Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks

TL;DR

This study compares univariate and multivariate models for short-term electricity price forecasting, revealing that combining both approaches can improve accuracy, with insights into variable selection for high-dimensional models.

Contribution

It provides an extensive empirical comparison of univariate and multivariate frameworks and demonstrates the benefits of combining models and effective variable selection.

Findings

Multivariate models have a slight overall edge but do not always outperform univariate models.

Combining models via simple averaging can enhance forecasting accuracy.

Guidelines for variable selection in high-dimensional models are provided.

Abstract

We conduct an extensive empirical study on short-term electricity price forecasting (EPF) to address the long-standing question if the optimal model structure for EPF is univariate or multivariate. We provide evidence that despite a minor edge in predictive performance overall, the multivariate modeling framework does not uniformly outperform the univariate one across all 12 considered datasets, seasons of the year or hours of the day, and at times is outperformed by the latter. This is an indication that combining advanced structures or the corresponding forecasts from both modeling approaches can bring a further improvement in forecasting accuracy. We show that this indeed can be the case, even for a simple averaging scheme involving only two models. Finally, we also analyze variable selection for the best performing high-dimensional lasso-type models, thus provide guidelines to structuring better performing forecasting model designs.