Quantile Regression for Probabilistic Electricity Price Forecasting in the U.K. Electricity Market

The volatility and uncertainty of electricity prices due to renewable energy sources create challenges for electricity trading, necessitating reliable probabilistic electricity-price forecasting (EPF) methods. This study introduces an EPF approach using quantile regression (QR) with general predictors, focusing on the UK market. Unlike market-specific models, this method ensures adaptability and reduces complexity. Using 1,132 days of training data, including electricity prices, demand forecasts, and generation forecasts obtained from UK electricity companies, results show that the proposed model achieved a mean absolute error of 18.27 [(£/MWh] for predicting volatile short-term spot market prices. The QR model achieved high predictive accuracy and stability, with only a 4–25% average pinball loss increases when the previous day’s prices (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P_{t-1}$ </tex-math></inline-formula>) were excluded due to bidding deadlines. These findings demonstrate the model’s robustness and its potential to enhance market efficiency by providing reliable and simplified probabilistic forecasts, aiding stakeholders in mitigating risks and optimizing strategies.