Machine learning para la predicción de energía eléctrica: una revisión sistemática de literatura
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Este estudio presenta una Revisión Sistemática de la Literatura (RSL) sobre las técnicas de inteligencia artificial (IA) aplicadas para la predicción de energía eléctrica. Las bases de datos especializadas que se emplearon en esta revisión son Scopus, IEEE, ACM y Google Scholar. Este análisis ofreció una perspectiva sobre las técnicas de inteligencia artificial utilizadas en este campo, lo que facilitó la identificación de las tendencias presentes y en desarrollo. Esto proporciona una comprensión clara de las oportunidades venideras para mejorar la precisión en la predicción de la energía eléctrica y, en consecuencia, en la toma de decisiones.
Un hallazgo destacado de esta revisión fue el predominio del uso de redes neuronales artificiales (RNA) como la técnica más prevalente dentro del campo de Machine Learning aplicado a la predicción de energía eléctrica. Esta preferencia se justifica por la capacidad inherente de las RNA para identificar patrones complejos y relaciones en los datos, lo que las convierte en una herramienta valiosa para la predicción de energía eléctrica. Además, se destaca la importancia de varios factores fundamentales en la predicción de energía eléctrica, como la importancia de recolectar datos relevantes y representativos, que abarquen tanto información histórica como contextual. El preprocesamiento de datos, el cual implica la limpieza y transformación de los datos recopilados para prepararlos adecuadamente para su análisis y modelado y la división de datos, crucial para evitar sesgos y evaluar de manera precisa la capacidad predictiva del modelo.
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Aceptado 2024-06-17
Publicado 2024-07-11
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