Conformal Prediction for electricity price forecasting in the day-ahead and real-time balancing market

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. • Applied time-series-adapted Conformal Prediction to electricity price forecasting. • Developed Q-Ens ensemble for enhanced prediction accuracy and interval reliability. • Achieved narrower prediction intervals while maintaining coverage guarantees. • Assessed financial benefits of CP and QR models in Day-Ahead and Balancing Markets. • Demonstrated increased returns in energy storage arbitrage using the proposed ensemble.