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Seasonality in deep learning forecasts of electricity imbalance prices

Author

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  • Deng, Sinan
  • Inekwe, John
  • Smirnov, Vladimir
  • Wait, Andrew
  • Wang, Chao

Abstract

In this paper, we propose a seasonal attention mechanism, the effectiveness of which is evaluated via the Bidirectional Long Short-Term Memory (BiLSTM) model. We compare its performance with alternative deep learning and machine learning models in forecasting the balancing settlement prices in the electricity market of Great Britain. Critically, the Seasonal Attention-Based BiLSTM framework provides a superior forecast of extreme prices with an out-of-sample gain in the predictability of 11%–15% compared with models in the literature. Our forecasting techniques could aid both market participants, to better manage their risk and assign their assets, and policy makers, to operate the system at lower cost.

Suggested Citation

  • Deng, Sinan & Inekwe, John & Smirnov, Vladimir & Wait, Andrew & Wang, Chao, 2024. "Seasonality in deep learning forecasts of electricity imbalance prices," Energy Economics, Elsevier, vol. 137(C).
    • Handle: RePEc:eee:eneeco:v:137:y:2024:i:c:s014098832400478x
    DOI: 10.1016/j.eneco.2024.107770
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Xu, Yuzhen & Huang, Xin & Zheng, Xidong & Zeng, Ziyang & Jin, Tao, 2024. "VMD-ATT-LSTM electricity price prediction based on grey wolf optimization algorithm in electricity markets considering renewable energy," Renewable Energy, Elsevier, vol. 236(C).
    2. Kaiyao Jiang & Yuji Yamada, 2025. "A Comprehensive Analysis of Imbalance Signal Prediction in the Japanese Electricity Market Using Machine Learning Techniques," Energies, MDPI, vol. 18(11), pages 1-28, May.
    3. Failing, Johanna M. & Cardo-Miota, Javier & Pérez, Emilio & Beltran, Hector & Segarra-Tamarit, Jorge, 2025. "Deep learning approaches for predicting the upward and downward energy prices in the Spanish automatic Frequency Restoration Reserve market," Energy, Elsevier, vol. 320(C).
    4. Runyao Yu & Derek W. Bunn & Julia Lin & Jochen Stiasny & Fabian Leimgruber & Tara Esterl & Yuchen Tao & Lianlian Qi & Yujie Chen & Wentao Wang & Jochen L. Cremer, 2026. "Deep Learning for Electricity Price Forecasting: A Review of Day-Ahead, Intraday, and Balancing Electricity Markets," Papers 2602.10071, arXiv.org, revised May 2026.
    5. Singh, Chandransh & Sreekumar, Sreenu & Malakar, Tanmoy, 2026. "Balancing markets and imbalance forecasting: A pathway towards net zero grids," Applied Energy, Elsevier, vol. 402(PB).
    6. Ciaran O’Connor & Mohamed Bahloul & Steven Prestwich & Andrea Visentin, 2025. "A Review of Electricity Price Forecasting Models in the Day-Ahead, Intra-Day, and Balancing Markets," Energies, MDPI, vol. 18(12), pages 1-40, June.

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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