IEEE Power & Energy Magazine - Spanish - May/June 2022 - 15

a los posibles errores de medición y
predicción y a la falta de generalización.
Los enfoques basados en el AA
han despertado un gran interés en este
ámbito entre la comunidad de las redes
inteligentes. En el cuarto artículo, " Demostración
de la gestión inteligente de la
carga de HVAC mediante el aprendizaje
profundo por refuerzo " , Y. Du, F. Li, K.
Kurte, J. Munk y H. Zandi hablan de la
gestión de la carga de los edificios para
los sistemas de calefacción, ventilación
y aire acondicionado (HVAC) mediante
el aprendizaje profundo por refuerzo
(AR). Los autores describen y evalúan
dos enfoques de aprendizaje profundo
por refuerzo para el control de HVAC.
Asimismo, discuten el despliegue de un
enfoque de control de HVAC basado en
AR profundo en una casa residencial
real en Knoxville, Tennessee, y luego
analizan los resultados del despliegue,
seguidos de observaciones finales.
Millones de medidores inteligentes
instalados en todo el mundo están generando
una enorme cantidad de perfiles
de carga cada día. El acceso a estos
datos crea oportunidades para que
las empresas de energía comprendan
mejor los patrones de consumo de sus
consumidores. En el quinto artículo,
" Agrupación de consumidores de electricidad:
desafíos y aplicaciones para el
funcionamiento de las redes inteligentes " ,
A. Alonso, E. Martín, A. Mateo,
F. Nogales, C. Ruiz y A. Veiga relacionan
los métodos de agrupación con el
funcionamiento del sistema eléctrico.
En concreto, abordan los aspectos de
eficiencia, robustez y escalabilidad, teniendo
en cuenta los valores atípicos,
los valores perdidos y el volumen de
los datos de los medidores inteligentes.
Los enfoques basados en datos
para el desarrollo de modelos de sistemas
dinámicos se han vuelto muy
relevantes hoy en día para las redes
eléctricas que tienen una alta penetración
de fuentes basadas en inversores
(IBR, por sus siglas en inglés), ya que
la información de las IBR es de propiedad
exclusiva. En el sexto artículo,
" Modelo dinámico basado en datos
aplicado a los sistemas eléctricos " , L.
Fan, Z. Miao, S. Shah, P. Koralewicz,
V. Gevorgian y J. Fu revisan en primer
lugar cinco aplicaciones del modelado
dinámico del sistema eléctrico basado
en datos. La revisión incluye la identificación
de parámetros para generadores
y cargas a través de datos y el análisis
del modo de oscilación a través de
datos de medición fasorial, entre otras
aplicaciones. Por otra parte, los autores
presentan la identificación del modelo
IBR de admitancia en el dominio de la
frecuencia a través de los datos que se
generan a partir de experimentos controlados
y los desafíos en el diseño de
mayo/junio 2022
ieee power & energy magazine
15

http://www.powerworld.com

IEEE Power & Energy Magazine - Spanish - May/June 2022

Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - Spanish - May/June 2022

Contents
IEEE Power & Energy Magazine - Spanish - May/June 2022 - Cover1
IEEE Power & Energy Magazine - Spanish - May/June 2022 - Cover2
IEEE Power & Energy Magazine - Spanish - May/June 2022 - Contents
IEEE Power & Energy Magazine - Spanish - May/June 2022 - 2
IEEE Power & Energy Magazine - Spanish - May/June 2022 - 3
IEEE Power & Energy Magazine - Spanish - May/June 2022 - 4
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IEEE Power & Energy Magazine - Spanish - May/June 2022 - 9
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IEEE Power & Energy Magazine - Spanish - May/June 2022 - 15
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IEEE Power & Energy Magazine - Spanish - May/June 2022 - Cover3
IEEE Power & Energy Magazine - Spanish - May/June 2022 - Cover4
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