Abstract
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.
Highlights
ELECTRIC sector and in particular the Spanish electric market is highly complex but at the same time fundamental to be able to maintain the contemporary way of life
The way to establish the price follows the algorithm Euphemia that emerged in the initiative “Price Coupling of Regions” (PCR) by seven European electricity markets, among which is the Spanish one
Because with the study of the time series we discovered a great difference between working days and non-working days, a normalization of the price is proposed to achieve a reduction in variance
Summary
ELECTRIC sector and in particular the Spanish electric market is highly complex but at the same time fundamental to be able to maintain the contemporary way of life. The way to establish the price follows the algorithm Euphemia that emerged in the initiative “Price Coupling of Regions” (PCR) by seven European electricity markets, among which is the Spanish one This algorithm calculates the prices of electric energy efficiently, pursuing the maximization of welfare, which is defined as the surplus or profit, both of buyers and sellers, while optimizing the use of available capacity in interconnections. For this welfare maximization, for both the daily and intraday markets, the Euphemia algorithm considers aggregate step curves. The intersection of the supply and demand curves is called the matching point In principle, this is the point that optimizes welfare and, establishes the price of energy for that particular hour. This process is repeated for each of the 24 hours of a day
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