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Prediction of Matching Prices in Electricity Markets through Curve Representation
by
Alonso, Andrés M.
, Foronda-Pascual, Daniel
in
Alternative energy sources
/ Consumers
/ Consumption
/ Deep learning
/ Electric power transmission
/ Electricity
/ electricity secondary market
/ electricity supply curves
/ Energy industry
/ forecasting
/ Machine learning
/ multivariate time series
/ Natural gas
/ Nuclear energy
/ Nuclear power plants
/ Prices
/ Prices and rates
/ Renewable resources
/ Secondary market
/ Suppliers
/ Supply & demand
/ Time series
2023
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Prediction of Matching Prices in Electricity Markets through Curve Representation
by
Alonso, Andrés M.
, Foronda-Pascual, Daniel
in
Alternative energy sources
/ Consumers
/ Consumption
/ Deep learning
/ Electric power transmission
/ Electricity
/ electricity secondary market
/ electricity supply curves
/ Energy industry
/ forecasting
/ Machine learning
/ multivariate time series
/ Natural gas
/ Nuclear energy
/ Nuclear power plants
/ Prices
/ Prices and rates
/ Renewable resources
/ Secondary market
/ Suppliers
/ Supply & demand
/ Time series
2023
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Do you wish to request the book?
Prediction of Matching Prices in Electricity Markets through Curve Representation
by
Alonso, Andrés M.
, Foronda-Pascual, Daniel
in
Alternative energy sources
/ Consumers
/ Consumption
/ Deep learning
/ Electric power transmission
/ Electricity
/ electricity secondary market
/ electricity supply curves
/ Energy industry
/ forecasting
/ Machine learning
/ multivariate time series
/ Natural gas
/ Nuclear energy
/ Nuclear power plants
/ Prices
/ Prices and rates
/ Renewable resources
/ Secondary market
/ Suppliers
/ Supply & demand
/ Time series
2023
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Prediction of Matching Prices in Electricity Markets through Curve Representation
Journal Article
Prediction of Matching Prices in Electricity Markets through Curve Representation
2023
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Overview
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.
Publisher
MDPI AG
Subject
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