2018/02/01 by Florian Ziel, Rafał Weron, Rafal Weron · 1 citation
Economics, Econometrics and Finance · Engineering · Mathematics · #Computer science #Econometrics #Economics #Electric Power System Optimization #Electricity #Electricity price #Electricity price forecasting #Energy Load and Power Forecasting #Engineering #Finance #Lasso (programming language) #Machine learning #Model selection #Monetary Policy and Economic Impact #Multivariate analysis #Multivariate statistics #Structuring #Univariate #acm:62J07 #acm:62P05 #acm:62P12 #acm:62P20 #acm:91G70 #msc:62J07 #msc:62P05 #msc:62P12 #msc:62P20 #msc:91G70 #q-fin.ST #stat.AP #stat.ML
paper · pdf · doi:10.1016/j.eneco.2017.12.016
published as Energy Economics, 70 (2018), 396-420
openalex publication_date 2018/02/01 · arxiv created 2018/05/17 · arxiv updated 2018/05/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.