Working Paper Review Version 1 This version is not peer-reviewed

Systematic Review of Electricity Demand Forecast Using ANN-Based Machine Learning Algorithms

Version 1 : Received: 19 May 2021 / Approved: 21 May 2021 / Online: 21 May 2021 (09:48:10 CEST)

How to cite: Román-Portabales, A.; López-Nores, M.; Pazos-Arias, J. Systematic Review of Electricity Demand Forecast Using ANN-Based Machine Learning Algorithms. Preprints 2021, 2021050514 Román-Portabales, A.; López-Nores, M.; Pazos-Arias, J. Systematic Review of Electricity Demand Forecast Using ANN-Based Machine Learning Algorithms. Preprints 2021, 2021050514

Abstract

The forecast of electricity demand has been a recurrent research topic for decades, due to its economical and strategic relevance. Several Machine Learning (ML) techniques have evolved in parallel with the complexity of the electric grid. This paper reviews a wide selection of approaches that have used Artificial Neural Networks (ANN) to forecast electricity demand, aiming to help newcomers and experienced researchers to appraise the common practices and to detect areas where there is room for improvement in the face of the current widespread deployment of smart meters and sensors, which yields an unprecedented amount of data to work with. The review looks at the specific problems tackled by each one of the selected papers, at the results attained by their algorithms, and at the strategies followed to validate and compare the results. This way, it is possible to highlight some peculiarities and algorithm configurations that seem to consistently outperform others in specific settings.

Subject Areas

Electricity demand forecast; Machine Learning; Artificial Neural Networks; systematic review.

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.