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The Evolution of Probabilistic Price Forecasting Techniques: A Review of the Day-Ahead, Intra-Day, and Balancing Markets

arXiv 2511.05523 ↗First posted 28 Oct 2025q-fin.ST cs.AI stat.APcited by 0

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Abstract (arXiv)

Electricity price forecasting has become a critical tool for decision-making in energy markets, particularly as the increasing penetration of renewable energy introduces greater volatility and uncertainty. Historically, research in this field has been dominated by point forecasting methods, which provide single-value predictions but fail to quantify uncertainty. However, as power markets evolve due to renewable integration, smart grids, and regulatory changes, the need for probabilistic forecasting has become more pronounced, offering a more comprehensive approach to risk assessment and market participation. This paper presents a review of probabilistic forecasting methods, tracing their evolution from Bayesian and distribution based approaches, through quantile regression techniques, to recent developments in conformal prediction. Particular emphasis is placed on advancements in probabilistic forecasting, including validity-focused methods which address key limitations in uncertainty estimation. Additionally, this review extends beyond the Day-Ahead Market to include the Intra-Day and Balancing Markets, where forecasting challenges are intensified by higher temporal granularity and real-time operational constraints. We examine state of the art methodologies, key evaluation metrics, and ongoing challenges, such as forecast validity, model selection, and the absence of standardised benchmarks, providing researchers and practitioners with a comprehensive and timely resource for navigating the complexities of modern electricity markets.

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References · 32

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20 more
  1. 2023Probabilistically robust conformal predictioncited by 25
  2. 2022Distributional neural networks for electricity price forecastingcited by 24
  3. 2019Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power Marketscited by 19
  4. 2022Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flowscited by 14
  5. 2022Electricity Price Forecasting: The Dawn of Machine Learningcited by 12
  6. 2020Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Pricescited by 11
  7. 2022Probabilistic forecasting of German electricity imbalance pricescited by 10
  8. 2021Probabilistic Forecasting of Imbalance Prices in the Belgian Contextcited by 8
  9. 2024Conformal Prediction for Stochastic Decision-Making of PV Power in Electricity Marketscited by 8
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  12. 2024Transformer Conformal Prediction for Time Seriescited by 7
  13. 2024Electricity Price Forecasting in the Irish Balancing Marketcited by 6
  14. 2024On-line conformalized neural networks ensembles for probabilistic forecasting of day-ahead electricity pricescited by 5
  15. 2024Bayesian Hierarchical Probabilistic Forecasting of Intraday Electricity Pricescited by 4
  16. 2022Simulation-based Forecasting for Intraday Power Markets: Modelling Fundamental Drivers for Location, Shape and Scale of the Price Distributioncited by 3
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  19. 2025Probabilistic intraday electricity price forecasting using generative machine learningcited by 2
  20. 2024ReModels: Quantile Regression Averaging modelscited by 1

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