Forecasting day-ahead electricity prices in Europe: The importance of considering market integration

Forecasting day-ahead electricity prices in Europe: The importance of considering market integration •Models to include market integration in electricity price forecasting are proposed.•The forecasters lead to accuracy improvements that are statistically significant.•Deep neural networks are used as based models of the larger modeling framework.•A forecasters that predicts prices in various markets leads to the best results.•A novel feature selection algorithm based on functional ANOVA is proposed. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Energy Elsevier

Forecasting day-ahead electricity prices in Europe: The importance of considering market integration

Forecasting day-ahead electricity prices in Europe: The importance of considering market integration

Applied Energy 211 (2018) 890–903 Contents lists available at ScienceDirect Applied Energy journal homepage: www.elsevier.com/locate/apenergy Forecasting day-ahead electricity prices in Europe: The importance of considering market integration a,b, b c a Jesus Lago , Fjo De Ridder , Peter Vrancx , Bart De Schutter Delft Center for Systems and Control, Delft University of Technology, Mekelweg 2, 2628CD Delft, The Netherlands Energy Technology, VITO-Energyville, ThorPark, 3600 Genk, Belgium AI Lab, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium HIGHLIGHTS Models to include market integration in electricity price forecasting are proposed. The forecasters lead to accuracy improvements that are statistically significant. Deep neural networks are used as based models of the larger modeling framework. A forecasters that predicts prices in various markets leads to the best results. A novel feature selection algorithm based on functional ANOVA is proposed. ARTICLE I NFO ABSTRACT Keywords: Motivated by the increasing integration among electricity markets, in this paper we propose two different Electricity price forecasting methods to incorporate market integration in electricity price forecasting and to improve the predictive per- Electricity market integration formance. First, we propose a deep neural network that considers features from connected markets to improve Deep neural networks the predictive accuracy in a local market. To measure the importance of these features, we propose a novel Functional ANOVA feature selection algorithm that, by using Bayesian optimization and functional analysis of variance, evaluates Bayesian optimization the effect of the features on the algorithm performance. In addition, using market integration, we propose a second model that, by simultaneously predicting prices from two markets, improves the forecasting accuracy even further. As a case study, we consider the electricity market in Belgium and the improvements...
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Publisher
Elsevier
Copyright
Copyright © 2017 Elsevier Ltd
ISSN
0306-2619
D.O.I.
10.1016/j.apenergy.2017.11.098
Publisher site
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Abstract

•Models to include market integration in electricity price forecasting are proposed.•The forecasters lead to accuracy improvements that are statistically significant.•Deep neural networks are used as based models of the larger modeling framework.•A forecasters that predicts prices in various markets leads to the best results.•A novel feature selection algorithm based on functional ANOVA is proposed.

Journal

Applied EnergyElsevier

Published: Feb 1, 2018

References

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