Paper: arXiv 2510.12685

Authors: Runyao Yu, Ruochen Wu, Yongsheng Han, Jochen L. Cremer

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.

Complexity vs Empirical Score

  • Math Complexity: 5.0/10
  • Empirical Rigor: 8.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents advanced statistical modeling with specific loss functions and regularization for quantile forecasting, but its core contribution is a rigorous, multi-country/multi-product empirical benchmark with systematic generalization tests using real market data and comprehensive hyperparameter tuning.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Assess orderbook features &amp; generalization<br>in intraday electricity forecasting"] --> B["Data Collection &amp; Feature Extraction<br>Germany &amp; Austria: 60-min &amp; 15-min products"]
  B --> C["Feature Selection<br>Identify top 384 orderbook features"]
  C --> D["Model Benchmarking<br>Classical ML, Tree-based, Deep Learning"]
  D --> E["Generalization Study<br>Train on Country A / Test on Country B"]
  E --> F["Key Findings<br>1. Orderbook features significantly improve accuracy<br>2. Asymmetric generalization observed<br>3. 60-min &gt; 15-min generalization"]