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

ArXiv ID: 2511.05523 “View on arXiv”

Authors: Ciaran O’Connor, Mohamed Bahloul, Steven Prestwich, Andrea Visentin

Abstract

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.

Keywords: probabilistic forecasting, quantile regression, conformal prediction, renewable energy, electricity markets, Energy Markets

Complexity vs Empirical Score

• Math Complexity: 3.0/10
• Empirical Rigor: 2.0/10
• Quadrant: Philosophers
• Why: The paper is a literature review that references advanced methods like conformal prediction and Bayesian models, but it presents them theoretically rather than deriving them, placing it in the low-math category; it lacks code, backtests, or datasets, resulting in low empirical rigor.

  flowchart TD
    A["Research Goal<br>Review probabilistic forecasting methods"] --> B["Data/Inputs<br>Historical price data, Renewables data, Market specifics"]
    B --> C{"Computational Processes<br>Analysis of Key Methodologies"}
    C --> D["Bayesian &<br>Distribution-based Models"]
    C --> E["Quantile Regression<br>Techniques"]
    C --> F["Conformal Prediction<br>& Validity Methods"]
    D & E & F --> G["Key Findings/Outcomes<br>Evolution from point to probabilistic forecasting, Superior risk assessment, Specific challenges in Intra-Day & Balancing markets, Need for standardized benchmarks"]