Machine Learning en la Optimización del Consumo de Energía en Edificios Inteligentes: Tendencias y Desafíos

Jorge Luis  Veloz Zambrano; Yván Jesús Túpac Valdivia; Augusto Bernuy Alva

doi:10.56124/encriptar.v8i15.011

Autores/as

Jorge Luis Veloz Zambrano Universidad Nacional Mayor San Marcos
Yván Jesús Túpac Valdivia Universidad Católica San Pablo
Augusto Bernuy Alva Universidad de San Martin de Porres

DOI:

https://doi.org/10.56124/encriptar.v8i15.011

Palabras clave:

energía eléctrica, consumo, previsión, optimización

Resumen

A la vanguardia de la revolución tecnológica y sostenible, los edificios inteligentes destacan como modelos de eficiencia y confort, con un crecimiento previsto de 45 millones en 2022 a 115 millones en 2026. Estos edificios utilizan tecnología avanzada para optimizar los recursos, mejorando la seguridad y el bienestar de sus ocupantes. Sin embargo, enfrentan desafíos en infraestructura urbana, equilibrando precisión, escalabilidad y adaptabilidad en el campo de Internet de las cosas. Se enfatiza la importancia de los datos y el aprendizaje automático para predecir y mejorar el consumo de energía, abordando la necesidad de modelos predictivos eficientes y prácticos que manejen datos complejos y capturen patrones espaciotemporales. Se identifican lagunas en la comparación estandarizada de modelos, la eficiencia de los algoritmos predictivos y la transformación de la investigación en aplicaciones prácticas. Este estudio plantea preguntas clave sobre la gestión de recursos y la optimización del consumo de energía a través del aprendizaje automático, proponiendo objetivos específicos como la recopilación de datos y la evaluación de modelos para validar la efectividad y mejorar la vida de los ocupantes, así como contribuir al desarrollo económico, ambiental y social. impacto. Se sigue una metodología sistemática para la revisión de la literatura, empleando la declaración PRISMA, y se plantean preguntas de investigación para guiar la identificación de tendencias y respuestas al uso del aprendizaje automático para predecir el consumo de energía en edificios inteligentes.

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