A hybrid model for day-ahead electricity price forecasting: Combining fundamental and stochastic modelling

Mira Watermeyer, Thomas M\"obius, Oliver Grothe and Felix M\"usgens

Papers from arXiv.org

Abstract: The accurate prediction of short-term electricity prices is vital for effective trading strategies, power plant scheduling, profit maximisation and efficient system operation. However, uncertainties in supply and demand make such predictions challenging. We propose a hybrid model that combines a techno-economic energy system model with stochastic models to address this challenge. The techno-economic model in our hybrid approach provides a deep understanding of the market. It captures the underlying factors and their impacts on electricity prices, which is impossible with statistical models alone. The statistical models incorporate non-techno-economic aspects, such as the expectations and speculative behaviour of market participants, through the interpretation of prices. The hybrid model generates both conventional point predictions and probabilistic forecasts, providing a comprehensive understanding of the market landscape. Probabilistic forecasts are particularly valuable because they account for market uncertainty, facilitating informed decision-making and risk management. Our model delivers state-of-the-art results, helping market participants to make informed decisions and operate their systems more efficiently.

Date: 2023-04
New Economics Papers: this item is included in nep-des, nep-ene and nep-for
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2304.09336

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