OrderFusion: Encoding Orderbook for End-to-End Probabilistic Intraday Electricity Price Forecasting

Runyao Yu, Yuchen Tao, Fabian Leimgruber, Tara Esterl, Jochen Stiasny, Qingsong Wen, Hongye Guo, Jochen Lorenz Cremer

Keywords: Intraday Electricity Market, Orderbook, End-to-End, Probabilistic Forecasting

TL;DR: We propose OrderFusion, an end-to-end probabilistic model for accurate intraday electricity price forecasting.

Abstract: Probabilistic forecasting of intraday electricity prices is essential to manage market uncertainties. However, current methods rely heavily on domain feature extraction, which not only breaks the end-to-end learning paradigm but also hinders representation learning of buy–sell interactions from the orderbook, leading to inaccurate forecasts. Moreover, these methods often require training separate models for different quantiles, further violating the end-to-end principle and introducing the quantile crossing issue. Recent advances in time-series models have demonstrated promising performance in general forecasting tasks. However, these models lack inductive biases arising from buy–sell interactions and are thus parameter-heavy. To address these challenges, we propose an end-to-end probabilistic model called OrderFusion, which produces rich representations of buy–sell interactions, hierarchically estimates multiple quantiles via constrained residuals, and remains parameter-efficient with only 4,872 parameters. We conduct extensive experiments and ablation studies on widely used price indices (ID1, ID2, and ID3) using three years of orderbook in high-liquidity (German) and low-liquidity (Austrian) markets. The experimental results demonstrate that OrderFusion consistently outperforms multiple competitive baselines across markets, and ablation studies highlight the contribution of its individual components.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

Submission Number: 7402