Work

A novel framework for probabilistic forecasting of electricity forward curves

Year: 2026

Type: article

Abstract: 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.

Source: Energy Economics

DOI: 10.1016/j.eneco.2026.109293

Authors: Marina Dietze, Davi Valladão, Alexandre Street, Stein-Erik Fleten

Institutions: Pontifícia Universidade Católica do Rio de Janeiro, Stanford University, Norwegian University of Science and Technology

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: hybrid

Estimated APC paid: $4,280