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

TL;DR

This paper introduces a hybrid approach combining techno-economic and stochastic models to improve short-term electricity price forecasting, capturing market fundamentals and uncertainties for better decision-making.

Contribution

It presents a novel hybrid model that integrates market understanding with probabilistic forecasting, advancing beyond purely statistical or economic models.

Findings

State-of-the-art forecasting accuracy achieved

Provides both point and probabilistic predictions

Enhances decision-making under market uncertainty

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.