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Resumen
El acceso y uso ilegal de la electricidad, conocido como robo de electricidad, representa una amenaza significativa para la industria energética y la sociedad en su conjunto. Este fenómeno fraudulento socava la integridad del sistema eléctrico, afecta negativamente a los proveedores de servicios y tiene graves consecuencias económicas, sociales y ambientales. Además, los consumos anómalos de electricidad también plantean desafíos importantes en términos de detección temprana de irregularidades y optimización del consumo energético. En esta investigación, se analizaron las causas y motivaciones que impulsan el robo de electricidad. Asimismo, se examinó los diversos métodos utilizados por los infractores para manipular los medidores de energía y ocultar sus actividades fraudulentas, poniendo de relieve la necesidad de soluciones innovadoras para combatir esta problemática. De todas las soluciones existentes nos focalizamos en el análisis y predicción de consumos mediante técnicas de Machine Learning. El empleo de modelos algorítmicos de aprendizaje automático se explora como una herramienta clave para detectar y prevenir el robo de electricidad y consumos anómalos. Presentando una mejora importante en la detección del hurto de la energía eléctrica.
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Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Derechos de autor 2024 Pedro Martin Lezama Gonzales, Orestes Cachay Boza, Máximo Zevallos León