TY - GEN
T1 - Learning-Enabled Residential Demand Response
T2 - Automation and Security of Cyberphysical Demand Response Systems
AU - Mieth, Robert
AU - Acharya, Samrat
AU - Hassan, Ali
AU - Dvorkin, Yury
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2021/3
Y1 - 2021/3
N2 - Residential Demand Response (DR) Programs have been validated as a viable technology to improve energy efficiency and the reliability of electric power distribution. However, various technical and organizational challenges hinder their full techno-economic potential. In practice, these challenges are related to the small-scale, distributed, heterogeneous, and stochastic nature of residential DR resources. This article investigates state-of-the-art online and reinforcement learning methods that are capable of overcoming these challenges in the context of DR pricing, scheduling, and cybersecurity.
AB - Residential Demand Response (DR) Programs have been validated as a viable technology to improve energy efficiency and the reliability of electric power distribution. However, various technical and organizational challenges hinder their full techno-economic potential. In practice, these challenges are related to the small-scale, distributed, heterogeneous, and stochastic nature of residential DR resources. This article investigates state-of-the-art online and reinforcement learning methods that are capable of overcoming these challenges in the context of DR pricing, scheduling, and cybersecurity.
UR - http://www.scopus.com/inward/record.url?scp=85102442125&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85102442125&partnerID=8YFLogxK
U2 - 10.1109/MELE.2020.3047470
DO - 10.1109/MELE.2020.3047470
M3 - Article
AN - SCOPUS:85102442125
SN - 2325-5897
VL - 9
SP - 36
EP - 44
JO - IEEE Electrification Magazine
JF - IEEE Electrification Magazine
ER -