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

Paper: arXiv 2502.06830

Abstract

Probabilistic intraday electricity price forecasting is becoming increasingly important with the growth of renewable generation and the rise in demand-side engagement. Their uncertainties have increased the trading risks closer to delivery and the subsequent imbalance settlement costs. As a consequence, intraday trading has emerged to mitigate these risks. Unlike auction markets, intraday trading in many jurisdictions is characterized by the continuous posting of buy and sell orders on power exchange platforms. This dynamic orderbook microstructure of price formation presents special challenges for price forecasting. Conventional methods represent the orderbook via domain features aggregated from buy and sell trades, or by treating it as a multivariate time series, but such representations neglect the full buy-sell interaction structure of the orderbook. This research therefore develops a new order fusion methodology, which is an end-to-end and parameter-efficient probabilistic forecasting model that learns a full interaction-aware representation of the buy-sell dynamics. Furthermore, as quantile crossing is often a problem in probabilistic forecasting, this approach hierarchically estimates the quantiles with non-crossing constraints. Extensive experiments on the market price indices across high-liquidity (German) and low-liquidity (Austrian) markets demonstrate consistent improvements over conventional baselines, and ablation studies highlight the contributions of the main modeling components. The methodology is available at: https://runyao-yu.github.io/OrderFusion/.

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced deep learning architectures (e.g., attention-based encoders) and formal probabilistic modeling with quantile constraints, representing high mathematical complexity. It also demonstrates strong empirical rigor through extensive experiments on real market data (German and Austrian electricity markets), ablation studies, and references to a public GitHub repository for reproducibility.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Probabilistic Intraday<br>Electricity Price Forecasting"] --> B["Input Data:<br>Orderbook Microstructure<br>Continuous Buy/Sell Orders"]
  B --> C["Methodology:<br>OrderFusion<br>End-to-End Encoder"]
  C --> D["Key Process:<br>Learn Interaction-Aware<br>Buy-Sell Dynamics"]
  D --> E["Computational Step:<br>Hierarchical Quantile<br>Estimation w/ Non-Crossing Constraints"]
  E --> F["Outcomes:<br>Probabilistic Forecasts<br>Reduced Trading Risk"]
  F --> G["Results:<br>Improvements over Baselines<br>High & Low Liquidity Markets"]

Cited References (78)

1. Orderbook Feature Learning and Asymmetric Generalization in Intraday Electricity Markets (2026) — link
2. Price jumps on the intraday energy market - design and implementation of an alarm system with machine learning methods (2025) — link
3. iTransformer: Inverted Transformers Are Effective for Time Series Forecasting (2023) — link
4. A Time Series is Worth 64 Words: Long-term Forecasting with Transformers (2023)
5. Aggregated residual transformations for deep neural networks (2017)
6. Scaling Learning Algorithms Towards AI (2007)
7. A Fast Learning Algorithm for Deep Belief Nets (2006)
8. Deep learning (2016)

0 of 78 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.