Katarzyna Chęć, Bartosz Uniejewski, Rafał Weron
arXiv 4 Mar 2025 · Finance — Statistical Finance · publishedJournal of commodity markets (2024) · 7 citations (OpenAlex)
arXiv:2503.02518 · PDF · DOI · OpenAlex · Extracted main text
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 hours. 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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Lago, J., Marcjasz, G., De Schutter, B., Weron, R (2021) Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark self | 1.000 | 10 | 4 | 100% |
| 2 | Ziel, F., Weron, R (2018) Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks self | 1.000 | 10 | 4 | 100% |
| 3 | Maciejowska, K., Uniejewski, B., Weron, R (2023) Forecasting electricity prices self | 1.000 | 5 | 4 | 100% |
| 4 | Nowotarski, J., Weron, R (2016) On the importance of the long-term seasonal component in day-ahead electricity price forecasting self | 1.000 | 5 | 3 | 100% |
| 5 | Marcjasz, G., Narajewski, M., Weron, R., Ziel, F (2023) Distributional neural networks for electricity price forecasting self | 0.928 | 4 | 3 | 100% |
| 6 | Weron, R (2014) Electricity price forecasting: A review of the state-of-the-art with a look into the future self | 0.928 | 4 | 3 | 100% |
| 7 | Billé, A., Gianfreda, A., Del Grosso, F., Ravazzolo, F (2023) Forecasting electricity prices with expert, linear, and nonlinear models | 0.843 | 3 | 3 | 100% |
| 8 | Grossi, L., Nan, F (2019) Robust forecasting of electricity prices: Simulations, models and the impact of renewable sources | 0.843 | 3 | 3 | 100% |
| 9 | Narajewski, M., Ziel, F (2020) Econometric modelling and forecasting of intraday electricity prices | 0.843 | 3 | 3 | 100% |
| 10 | Nitka, W., Weron, R (2023) Combining predictive distributions of electricity prices. Does minimizing the CRPS lead to optimal decisions in day-ahead bidding? self | 0.843 | 3 | 3 | 100% |
Showing the top 10 of 57 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Probabilistic Forecasting for Day-ahead Electricity Prices, Battery Trading Strategies and the Economic Evaluation of Predictive Accuracy | 0.644 | 2 | 2 |