Secondary Reserve Marginal Band Price Prediction with Classical and Machine Learning Based Techniques
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Altres documents de l'autoria: Cardo Miota, Javier; Beltran, Hector; Pérez, Emilio; Sansano-Sansano, Emilio
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Títol
Secondary Reserve Marginal Band Price Prediction with Classical and Machine Learning Based TechniquesData de publicació
2023Editor
IEEEISBN
9798350331820Cita bibliogràfica
CARDO-MIOTA, J., et al. Secondary Reserve Marginal Band Price Prediction with Classical and Machine Learning Based Techniques. In: IECON 2023-49th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2023. p. 1-6.Tipus de document
info:eu-repo/semantics/conferenceObjectVersió de l'editorial
https://ieeexplore.ieee.org/abstract/document/10311889Versió
info:eu-repo/semantics/publishedVersionParaules clau / Matèries
Resum
As a consequence of the continuous growth being
experienced by renewable energy systems, the role of the different ancillary services is becoming essential for the reliable operation of the
electric system. This ... [+]
As a consequence of the continuous growth being
experienced by renewable energy systems, the role of the different ancillary services is becoming essential for the reliable operation of the
electric system. This paper develops a methodology for estimating
the secondary reserve marginal band price in the Iberian electricity
market using four forecasting techniques: two classical models
(ARIMAX and SARIMAX) and two machine learning models
(Random Forest and Support Vector Regression). The methodology
involves a Pearson correlation analysis and a Sequential Forward
Selection algorithm to select the relevant model inputs and exogenous
variables. A statistical data analysis is conducted to examine the temporal characteristics of the target variable and a data-preprocessing
is performed for the proper implementation of the models. A grid
search and a sequential division cross-validation are applied to
determine the optimal parameters of the models. The performance
is evaluated using three statistical metrics. The results show that
the Random Forest model outperforms the other models, achieving
the lowest MAE (2.40 e/MW) and RMSE (3.40 e/MW) values. [-]
Publicat a
ECON 2023-49th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2023. p. 1-6.Drets d'accés
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info:eu-repo/semantics/restrictedAccess