Electricity price forecast on day-ahead market for mid- and short terms: capturing spikes in data sequences using recurrent neural network techniques
Author:
Funder
UEFISCDI
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00202-024-02393-w.pdf
Reference39 articles.
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2. Bâra A, Oprea S-V (2024) Predicting day-ahead electricity market prices through the integration of macroeconomic factors and machine learning techniques. Int J Comput Intell Syst 17(1):10. https://doi.org/10.1007/s44196-023-00387-3
3. Bashir N, Irwin D, Shenoy P (2021) A probabilistic approach to committing solar energy in day-ahead electricity markets. Sustain Comput: Inform Syst. https://doi.org/10.1016/j.suscom.2020.100477
4. Beltrán S, Castro A, Irizar I, Naveran G, Yeregui I (2022) Framework for collaborative intelligence in forecasting day-ahead electricity price. Appl Energy. https://doi.org/10.1016/j.apenergy.2021.118049
5. Busse J, Rieck J (2022) Mid-term energy cost-oriented flow shop scheduling: integration of electricity price forecasts, modeling, and solution procedures. Comput Ind Eng. https://doi.org/10.1016/j.cie.2021.107810
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