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E-Ciencias de la Información

On-line version ISSN 1659-4142


SANCHEZ SOLIS, Joseline  and  COTO JIMENEZ, Marvin. State of the art of Predicting Electrical Engineering Variables Based on Artificial Intelligence. E-Ciencias de la Información [online]. 2022, vol.12, n.1, pp.59-78. ISSN 1659-4142.

In many systems that are studied and developed in the field of Electrical Engineering, analyzes are carried out that have as one of their main purposes the prediction of their variables, both for planning and decision-making processes. With the advent of Artificial Intelligence, it has been observed how different techniques related to machine learning and optimization have been incorporated into these prediction tasks. Those new techniques generally obtained better results in the estimation of values ​​than those generated from more traditional techniques. The objective of this research is to review what has been published on predictions of variables in Electrical Engineering systems in the databases EBSCO, SciELO, RedAlyc, Springer Link, IEEE Xplorer, and Google Scholar, given specific temporal and keyworks delimitations for the area. From the analysis of the literature, the trend on the subject was obtained from the most productive years, areas of impact, and most frequent languages. It was observed that the studies developed have grown in recent years and that the areas of greatest impact, according to the number of publications and citations, are the prediction of electricity consumption and production, and the variables related to renewable energy.

Keywords : Artificial intelligence; electrical variables prediction; Electrical Engineering.

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