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

Jesus Lago Garcia*, Fjo De Ridder, Peter Vrancx, Bart De Schutter

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

134 Citations (Scopus)
21 Downloads (Pure)


Motivated by the increasing integration among electricity markets, in this paper we propose two different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance. First, we propose a deep neural network that considers features from connected markets to improve the predictive accuracy in a local market. To measure the importance of these features, we propose a novel feature selection algorithm that, by using Bayesian optimization and functional analysis of variance, evaluates 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 in forecasting accuracy when using various French electricity features. We show that the two proposed models lead to improvements that are statistically significant. Particularly, due to market integration, the predictive accuracy is improved from 15.7% to 12.5% sMAPE (symmetric mean absolute percentage error). In addition, we show that the proposed feature selection algorithm is able to perform a correct assessment, i.e. to discard the irrelevant features.

Original languageEnglish
Pages (from-to)890-903
JournalApplied Energy
Publication statusPublished - 2018

Bibliographical note

Accepted Author Manuscript


  • Bayesian optimization
  • Deep neural networks
  • Electricity market integration
  • Electricity price forecasting
  • Functional ANOVA


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