Systematic review of electricity demand forecast using ANN-based machine learning algorithms
UNIVERSAL IDENTIFIER: http://hdl.handle.net/11093/2602
EDITED VERSION: https://www.mdpi.com/1424-8220/21/13/4544
UNESCO SUBJECT: 1203.02 Lenguajes Algorítmicos ; 3325 Tecnología de las Telecomunicaciones ; 3306 Ingeniería y Tecnología Eléctricas
DOCUMENT TYPE: article
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, the results attained by their algorithms, and 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.
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