Orderbook Feature Learning and
Asymmetric Generalization in
Intraday Electricity Markets

Runyao Yu1,2, Ruochen Wu1, Yongsheng Han1, Jochen L. Cremer1,2
1 Delft University of Technology, 2 Austrian Institute of Technology
Preprint (under review)
Code Paper
Teaser Image

Teaser figure: The impact of market liquidity on model performance. (a) Comparison of market liquidity. (b) Loss ratio versus trade-count ratio.

Abstract

Accurate probabilistic forecasting of intraday electricity prices is critical for market participants to inform trading decisions. Existing studies rely on specific domain features, such as Volume-Weighted Average Price (VWAP) and the last price. However, the rich information in the orderbook remains underexplored. Furthermore, these approaches are often developed within a single country and product type, making it unclear whether the approaches are generalizable. In this paper, we extract 384 features from the orderbook and identify a set of powerful features via feature selection. Based on selected features, we present a comprehensive benchmark using classical statistical methods, tree-based ensembles, and deep learning models across two countries (Germany and Austria) and two product types (60-min and 15-min). We further perform a systematic generalization study across countries and product types, from which we reveal an asymmetric generalization phenomenon.

Teaser Image

Overview of the three phases in our study: Feature Engineering, Model Optimization, and Generalization Analysis.

Teaser Image

Visualization of 60-min and 15-min ID3 from Germany and Austria. (a)--(d) Histograms of ID3. The price indices exhibit high skewness and dispersion during the energy crisis in 2022, gradually reverting to a more stable distribution in 2023 and 2024. (e)--(f) ID3 trajectories in 2024 (range limited to [-500, 1000] €/MWh for better visual comparison). Volatility increases in the order: (AT, 60-min), (DE, 60-min), (AT, 15-min), (DE, 15-min).

BibTeX

If you use our code or find our paper useful, please cite:

@misc{yu2025orderbookfeaturelearningasymmetric,
      title={Orderbook Feature Learning and Asymmetric Generalization in Intraday Electricity Markets}, 
      author={Runyao Yu and Ruochen Wu and Yongsheng Han and Jochen L. Cremer},
      year={2025},
      eprint={2510.12685},
      archivePrefix={arXiv},
      primaryClass={q-fin.CP},
      url={https://arxiv.org/abs/2510.12685}, 
}