Paper: arXiv 2605.09061

Authors: Runyao Yu, Julia Lin, Derek W. Bunn, Jochen Stiasny, Wentao Wang, Yujie Chen, Tara Esterl, Peter Palensky, Jochen L. Cremer

Abstract

Accurate and efficient imbalance electricity price forecasting is critical for industrial energy trading systems, especially as battery assets and automated bidding pipelines increasingly participate in balancing markets. However, real-time forecasting is complicated by nonlinear market-rule-based price formation, heterogeneous input signals, and incomplete data availability caused by communication delays, publication lags, and measurement outages. This paper proposes a market-rule-informed neural forecasting framework that embeds imbalance price formation rules into the latent space of an expressive neural network. The proposed framework preserves raw signal information while exploiting transparent market-rule priors. We further analyze operational robustness by removing price-component information and characterize how forecasting performance scales with input length and forecasting horizon. Experimental results show that the proposed model achieves competitive forecasting performance with substantially fewer trainable parameters and shorter training time than generic deep learning baselines. Experimental results show that the proposed model achieves competitive forecasting performance with substantially fewer trainable parameters and shorter training time than generic deep learning baselines, demonstrating that market-rule priors and expressive neural networks should be jointly used for accurate and computationally sustainable forecasting in industrial energy trading applications. The implementation is publicly available at https://runyao-yu.github.io/MRINN/.

Complexity vs Empirical Score

  • Math Complexity: 5.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper combines sophisticated neural network architecture design—specifically embedding deterministic market-rule mechanisms as differentiable operators within a latent space—with deep empirical validation using long-term, high-resolution 4-year energy market datasets and rigorous baselining. It successfully bridges theoretical structural priors with practical, deployment-oriented performance analysis.

Research Flowchart

  flowchart TD
    A[<b>Goal:</b> Efficient & Accurate Imbalance Electricity Price Forecasting] --> B[<b>Inputs:</b> Heterogeneous Signals & Market-Rule Priors]
    B --> C[<b>Process:</b> Market-Rule-Informed Neural Network (MRINN)]
    C --> D{<b>Analysis:</b> Robustness & Scaling Tests}
    D --> E[<b>Outcomes:</b> Superior Performance / Fewer Parameters / Shorter Training Time]
    E --> F[<b>Deployment:</b> Sustainable Industrial Energy Trading]

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0 of 52 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.