02445nas a2200253   4500000000100000000000100001008004100002260001200043653002100055653001300076653001800089653002000107653002400127653001100151100001600162700001900178700002800197245006700225856009500292300001000387490000600397520177400403022001402177       2019                            d  c03/201910aMachine Learning10aBig Data10aRandom Forest10aElectric Market10aPredictive Analysis10aPrices1 aJulia Díaz1 aÁlvaro Romero1 aJosé Ramón Dorronsoro00aDay-Ahead Price Forecasting for the Spanish Electricity Market  uhttp://www.ijimai.org/journal/sites/default/files/files/2018/04/ijimai_5_4_5_pdf_14997.pdf  a42-500 v53 aDuring 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.  a1989-1660