TY - GEN
T1 - Survey on Demand Response in the Landscape of Adaptive and Intelligent Building Energy Management Systems
AU - Ibrar, Muhammad
AU - Abishu, Hayla Nahom
AU - Seid, Abegaz Mohammed
AU - Márquez-Sánchez, Sergio
AU - Fernandez, Javier Hernandez
AU - Corchado, Juan Manuel
AU - Erbad, Aiman
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024/5/31
Y1 - 2024/5/31
N2 - Demand response (DR) plays a significant role in modern energy management systems, particularly within the context of adaptive and intelligent building energy management systems (AI-BEMS). In the AI-BEMS context, DR focuses on dynamically adjusting energy usage in response to external factors, such as electricity prices, grid conditions, and environmental considerations. This survey paper explores the evolving landscape of DR within the framework of AI-BEMS, focusing on the integration of advanced technologies and adaptive strategies to optimize energy consumption and enhance grid reliability. This article reviews state-of-the-art research addressing the key concepts associated with integrating DR and AI-BEMS, including an overview of DR techniques in AI-BEMS, and an artificial intelligence and machine learning applications for the development of adaptive control strategies and DR optimization. Then, insights are provided on the future directions and the challenges in this field regarding the implementation of DR within AI-BEMS.
AB - Demand response (DR) plays a significant role in modern energy management systems, particularly within the context of adaptive and intelligent building energy management systems (AI-BEMS). In the AI-BEMS context, DR focuses on dynamically adjusting energy usage in response to external factors, such as electricity prices, grid conditions, and environmental considerations. This survey paper explores the evolving landscape of DR within the framework of AI-BEMS, focusing on the integration of advanced technologies and adaptive strategies to optimize energy consumption and enhance grid reliability. This article reviews state-of-the-art research addressing the key concepts associated with integrating DR and AI-BEMS, including an overview of DR techniques in AI-BEMS, and an artificial intelligence and machine learning applications for the development of adaptive control strategies and DR optimization. Then, insights are provided on the future directions and the challenges in this field regarding the implementation of DR within AI-BEMS.
KW - Adaptive Control
KW - Building Energy Management Systems
KW - Demand Response
KW - Energy Consumption
KW - Explainable AI (XAI)
KW - Machine Learning
UR - https://www.scopus.com/pages/publications/85199969889
U2 - 10.1109/IWCMC61514.2024.10592593
DO - 10.1109/IWCMC61514.2024.10592593
M3 - Conference contribution
AN - SCOPUS:85199969889
SN - 979-8-3503-6127-8
T3 - 20th International Wireless Communications and Mobile Computing Conference, IWCMC 2024
SP - 1203
EP - 1209
BT - 20th International Wireless Communications and Mobile Computing Conference, IWCMC 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2024
Y2 - 27 May 2024 through 31 May 2024
ER -