilistic intraday electricity price forecasting is becoming increasingly important for short-term power-system operation. With increasing renewable generation, demand-side flexibility, and storage assets, market participants need to adjust their positions under uncertainty closer to delivery. Continuous intraday (CID) markets support this process by providing updated price signals, helping participants manage imbalance exposure and operational risk. Unlike auction markets, CID trading in many jurisdictions is characterized by the continuous posting of buy and sell orders. This dynamic orderbook microstructure of price formation presents special challenges for price forecasting. Conventional methods represent the orderbook via domain features aggregated from buy and sell trades, or by treating it as a multivariate time series, but such representations neglect the full buy-sell interaction structure of the orderbook. This research therefore develops a new order fusion methodology, which is an end-to-end and parameter-efficient probabilistic forecasting model that learns a interaction-aware representation of the buy-sell dynamics. Furthermore, as quantile crossing is often a problem in probabilistic forecasting, this approach hierarchically estimates the quantiles with non-crossing constraints. Extensive experiments on CID price indices across high- and low-liquidity European markets demonstrate consistent improvements over conventional baselines, and ablation studies highlight the contributions of the main components.The methodology is available at: https://runyao-yu.github.io/OrderFusion/.
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。
