Probabilistic intraday electricity price forecasting is becoming increasingly important with the growth of renewable generation and the rise in demand-side engagement. Their uncertainties have increased the trading risks closer to delivery and the subsequent imbalance settlement costs. As a consequence, intraday trading has emerged to mitigate these risks. Unlike auction markets, intraday trading in many jurisdictions is characterized by the continuous posting of buy and sell orders on power exchange platforms. 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 full 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 the market price indices across high-liquidity (German) and low-liquidity (Austrian) markets demonstrate consistent improvements over conventional baselines, and ablation studies highlight the contributions of the main modeling components. The methodology is available at: https://runyao-yu.github.io/OrderFusion/.

翻译：随着可再生能源发电的增长和需求侧参与度的提高，日内电价概率预测正变得日益重要。其不确定性增加了临近交割时的交易风险及后续不平衡结算成本。因此，日内交易应运而生以缓解这些风险。与拍卖市场不同，许多司法管辖区的日内交易以电力交易平台上持续发布买卖订单为特征。这种动态订单簿微观价格形成机制给价格预测带来了特殊挑战。传统方法通过从买卖交易中聚合领域特征来表示订单簿，或将其视为多元时间序列，但此类表示忽略了订单簿完整的买卖交互结构。为此，本研究开发了一种新的订单融合方法，该方法是一种端到端且参数高效的概率预测模型，能够学习买卖动态的完整交互感知表示。此外，由于分位数交叉常是概率预测中的难题，本方法通过施加非交叉约束对分位数进行分层估计。在高流动性（德国）与低流动性（奥地利）市场的价格指数上进行的大量实验表明，该方法相较传统基线模型取得了持续改进，消融研究则凸显了各主要建模组件的贡献。该方法可通过以下网址获取：https://runyao-yu.github.io/OrderFusion/。