Orderbook Feature Learning and Asymmetric Generalization in Intraday Electricity Markets

Runyao Yu, Ruochen Wu, Yongsheng Han and Jochen L. Cremer

Papers from arXiv.org

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 models, 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.

Date: 2025-10
New Economics Papers: this item is included in nep-ene and nep-for
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