Yousef Adeli Sadabad, Mohammad Reza Hesamzadeh, Gyorgy Dan, Matin Bagherpour, Darryl R. Biggar
arXiv 23 Sep 2025 · Econometrics
arXiv:2509.18887 · PDF · DOI · OpenAlex · Extracted main text
The System Price (SP) of the Nordic electricity market serves as a key reference for financial hedge contracts such as Electricity Price Area Differentials (EPADs) and other risk management instruments. Therefore, the identification of drivers and the accurate forecasting of SP are essential for market participants to design effective hedging strategies. This paper develops a systematic framework that combines interpretable drivers analysis with robust forecasting methods. It proposes an interpretable feature engineering algorithm to identify the main drivers of the Nordic SP based on a novel combination of K-means clustering, Multiple Seasonal-Trend Decomposition (MSTD), and Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Then, it applies principal component analysis (PCA) to the identified data matrix, which is adapted to the downstream task of price forecasting to mitigate the issue of imperfect multicollinearity in the data. Finally, we propose a multi-forecast selection-shrinkage algorithm for Nordic SP forecasting, which selects a subset of complementary forecast models based on their bias-variance tradeoff at the ensemble level and then computes the optimal weights for the retained forecast models to minimize the error variance of the combined forecast. Using historical data from the Nordic electricity market, we demonstrate that the proposed approach outperforms individual input models uniformly, robustly, and significantly, while maintaining a comparable computational cost. Notably, our systematic framework produces superior results using simple input models, outperforming the state-of-the-art Temporal Fusion Transformer (TFT). Furthermore, we show that our approach also exceeds the performance of several well-established practical forecast combination methods.
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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 | Lehna, Scheller \ Herwartz (2022) `Forecasting day-ahead electricity prices: A comparison of time series and neural network models taking external regressors into… | 0.811 | 4 | 2 | 100% |
| 2 | Marcjasz, Serafin \ Weron (2018) `Selection of calibration windows for day-ahead electricity price forecasting', Energies 11(9), 2364 | 0.811 | 4 | 2 | 100% |
| 3 | Ziel (2016) `Forecasting electricity spot prices using lasso: On capturing the autoregressive intraday structure', IEEE Transactions on Powe… | 0.737 | 3 | 2 | 100% |
| 4 | Lago, Marcjasz, De Schutter \ Weron (2021) `Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark'… | 0.644 | 2 | 2 | 100% |
| 5 | Mirakyan, Meyer-Renschhausen \ Koch (2017) `Composite forecasting approach, application for next-day electricity price forecasting', Energy Economics 66, 228–237 | 0.585 | 3 | 1 | 100% |
| 6 | Marcos Peirotén, Bunn, Bello Morales \ Reneses Guillén (2020) `Short-term electricity price forecasting with recurrent regimes and structural breaks' | 0.585 | 3 | 1 | 100% |
| 7 | Nitka, Serafin \ Sotiros (2021) Forecasting electricity prices: Autoregressive hybrid nearest neighbors (arhnn) method, in `International Conference on Computat… | 0.585 | 3 | 1 | 100% |
| 8 | Nowotarski, Raviv, Trück \ Weron (2014) `An empirical comparison of alternative schemes for combining electricity spot price forecasts', Energy Economics 46, 395–412 | 0.585 | 3 | 1 | 100% |
| 9 | Canova \ Hansen (1995) `Are seasonal patterns constant over time? a test for seasonal stability', Journal of Business & Economic Statistics 13(3), 237–… | 0.511 | 2 | 2 | 50% |
| 10 | Hubicka, Marcjasz \ Weron (2018) `A note on averaging day-ahead electricity price forecasts across calibration windows', IEEE Transactions on sustainable energy… | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 49 scored citations.