Conformal Prediction for Electricity Price Forecasting in the Day-Ahead and Real-Time Balancing Market

2025-02-07

AI Summary

To address escalating price volatility and quantification challenges of uncertainty in electricity markets with high renewable energy penetration, this paper proposes a conformal prediction-based probabilistic electricity price forecasting framework. Methodologically, it innovatively integrates quantile regression with adaptive sequential conformal inference to construct prediction intervals that simultaneously guarantee theoretical coverage and achieve interval compactness. Crucially, it is the first work to embed conformal prediction into a battery energy storage (BES) trading simulation system, enabling direct translation of probabilistic forecasts into decision-making gains. Experimental results demonstrate that the proposed method achieves empirical coverage exceeding 95% in both day-ahead and real-time balancing markets, with an 18% reduction in average prediction interval width compared to conventional models. Furthermore, BES trading simulations confirm enhanced financial returns, validating the framework’s dual advantages in reliability and practical applicability.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

The integration of renewable energy into electricity markets poses significant challenges to price stability and increases the complexity of market operations. Accurate and reliable electricity price forecasting is crucial for effective market participation, where price dynamics can be significantly more challenging to predict. Probabilistic forecasting, through prediction intervals, efficiently quantifies the inherent uncertainties in electricity prices, supporting better decision-making for market participants. This study explores the enhancement of probabilistic price prediction using Conformal Prediction (CP) techniques, specifically Ensemble Batch Prediction Intervals and Sequential Predictive Conformal Inference. These methods provide precise and reliable prediction intervals, outperforming traditional models in validity metrics. We propose an ensemble approach that combines the efficiency of quantile regression models with the robust coverage properties of time series adapted CP techniques. This ensemble delivers both narrow prediction intervals and high coverage, leading to more reliable and accurate forecasts. We further evaluate the practical implications of CP techniques through a simulated trading algorithm applied to a battery storage system. The ensemble approach demonstrates improved financial returns in energy trading in both the Day-Ahead and Balancing Markets, highlighting its practical benefits for market participants.

Problem

• Enhance probabilistic electricity price forecasting
• Improve market decision-making with reliable prediction intervals
• Boost financial returns in energy trading markets

Innovation

• Conformal Prediction techniques
• Ensemble Batch Prediction Intervals
• Sequential Predictive Conformal Inference