- Wroclaw University of Technology
Wednesday, September 27, 2017
- Polo Santa Marta, Via Cantarane 24, Room 1.59
We conduct an extensive empirical study on short-term electricity price forecasting (EPF), involving datasets from 12 power markets, state-of-the-art parsimonious expert models, univariate and multivariate autoregressive benchmarks, and multi-parameter regression models estimated via the lasso. We show that using the latter shrinkage approach can bring statistically significant accuracy gains compared to commonly used EPF models. Additionally, we address the long-standing question on the optimal model structure for EPF. 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 datasets, seasons of the year or hours of the day, and at times is outperformed by the latter. This may be an indication that combining advanced structures or the corresponding forecasts from both modeling approaches can bring a further improvement in forecasting accuracy. 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.
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- Publication date
May 22, 2016