Forecasting day-ahead electricity prices in Europe: the importance of considering market integration (1708.07061v3)

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.

• The paper demonstrates that incorporating interconnected market data significantly improves day-ahead electricity price forecasting accuracy in Europe, proposing novel DNN and dual-market models to capture integration effects.
• Empirical testing on Belgian and French markets shows including integrated features leads to substantial reductions in forecasting errors, with the dual-market forecaster reducing sMAPE from 15.7% to 12.5%.
• The findings validate the influence of regional market dynamics on price forecasts, providing practical insights for utilities to optimize strategies and policymakers to evaluate integration efforts.

Electricity Price Forecasting in the Context of Market Integration

The paper "Forecasting day-ahead electricity prices in Europe: the importance of considering market integration" explores the impact of market integration on the predictive performance for day-ahead electricity price forecasting in Europe. It proposes novel methodologies leveraging interconnected market features to enhance forecasting accuracy. The paper primarily focuses on Belgium and France as a case paper.

Methodologies Proposed

The authors introduce two distinct models to capture market integration effects:

1. Feature-based Integration Model: This model employs a Deep Neural Network (DNN) to incorporate features from neighboring markets. Utilizing a custom feature selection algorithm driven by Bayesian optimization and functional ANOVA, the model rigorously evaluates market integration effects. The integration of features from connected markets results in a marked improvement in predictive accuracy over models using isolated data.
2. Dual-market Forecaster: This advanced model endeavors simultaneous prediction in multiple interconnected markets. By leveraging shared market dynamics, it further optimizes forecasting accuracy, showcasing how simultaneous dual-market learning can enhance the performance beyond single-market models.

Empirical Findings

The models are empirically tested on electricity markets in Belgium, with French market data considered for integration. The results reveal:

• A significant improvement in forecasting accuracy when including French market data, with reductions in sMAPE from 15.7% to 12.5% for the dual-market forecaster.
• A thorough feature selection process indicated the combination of French electricity features and local data significantly contributes to forecast accuracy. Market integration variables were particularly impactful.

Implications

The outcomes have several implications. Firstly, they validate the intuitive notion that electricity markets do not operate in isolation but are influenced by wider regional dynamics. Specifically, in regions like the European Union, unified market operations necessitate models that incorporate cross-border influences to enhance forecast reliability.

Practically, utility companies can leverage these models to optimize bidding strategies, effectively leading to cost cutting and improved market competitiveness. In terms of system stability, better price forecasts help in maintaining equilibrium between electricity supply and demand.

From a policy perspective, these observations underscore the importance of continued efforts towards market integration within the EU. Policymakers could use this framework to evaluate the effectiveness of integration policies and understand their market dynamics implications.

Future Directions

Future research could expand this modeling framework to other interconnected electricity markets within Europe or globally to assess transferability and scalability. Additionally, exploring the integration of renewable energy data into such models could further refine predictive accuracy amidst the increasing volatility introduced by renewable sources.

In conclusion, the paper provides a robust framework and empirical evidence reinforcing the significance of market integration in enhancing day-ahead electricity price forecasts. It invites further exploration into interconnected market price dynamics, offering avenues for both operational efficiencies and academic inquiries.