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- Title
Machine learning for electric power prediction: a systematic literature review.
- Authors
Yandar, Kandel L.; Revelo-Sánchez, Oscar; Bolaños-González, Manuel E.
- Abstract
This study presents a Systematic Literature Review (SLR) on artificial intelligence (AI) techniques applied to electric power prediction. The specialized databases employed in this review are Scopus, IEEE, ACM, and Google Scholar. This analysis provided a perspective on the artificial intelligence techniques utilized in this field, facilitating the identification of current and emerging trends. This offers a clear understanding of upcoming opportunities to enhance accuracy in electric power prediction and decision-making. A notable finding of this review was the predominant usage of Artificial Neural Networks (ANN) as the most prevalent technique in Machine Learning applied to electric power prediction. This preference is justified by the inherent ability of ANN to identify complex patterns and relationships in data, making them a valuable tool for electric power prediction. Additionally, the importance of essential factors in electric power prediction is highlighted, such as the significance of collecting relevant and representative data, encompassing historical and contextual information. Data preprocessing, which involves cleaning and transforming collected data to adequately prepare them for analysis and modeling, and data splitting, crucial for avoiding biases and accurately evaluating the predictive capability of the model, are also emphasized.
- Subjects
ELECTRIC power factor; ARTIFICIAL neural networks; ARTIFICIAL intelligence; ELECTRIC power; ELECTRIC metal-cutting
- Publication
Ingeniería y Competitividad, 2024, Vol 26, Issue 2, p1
- ISSN
0123-3033
- Publication type
Article
- DOI
10.25100/iyc.v26i2.13875