Type of Publication: Article in Journal

A data-driven merit order: Learning a fundamental electricity price model

Author(s):

Ghelasi, Paul; Ziel, Florian

Title of Journal:

Energy Economics

Volume (Publication Date):

154 (2026)

pages:

109114

ISSN:

0140-9883

Keywords:

Electricity price, Power, Forecasting, Fundamental, Data-driven, Merit order, Nested system, Estimation

Digital Object Identifier (DOI):

doi:10.1016/j.eneco.2025.109114

Link to complete version:

https://www.sciencedirect.com/science/article/pii/S0140988325009442

Citation:

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Abstract

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.