mlsanityData to 14 Oct 2026

Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression

arXiv 2404.02270 ↗First posted 2 Apr 2024q-fin.ST stat.AP stat.MLcited by 7

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Abstract (arXiv)

Operational decisions relying on predictive distributions of electricity prices can result in significantly higher profits compared to those based solely on point forecasts. However, the majority of models developed in both academic and industrial settings provide only point predictions. To address this, we examine three postprocessing methods for converting point forecasts of day-ahead electricity prices into probabilistic ones: Quantile Regression Averaging, Conformal Prediction, and the recently introduced Isotonic Distributional Regression. We find that while the latter demonstrates the most varied behavior, it contributes the most to the ensemble of the three predictive distributions, as measured by Shapley values. Remarkably, the performance of the combination is superior to that of state-of-the-art Distributional Deep Neural Networks over two 4.5-year test periods from the German and Spanish power markets, spanning the COVID pandemic and the war in Ukraine.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 14 of its 45 references that are in the map. First posted on arXiv 2 Apr 2024.

Builds on

14 of 45 references.

References · 14

  1. 2022Adaptive Conformal Predictions for Time Seriescited by 161
  2. 2020Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmarkcited by 108
  3. 2018Neural networks for post-processing ensemble weather forecastscited by 95
  4. 2021Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSxcited by 64
  5. 2019Isotonic Distributional Regressioncited by 28
  6. 2022Generative machine learning methods for multivariate ensemble post-processingcited by 26
  7. 2022Distributional neural networks for electricity price forecastingcited by 24
  8. 2018Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworkscited by 21
  9. 2021CRPS Learningcited by 21
  10. 2019Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power Marketscited by 19
  11. 2022Easy Uncertainty Quantification (EasyUQ): Generating Predictive Distributions from Single-valued Model Outputcited by 11
  12. 2016Combining predictive distributions for statistical post-processing of ensemble forecastscited by 9
2 more
  1. 2023A probabilistic forecast methodology for volatile electricity prices in the Australian National Electricity Marketcited by 7
  2. 2023Combining predictive distributions of electricity prices: Does minimizing the CRPS lead to optimal decisions in day-ahead bidding?cited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 2.

7 papers.

  1. 2025Probabilistic measures afford fair comparisons of AIWP and NWP model outputcited by 3
  2. 2024NBMLSS: probabilistic forecasting of electricity prices via Neural Basis Models for Location Scale and Shapecited by 2
  3. 2025Online Multivariate Regularized Distributional Regression for High-dimensional Probabilistic Electricity Price Forecastingcited by 2
  4. 2025Analyzing Uncertainty Quantification in Statistical and Deep Learning Models for Probabilistic Electricity Price Forecastingcited by 1
  5. 2026Foundation models for electricity price forecasting and battery arbitrage: Can they replace market-specific forecasting models?cited by 1
  6. 2025The Evolution of Probabilistic Price Forecasting Techniques: A Review of the Day-Ahead, Intra-Day, and Balancing Marketscited by 0
  7. 2026Stealing profits: Spread-based temporal hierarchy forecasting for day-ahead electricity marketscited by 0