A novel framework for probabilistic forecasting of electricity forward curves

Electricity forward contracts are key instruments for managing price volatility in liberalized power markets, where non-storability and real-time balancing create complex price dynamics. These contracts differ from traditional derivatives as they are defined over delivery periods, leading to overlapping maturities and interdependent forward curves. This structure, combined with low liquidity and sparse data in long-term horizons, poses challenges for accurate forecasting. This work proposes a novel probabilistic forecasting framework for electricity forward curves, addressing multivariate dependencies, seasonality, and data sparsity. The approach involves three steps: (i) forward curve estimation with seasonal adjustment, arbitrage-free constraints, and a non-parametric smoothing error model; (ii) dimensionality reduction, and orthogonalization of elementary errors; (iii) probabilistic forecasts using autoregressive models, bootstrap, and Generalized Autoregressive Score (GAS) models for residuals. The framework supports bidirectional estimation and forecasting. By enhancing forecast accuracy and capturing forward curve dynamics, the method facilitates more informed decision-making for energy market participants. Results confirm the model’s effectiveness in capturing key multivariate structures for portfolio risk management.