Neural networks in day-ahead electricity price forecasting: Single vs. multiple outputs
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Cited by:
- Li, Wei & Becker, Denis Mike, 2021. "Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling," Energy, Elsevier, vol. 237(C).
- Lago, Jesus & Marcjasz, Grzegorz & De Schutter, Bart & Weron, Rafał, 2021.
"Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark,"
Applied Energy, Elsevier, vol. 293(C).
- Jesus Lago & Grzegorz Marcjasz & Bart De Schutter & Rafa{l} Weron, 2020. "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark," Papers 2008.08004, arXiv.org, revised Dec 2020.
- Fangze Zhou & Hui Zhou & Zhaoyan Li & Kai Zhao, 2022. "Multi-Step Ahead Short-Term Electricity Load Forecasting Using VMD-TCN and Error Correction Strategy," Energies, MDPI, vol. 15(15), pages 1-18, July.
- Wei Li & Denis Mike Becker, 2021. "Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling," Papers 2101.05249, arXiv.org, revised Jul 2021.
- Dimitrios Kontogiannis & Dimitrios Bargiotas & Aspassia Daskalopulu & Athanasios Ioannis Arvanitidis & Lefteri H. Tsoukalas, 2022. "Error Compensation Enhanced Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 15(4), pages 1-21, February.
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This paper has been announced in the following NEP Reports:- NEP-BIG-2020-09-07 (Big Data)
- NEP-CMP-2020-09-07 (Computational Economics)
- NEP-ENE-2020-09-07 (Energy Economics)
- NEP-FOR-2020-09-07 (Forecasting)
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