  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.
