Extrapolating the long-term seasonal component of electricity prices for forecasting in the day-ahead market

Katarzyna Chec, Bartosz Uniejewski and Rafał Weron

No WORMS/24/04, WORking papers in Management Science (WORMS) from Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology

Abstract: Recent studies provide evidence that decomposing the electricity price into the long-term seasonal component (LTSC) and the remaining part, predicting both separately, and then combining their forecasts can bring significant accuracy gains in day-ahead electricity price forecasting. However, not much attention has been paid to predicting the LTSC, and the last 24 hourly values of the estimated pattern are typically copied for the target day. To address this gap, we introduce a novel approach which extracts the trend-seasonal pattern from a price series extrapolated using price forecasts for the next 24 h. We assess it using two 5-year long test periods from the German and Spanish power markets, covering the Covid-19 pandemic, the 2021/2022 energy crisis, and the war in Ukraine. Considering parsimonious autoregressive and LASSO-estimated models, we find that improvements in predictive accuracy range from 3% to 15% in terms of the root mean squared error and exceed 1% in terms of profits from a realistic trading strategy involving day-ahead bidding and battery storage.

Keywords: Electricity price forecasting; Long-term seasonal component; Day-ahead market; Combining forecasts (search for similar items in EconPapers)
JEL-codes: C22 C51 C53 Q41 Q47 (search for similar items in EconPapers)
Pages: 19 pages
Date: 2024
New Economics Papers: this item is included in nep-cis, nep-ene and nep-for
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https://worms.pwr.edu.pl/RePEc/ahh/wpaper/WORMS_24_04.pdf Published version, 19.11.2024 (application/pdf)

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