Paper: arXiv 2503.02518

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 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.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 8.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced statistical techniques like LASSO, wavelet smoothing, and Diebold-Mariano tests for significance, indicating high mathematical complexity. It is empirically rigorous with long-term out-of-sample backtests across two major markets, robust to crises, and includes a realistic trading strategy with profitability metrics.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Improve day-ahead<br>electricity price forecasting<br>by modeling LTSC"] --> B["Data Input<br>5-year daily price data<br>German & Spanish markets"]
  B --> C{"Key Methodology<br>Novel LTSC Extraction"}
  C --> D["Extrapolate price series<br>using 24h forecasts"]
  D --> E["Extract Trend-Seasonal Pattern<br>from extrapolated series"]
  E --> F["Combine with AR/LASSO<br>forecasts of residual"]
  F --> G["Compute Outcomes<br>RMSE Improvement: 3-15%<br>Trading Profit: >1%"]
  G --> H["Key Finding<br>Dynamic LTSC modeling<br>outperforms static copying"]

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3 of 502 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.