Energy Price Modelling: A Comparative Evaluation of four Generations of Forecasting Methods

TL;DR

This paper systematically compares four generations of energy price forecasting methods, from econometric models to advanced deep learning transformers, through a comprehensive empirical study using EU energy market data.

Contribution

It provides the first thorough empirical evaluation contrasting traditional, machine learning, sequence learning, and transformer-based models for energy price forecasting.

Findings

Transformers outperform traditional models in accuracy.

Deep learning methods show robustness across different market conditions.

Pre-training and transfer learning enhance forecasting performance.

Abstract

Energy is a critical driver of modern economic systems. Accurate energy price forecasting plays an important role in supporting decision-making at various levels, from operational purchasing decisions at individual business organizations to policy-making. A significant body of literature has looked into energy price forecasting, investigating a wide range of methods to improve accuracy and inform these critical decisions. Given the evolving landscape of forecasting techniques, the literature lacks a thorough empirical comparison that systematically contrasts these methods. This paper provides an in-depth review of the evolution of forecasting modeling frameworks, from well-established econometric models to machine learning methods, early sequence learners such LSTMs, and more recent advancements in deep learning with transformer networks, which represent the cutting edge in forecasting. We offer a detailed review of the related literature and categorize forecasting methodologies into four model families. We also explore emerging concepts like pre-training and transfer learning, which have transformed the analysis of unstructured data and hold significant promise for time series forecasting. We address a gap in the literature by performing a comprehensive empirical analysis on these four family models, using data from the EU energy markets, we conduct a large-scale empirical study, which contrasts the forecasting accuracy of different approaches, focusing especially on alternative propositions for time series transformers.