LAPSE:2023.22440
Published Article
LAPSE:2023.22440
Beating the Naïve—Combining LASSO with Naïve Intraday Electricity Price Forecasts
March 24, 2023
Abstract
In the last three decades the vast majority of electricity price forecasting (EPF) research has concerned day-ahead markets. However, the rapid expansion of renewable generation—mostly wind and solar—have shifted the focus to intraday markets, which can be used to balance the deviations between positions taken in the day-ahead market and the actual demand and renewable generation. A recent EPF study claims that the German intraday, continuous-time market for hourly products is weak-form efficient, that is, that the best predictor for the so-called ID3-Price index is the most recent transaction price. Here, we undermine this claim and show that we can beat the naïve forecast by combining it with a prediction of a parameter-rich model estimated using the least absolute shrinkage and selection operator (LASSO). We further argue, that that if augmented with timely predictions of fundamental variables for the coming hours, the LASSO-estimated model itself can significantly outperform the naïve forecast.
Keywords
averaging forecasts, fundamental variables, ID3-Price index, intraday electricity market, LASSO, price forecasting, variable selection
Suggested Citation
Marcjasz G, Uniejewski B, Weron R. Beating the Naïve—Combining LASSO with Naïve Intraday Electricity Price Forecasts. (2023). LAPSE:2023.22440
Author Affiliations
Marcjasz G: Department of Operations Research and Business Intelligence, Wrocław University of Science and Technology, 50-370 Wrocław, Poland [ORCID]
Uniejewski B: Department of Operations Research and Business Intelligence, Wrocław University of Science and Technology, 50-370 Wrocław, Poland [ORCID]
Weron R: Department of Operations Research and Business Intelligence, Wrocław University of Science and Technology, 50-370 Wrocław, Poland [ORCID]
Journal Name
Energies
Volume
13
Issue
7
Article Number
E1667
Year
2020
Publication Date
2020-04-03
ISSN
1996-1073
Version Comments
Original Submission
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PII: en13071667, Publication Type: Journal Article
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LAPSE:2023.22440
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https://doi.org/10.3390/en13071667
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