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Lookup NU author(s): Dr Anurag SharmaORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2026 The Author(s). IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.Electricity price forecasting is crucial for power market operations and improved trading decisions. Short-term electricity price primarily depends on demand and type of generation. High integration of intermittent renewable energy generation intensifies price volatility. Deep learning models are increasingly employed to model the nonlinear and volatile dynamics of electricity prices, addressing the limitations of traditional statistical forecasting methods. However, they struggle to extract electricity price features with apparent regularity. To overcome these limitations, this paper develops a novel hybrid short-term electricity price forecasting model that integrates multi-size depthwise convolutional neural networks (MDCNN), bidirectional gated recurrent units (BiGRU), and multi-head attention mechanism. The MDCNN component employs varied convolutional kernel sizes to extract both short and long-term information from input variables, alleviating information overload. These features are then processed by BiGRU, which captures intricate non-linear temporal dependencies. This proposed hybrid model is augmented by a multi-head attention mechanism to dynamically prioritize crucial features, improving interpretability and robustness. Finally, this model is fine-tuned by Bayesian optimization to obtain optimal hyperparameters. Advanced data-preprocessing techniques, such as two-stage dimensionality reduction approach and outlier treatment by random forest-based imputation, are applied to ensure data quality. For a case study of Spanish electricity market, the proposed model outperforms benchmark models with an (Formula presented.) of 3.31 (Formula presented.) /MWh, (Formula presented.) of 2.34 (Formula presented.) /MWh, and (Formula presented.) score of 91.2%. Additionally, Friedman rank test and post hoc analysis confirm the model's statistical significance. These results demonstrate the model's effectiveness in capturing complex price patterns and its potential practical applications in the power market.
Author(s): Prajesh A, Jain P, Sharma A
Publication type: Article
Publication status: Published
Journal: IET Generation, Transmission and Distribution
Year: 2026
Volume: 20
Issue: 1
Online publication date: 24/03/2026
Acceptance date: 09/03/2026
Date deposited: 13/04/2026
ISSN (print): 1751-8687
ISSN (electronic): 1751-8695
Publisher: John Wiley and Sons Inc
URL: https://doi.org/10.1049/gtd2.70278
DOI: 10.1049/gtd2.70278
Data Access Statement: Publicly available data has been referred to in this paper.
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