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

ArXiv ID: 2502.06830 “View on arXiv”

Authors: Unknown

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/.

Keywords: Electricity Price Forecasting, Probabilistic Forecasting, Orderbook Microstructure, Renewable Energy, Quantile Estimation

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: 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.

  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"]