Day-ahead price forecasting for the spanish electricity market
10.9781/ijimai.2018.04.008International Journal of Interactive Multimedia and Artificial Intelligence 5.4 (2019): 42-50
Funded byWith 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 Learning
ProjectGobierno de España. TIN2016-76406-P; Comunidad de Madrid. S2013/ICE-2845/CASI-CAM
SubjectsMachine learning; Big data; Electric market; Predictive analysis; Prices; Random forest.; Informática
Esta obra está bajo una Licencia Creative Commons Atribución 4.0 Internacional.
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
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