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Review

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

1
Quobis, 36380 O Porriño, Spain
2
atlanTTic, Universidade de Vigo, 36310 Vigo, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Paweł Pławiak
Sensors 2021, 21(13), 4544; https://doi.org/10.3390/s21134544
Received: 19 May 2021 / Revised: 18 June 2021 / Accepted: 25 June 2021 / Published: 2 July 2021
(This article belongs to the Section Internet of Things)
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. View Full-Text
Keywords: electricity demand forecast; machine learning; artificial neural networks; systematic review electricity demand forecast; machine learning; artificial neural networks; systematic review
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MDPI and ACS Style

Román-Portabales, A.; López-Nores, M.; Pazos-Arias, J.J. Systematic Review of Electricity Demand Forecast Using ANN-Based Machine Learning Algorithms. Sensors 2021, 21, 4544. https://doi.org/10.3390/s21134544

AMA Style

Román-Portabales A, López-Nores M, Pazos-Arias JJ. Systematic Review of Electricity Demand Forecast Using ANN-Based Machine Learning Algorithms. Sensors. 2021; 21(13):4544. https://doi.org/10.3390/s21134544

Chicago/Turabian Style

Román-Portabales, Antón, Martín López-Nores, and José J. Pazos-Arias. 2021. "Systematic Review of Electricity Demand Forecast Using ANN-Based Machine Learning Algorithms" Sensors 21, no. 13: 4544. https://doi.org/10.3390/s21134544

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