A two-stage deep learning approach for accurate day-ahead electricity price forecasting

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dc.contributor.authorAksu, Inayet Ozge
dc.contributor.authorGhaemi, Sina
dc.contributor.authorAnvari-Moghaddam, Amjad
dc.date.accessioned2026-02-27T07:33:39Z
dc.date.available2026-02-27T07:33:39Z
dc.date.issued2026
dc.description.abstractParticipants in the energy market are at greater risk of making decisions due to the nonlinear and volatile characteristics of electricity prices. Accurate short-term electricity price forecasting (EPF) is essential to ensure improved resource allocation, grid stability and enable market participants to manage their decisions efficiently. This study proposes a novel two-stage forecasting framework for day-ahead EPF using time series decomposition methods and hybrid deep learning algorithms. In the first stage, features related to EPF at the next time step are predicted. In this stage, the highest-frequency component extracted via Empirical Mode Decomposition (EMD) is further decomposed using Variational Mode Decomposition (VMD) so as to better capture rapid fluctuations and improve the overall prediction accuracy. Moreover, a decentralized deep learning architecture is designed in which Gated Recurrent Unit (GRU) networks are employed for high-frequency components, while Long Shortterm Memory (LSTM) networks are used for the remaining components. In the second stage, EPF is generated using a hybrid LSTM and GRU structure, which incorporates both features estimated in the first stage and historical electricity price data. Finally, hyperparameters of the deep learning models are optimized using Bayesian Optimization to enhance performance. To validate the proposed framework, real market data from the DK1 region of Denmark is used. The proposed hybrid prediction framework is evaluated against both machine learning methods and deep learning-based architectures. Experimental results demonstrate that the proposed method achieves approximately 27.15 % lower RMSE compared to traditional machine learning models, and around 28.24 % lower RMSE compared to LSTM-based models.
dc.description.sponsorshipScientific and Technological Research Council of Turkiye (TUBITAK) BIDEB-2219 Postdoctoral Research Fellowship Program; BAP by the Unit of Scientific Research Projects Coordination [24142001]
dc.description.sponsorshipI.O.A. acknowledges support from the Scientific and Technological Research Council of Turkiye (TUBITAK) BIDEB-2219 Postdoctoral Research Fellowship Program and the BAP project No. 24142001, which was adopted by the Unit of Scientific Research Projects Coordination connected to Adana Alparslan Turkes, Science and Technology University.
dc.identifier.doi10.1016/j.engappai.2025.112721
dc.identifier.issn0952-1976
dc.identifier.issn1873-6769
dc.identifier.urihttp://dx.doi.org/10.1016/j.engappai.2025.112721
dc.identifier.urihttps://hdl.handle.net/20.500.14669/4651
dc.identifier.volume163
dc.identifier.wosWOS:001674534200001
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofEngineering Applications of Artificial Intelligence
dc.relation.publicationcategoryMakale - Uluslararas� Hakemli Dergi - Kurum ��retim Eleman�
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20260302
dc.subjectTwo-stage prediction
dc.subjectDeep learning
dc.subjectHybrid
dc.subjectDecomposition
dc.subjectDay-ahead
dc.subjectEnergy market
dc.titleA two-stage deep learning approach for accurate day-ahead electricity price forecasting
dc.typeArticle

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