Publications:

• Ghelasi, Paul; Ziel, Florian: A data-driven merit order: Learning a fundamental electricity price model. In: Energy Economics, Vol154 (2026), p. 109114. doi:10.1016/j.eneco.2025.109114AbstractDetailsFull textCitation

  Electricity price forecasting approaches generally fall into two categories: data-driven models, which learn from historical patterns, or fundamental models, which simulate market mechanisms. We propose a novel and highly efficient data-driven merit order model that integrates both paradigms. The resulting supply stack framework embeds the classical expert-based merit order as a nested special case, allowing all key parameters, such as plant efficiencies, bidding behavior, and available capacities, to be estimated directly from historical data, rather than assumed. We further enhance the model with critical embedded extensions such as hydro power, cross-border flows and corrections for underreported capacities, which considerably improve forecasting accuracy. Applied to the German day-ahead market, our model outperforms both classic fundamental and state-of-the-art machine learning models. It retains the interpretability of fundamental models, offering insights into marginal technologies, fuel switches, and dispatch patterns, elements which are typically inaccessible to black-box machine learning approaches. This transparency and high computational efficiency make it a promising new direction for electricity price modeling.

• Ghelasi, Paul; Ziel, Florian: From day-ahead to mid and long-term horizons with econometric electricity price forecasting models. In: Renewable and Sustainable Energy Reviews, Vol217 (2025), p. 115684. doi:10.1016/j.rser.2025.115684DetailsFull textCitation
• Ghelasi, Paul; Ziel, Florian: Far beyond day-ahead with econometric models for electricity price forecasting. In: arXiv preprint arXiv:2406.00326 (2024). DetailsCitation
• Ghelasi, Paul; Ziel, Florian: Hierarchical forecasting for aggregated curves with an application to day-ahead electricity price auctions. In: International Journal of Forecasting (2022). doi:10.1016/j.ijforecast.2022.11.004DetailsCitation

Courses:

Econometrics of electricity markets (Wintersemester 2023/2024)

Tutored Theses:

• Decision tree learning for modeling price auctions of day-ahead electricity markets (Master Thesis Business Administration - Energy and Finance, 2021)