EconPapers    
Economics at your fingertips  
 

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

Jesus Lago, Fjo De Ridder, Peter Vrancx and Bart De Schutter

Papers from arXiv.org

Abstract: 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.

Date: 2017-08, Revised 2017-12
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ene, nep-eur, nep-for and nep-reg
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Published in Applied Energy, Volume 211, 1 February 2018, Pages 890-903

Downloads: (external link)
http://arxiv.org/pdf/1708.07061 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1708.07061

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().

 
Page updated 2024-12-28
Handle: RePEc:arx:papers:1708.07061