# A Market-Rule-Informed Neural Network for Efficient Imbalance Electricity Price Forecasting

arXiv: https://arxiv.org/abs/2605.09061 · HTML: https://mlsanity.com/paper/2605.09061/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 9 May 2026.

## Builds on (most cited first, 9 of 9)

- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis](https://mlsanity.com/paper/2210.02186/) (2022)
- [iTransformer: Inverted Transformers Are Effective for Time Series Forecasting](https://mlsanity.com/paper/2310.06625/) (2023)
- [TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables](https://mlsanity.com/paper/2402.19072/) (2024)
- [Probabilistic forecasting of German electricity imbalance prices](https://mlsanity.com/paper/2205.11439/) (2022)
- [Probabilistic Forecasting of Imbalance Prices in the Belgian Context](https://mlsanity.com/paper/2106.07361/) (2021)
- [Electricity Price Forecasting in the Irish Balancing Market](https://mlsanity.com/paper/2402.06714/) (2024)
- [PriceFM: Foundation Model for Probabilistic Electricity Price Forecasting](https://mlsanity.com/paper/2508.04875/) (2025)
- [OrderFusion: Encoding Orderbook for End-to-End Probabilistic Intraday Electricity Price Forecasting](https://mlsanity.com/paper/2502.06830/) (2025)

## Abstract (arXiv)

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/.