Day-ahead price forecasting for the spanish electricity market
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
IMAI SoftwareDate
2018-04-27Citation
10.9781/ijimai.2018.04.008
International Journal of Interactive Multimedia and Artificial Intelligence 5.4 (2019): 42-50
ISSN
1989-1660DOI
10.9781/ijimai.2018.04.008Funded by
With partial support from Spain’s grants TIN2016-76406-P and S2013/ICE-2845 CASI-CAM-CM. Work supported also by project FACIL--Ayudas Fundación BBVA a Equipos de Investigación Científica 2016 and the UAM--ADIC Chair for Data Science and Machine LearningProject
Gobierno de España. TIN2016-76406-P; Comunidad de Madrid. S2013/ICE-2845/CASI-CAMEditor's Version
https://doi.org/10.9781/ijimai.2018.04.008Subjects
Machine learning; Big data; Electric market; Predictive analysis; Prices; Random forest.; InformáticaAbstract
During the last years, electrical systems around the world and in particular the Spanish electric sector have
undergone great changes with the focus of turning them into more liberalized and competitive markets. For this
reason, in many countries like Spain have appeared electric markets where producers sell and electricity retailers
buy the power we consume. All agents involved in this market need predictions of generation, demand and
especially prices to be able to participate in them in a more efficient way, obtaining a greater profit. The present
work is focused on the context of development of a tool that allows to predict the price of electricity for the next
day in the most precise way possible. For such target, this document analyzes the electric market to understand
how prices are calculated and who are the agents that can make prices vary. Traditional proposals in the literature
range from the use of Game Theory to the use of Machine Learning, Time Series Analysis or Simulation Models.
In this work we analyze a normalization of the target variable due to a strong seasonal component in an hourly
and daily way to later benchmark several models of Machine Learning: Ridge Regression, K-Nearest Neighbors,
Support Vector Machines, Neural Networks and Random Forest. After observing that the best model is Random
Forest, a discussion has been carried out on the appropriateness of the normalization for this algorithm. From
this analysis it is obtained that the model that gives the best results has been Random Forest without applying
the normalization function. This is due to the loss of the close relationship between the objective variable and
the electric demand, obtaining an Average Absolute Error of 3.92€ for the whole period of 2016
Files in this item
Google Scholar:Romero, Álvaro
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Dorronsoro Ibero, José Ramón
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Díaz García, Julia
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