As European bidding zones are highly interconnected by physical transmission lines, spatial influences propagate across neighboring nodes through a network. It is reflected in the day-ahead electricity prices across European bidding zones, as the auction algorithm also uses information beyond each bidding zone's geographic boundary. To capture how this interconnection affects the electricity prices in neighboring bidding zones, we have used a metric graph to map the spatial coverage of information using a well-defined neighborhood measure. We propose the Networked Spatio-Temporal Model (NSTM), which maps irregular spatial nodes into an ordered network, enabling the systematic incorporation of neighborhood information. We implement the NSTM across 39 bidding zones covering the majority of European electricity markets in a high-resolution, streaming-forecasting setup. The model uses autoregressive, cross-hour, and seasonal effects, along with fuel and emission prices and day-ahead forecasts of fundamentals, as interconnected information to predict the day-ahead prices for each bidding zone. A Europe-wide study presented in this paper shows that the NSTM consistently outperforms traditional island-based pure local models. This paper provides a framework that demonstrates the critical role the networked structure plays in propagating information across interconnected markets and its vast implications for day-ahead electricity price forecasting.

翻译：欧洲竞价区域通过物理输电线路高度互联，空间影响通过网络在相邻节点间传播。这种效应反映在跨欧洲竞价区域的日前电价中，因为拍卖算法也会利用各竞价区域地理边界以外的信息。为捕捉这种互联关系对相邻竞价区域电价的影响，我们采用度量图，通过定义明确的邻域度量来映射信息的空间覆盖范围。我们提出网络时空模型（NSTM），该模型将不规则空间节点映射为有序网络，从而实现邻域信息的系统性整合。我们在覆盖欧洲大多数电力市场的39个竞价区域上，以高分辨率流式预测框架实施NSTM。该模型利用自回归、跨小时和季节效应，结合燃料与碳排放价格以及基础要素的日前预测，作为互联信息来预测每个竞价区域的日前电价。本文的泛欧研究表明，NSTM始终优于传统孤岛式纯局部模型。本文提供的框架揭示了网络化结构在跨互联市场信息传播中的关键作用，及其对日前电价预测的深远影响。