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Forecasting Nordic electricity spot price using deep learning networks

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Abstract

As a common data-driven method, artificial neural networks have been widely used in electricity spot price forecasting. To improve the accuracy of short-term forecasts, this paper proposes an optimized artificial neural network model for monthly electricity spot prices forecasting. A genetic algorithm is applied to regulate the weights and biases parameters of the artificial neural network structure. This study uses various historical dataset at monthly periods selected from Nordic electricity spot prices. For efficiency comparison, one-step ahead forecast method based on Schwartz-Smith stochastic model and two other prediction models, artificial neural network trained by Levenberg–Marquardt and particle swarm optimization algorithms are also presented. The comparison results show that the prediction model based on the genetic optimization algorithm is more accurate than the other prediction models. The proposed forecasting model can be considered as an alternative technique for the electricity spot price forecasting in the Nordic regions.

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Acknowledgements

The authors (Farshid Mehrdoust and Idin Noorani) acknowledge from University of Guilan and the author (Samir Brahim Belhaouari) likes to thank Qatar National Library (QNL) for supporting in publishing the paper.

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Authors and Affiliations

  1. Department of Applied Mathematics, Faculty of Mathematical Sciences, University of Guilan, Rasht, 41938-1914, Iran

    Farshid Mehrdoust & Idin Noorani

  2. College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar

    Samir Brahim Belhaouari

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  1. Farshid Mehrdoust
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  2. Idin Noorani
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  3. Samir Brahim Belhaouari
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Correspondence to Farshid Mehrdoust.

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Mehrdoust, F., Noorani, I. & Belhaouari, S.B. Forecasting Nordic electricity spot price using deep learning networks. Neural Comput & Applic 35, 19169–19185 (2023). https://doi.org/10.1007/s00521-023-08734-3

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