Survey on Demand Response in the Landscape of Adaptive and Intelligent Building Energy Management Systems

被引:1
|
作者
Ibrar, Muhammad [1 ]
Abishu, Hayla Nahom [1 ]
Seid, Abegaz Mohammed [1 ]
Marquez-Sanchez, Sergio [2 ]
Fernandez, Javier Hernandez [3 ]
Corchado, Juan Manuel [2 ]
Erbad, Aiman [1 ]
机构
[1] Hamad Bin Khalifa Univ, Coll Sci & Engn, Div Informat & Comp Technol, Doha, Qatar
[2] Univ Salamanca, Salamanca, Spain
[3] Iberdrola Innovat Middle East, Doha 210177, Qatar
来源
20TH INTERNATIONAL WIRELESS COMMUNICATIONS & MOBILE COMPUTING CONFERENCE, IWCMC 2024 | 2024年
关键词
Adaptive Control; Building Energy Management Systems; Energy Consumption; Explainable AI (XAI); Demand Response; Machine Learning;
D O I
10.1109/IWCMC61514.2024.10592593
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
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.
引用
收藏
页码:1203 / 1209
页数:7
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