Conformal Prediction for Electricity Price Forecasting in the Day-Ahead and Real-Time Balancing Market
This research addresses the problem of electricity price stability and market operation complexity for market participants, particularly those integrating renewable energy sources.
This study tackled the challenge of accurate and reliable electricity price forecasting by proposing an ensemble approach using Conformal Prediction techniques, resulting in more reliable and accurate forecasts with improved financial returns in energy trading. The ensemble approach delivered both narrow prediction intervals and high coverage, outperforming traditional models.
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.