Electricity Price Forecasting: The Dawn of Machine Learning

Arkadiusz J\k{e}drzejewski, Jesus Lago, Grzegorz Marcjasz and Rafał Weron

Papers from arXiv.org

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.

Date: 2022-04
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ene, nep-for and nep-ore
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Published in IEEE Power & Energy Magazine 20(3) (2022) 24-31

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