Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks
This work addresses a long-standing question in electricity price forecasting for energy market participants, but it is incremental as it compares existing modeling frameworks rather than introducing new methods.
The study tackled the problem of determining whether univariate or multivariate models are better for day-ahead electricity price forecasting, finding that multivariate models only slightly outperform univariate ones overall and that combining both approaches can improve accuracy, with a simple averaging scheme showing potential gains.
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.