Recurrent Neural Networks with Linear Structures for Electricity Price Forecasting

TL;DR

This paper introduces a novel recurrent neural network architecture that integrates linear structures for improved short-term electricity price forecasting, combining interpretability with high accuracy in European power markets.

Contribution

The paper presents a new RNN model embedding linear structures like Kalman filters, enhancing interpretability and capturing key market features for electricity price prediction.

Findings

Achieves 11% better RMSE than state-of-the-art models

Demonstrates robustness of hyperparameters over time

Provides probabilistic forecasts for uncertainty quantification

Abstract

We present a novel recurrent neural network architecture specifically designed for day-ahead electricity price forecasting, aimed at improving short-term decision-making and operational management in energy systems. Our combined forecasting model embeds linear structures, such as expert models and Kalman filters, into recurrent networks, enabling efficient computation and enhanced interpretability. The design leverages the strengths of both linear and non-linear model structures, allowing it to capture all relevant stylized price characteristics in power markets, including calendar and autoregressive effects, as well as influences from load, renewable energy, and related fuel and carbon markets. For empirical testing, we use hourly data from the largest European electricity market spanning 2018 to 2025 in a comprehensive forecasting study, comparing our model against state-of-the-art approaches, particularly high-dimensional linear and neural network models. In terms of RMSE, the proposed model achieves approximately 11% higher accuracy than the best-performing benchmark. We evaluate the contributions of the interpretable model components and conclude on the impact of combining linear and non-linear structures. We further evaluate the temporal robustness of the model by examining the stability of hyperparameters and the economic significance of key features. Additionally, we introduce a probabilistic extension to quantify forecast uncertainty.