Networked Spatial Effects in European Electricity Price Forecasting

2026-06-05

AI Summary

This study addresses the challenge of forecasting day-ahead electricity prices in Europe, where cross-border transmission networks induce complex spatial dependencies that conventional localized models fail to capture. To overcome this limitation, the authors propose a Networked Spatio-Temporal Model (NSTM), which— for the first time—integrates a metric graph–based neighborhood structure into price prediction. The approach maps irregular bidding zones onto an ordered network representation, effectively incorporating heterogeneous features including grid topology, autoregressive components, inter-temporal and seasonal dynamics, as well as fuel and emissions prices within a high-dimensional streaming forecasting framework. Empirical evaluation across 39 major European bidding zones demonstrates that NSTM significantly outperforms traditional localized models, underscoring the critical role of grid topology in information propagation and transcending the paradigm of isolated regional modeling.

Abstract

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.

Problem

• electricity price forecasting
• spatial effects
• networked markets
• bidding zones
• interconnected systems

Innovation

• Networked Spatio-Temporal Model
• metric graph
• spatial interconnection
• day-ahead electricity price forecasting
• bidding zones