A Market-Rule-Informed Neural Network for Efficient Imbalance Electricity Price Forecasting
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 ...
| Authors | Runyao Yu, Julia Lin, Derek W. Bunn et al. |
|---|---|
| Primary category | q-fin.CP |
| Published | 9 May 2026 |
| Last updated at source | 9 May 2026 |
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