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: models trained on more liquid markets or products transfer well to less liquid ones, whereas the reverse transfer leads to substantial performance degradation.
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={2026},
eprint={2510.12685},
archivePrefix={arXiv},
primaryClass={q-fin.CP},
url={https://arxiv.org/abs/2510.12685},
}