LAPSE:2018.0561
Published Article
LAPSE:2018.0561
Efficient Forecasting of Electricity Spot Prices with Expert and LASSO Models
September 21, 2018
Recent electricity price forecasting (EPF) studies suggest that the least absolute shrinkage and selection operator (LASSO) leads to well performing models that are generally better than those obtained from other variable selection schemes. By conducting an empirical study involving datasets from two major power markets (Nord Pool and PJM Interconnection), three expert models, two multi-parameter regression (called baseline) models and four variance stabilizing transformations combined with the seasonal component approach, we discuss the optimal way of implementing the LASSO. We show that using a complex baseline model with nearly 400 explanatory variables, a well chosen variance stabilizing transformation (asinh or N-PIT), and a procedure that recalibrates the LASSO regularization parameter once or twice a day indeed leads to significant accuracy gains compared to the typically considered EPF models. Moreover, by analyzing the structures of the best LASSO-estimated models, we identify the most important explanatory variables and thus provide guidelines to structuring better performing models.
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Keywords
automated variable selection, day-ahead market, electricity spot price, LASSO, long-term seasonal component, variance stabilizing transformation
Subject
Suggested Citation
Uniejewski B, Weron R. Efficient Forecasting of Electricity Spot Prices with Expert and LASSO Models. (2018). LAPSE:2018.0561
Author Affiliations
Uniejewski B: Department of Operations Research, Faculty of Computer Science and Management, Wrocław University of Science and Technology, 50-370 Wrocław, Poland; Faculty of Pure and Applied Mathematics, Wrocław University of Science and Technology, 50-370 Wrocław,
Weron R: Department of Operations Research, Faculty of Computer Science and Management, Wrocław University of Science and Technology, 50-370 Wrocław, Poland [ORCID]
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Weron R: Department of Operations Research, Faculty of Computer Science and Management, Wrocław University of Science and Technology, 50-370 Wrocław, Poland [ORCID]
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Journal Name
Energies
Volume
11
Issue
8
Article Number
E2039
Year
2018
Publication Date
2018-08-06
ISSN
1996-1073
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Original Submission
Other Meta
PII: en11082039, Publication Type: Journal Article
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Published Article
LAPSE:2018.0561
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External Link
https://doi.org/10.3390/en11082039
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[v1] (Original Submission)
Sep 21, 2018
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Sep 21, 2018
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https://psecommunity.org/LAPSE:2018.0561
Record Owner
Calvin Tsay
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