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

TL;DR

This paper introduces two novel deep learning models that incorporate market integration features to enhance day-ahead electricity price forecasting accuracy in Europe, demonstrating significant improvements in predictive performance.

Contribution

The paper presents a deep neural network with a new feature selection method and a multi-market prediction model that together improve electricity price forecasts by leveraging market integration.

Findings

Forecast accuracy improved from 15.7% to 12.5% sMAPE.

The feature selection algorithm effectively discards irrelevant features.

Market integration significantly enhances predictive performance.

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.