Prediction of Matching Prices in Electricity Markets through Curve Representation Articles uri icon

publication date

  • November 2023

start page

  • 1

end page

  • 20

issue

  • 23

volume

  • 16

Electronic International Standard Serial Number (EISSN)

  • 1996-1073

abstract

  • In the Spanish electricity market, after the daily market is held in which prices are set for
    the next day, the secondary and tertiary markets take place, which allow companies more accurate
    adjustment of the electricity they are able to offer. The objective of this paper is to predict the final
    price reached in these markets by predicting the supply curve in advance, which is the aggregate of
    what companies offer. First, we study a procedure to represent the supply curves, and then we consider
    different machine learning approaches to obtain the day-ahead supply curves for the secondary market.
    Finally, the predictions of the supply curves are crossed with the system requirements to obtain the
    expected price predictions. Histogram-Based Gradient Boosting is the best performing algorithm for
    predicting supply curves. The most relevant variables for the prediction are the lagged values, the daily
    market price, the price of gas and values of the wind recorded in the Spanish provinces.

subjects

  • Statistics

keywords

  • electricity secondary market; electricity supply curves; multivariate time series; forecasting