Electricity Price Forecasting: The Dawn of Machine Learning

TL;DR

This paper reviews the evolution of electricity price forecasting, highlighting how machine learning techniques have become central to modeling complex market dynamics over the past decade.

Contribution

It provides a comprehensive overview of the main trends and models in electricity price forecasting up to 2022, emphasizing the shift towards machine learning methods.

Findings

Growth of data and computational power enabled complex ML models.

Transition from simple linear models to advanced ML techniques.

Increased accuracy and capability in price prediction.

Abstract

Electricity price forecasting (EPF) is a branch of forecasting on the interface of electrical engineering, statistics, computer science, and finance, which focuses on predicting prices in wholesale electricity markets for a whole spectrum of horizons. These range from a few minutes (real-time/intraday auctions and continuous trading), through days (day-ahead auctions), to weeks, months or even years (exchange and over-the-counter traded futures and forward contracts). Over the last 25 years, various methods and computational tools have been applied to intraday and day-ahead EPF. Until the early 2010s, the field was dominated by relatively small linear regression models and (artificial) neural networks, typically with no more than two dozen inputs. As time passed, more data and more computational power became available. The models grew larger to the extent where expert knowledge was no longer enough to manage the complex structures. This, in turn, led to the introduction of machine learning (ML) techniques in this rapidly developing and fascinating area. Here, we provide an overview of the main trends and EPF models as of 2022.