  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.
