Smart Meters for Smart Energy: A Review of Business Intelligence Applications

被引:6
作者
Raza, Muhammad Haseeb [1 ]
Rind, Yousaf Murtaza [1 ]
Javed, Isma [1 ]
Zubair, Muhammad [2 ]
Mehmood, Muhammad Qasim [1 ]
Massoud, Yehia [2 ]
机构
[1] Informat Technol Univ ITU, Dept Elect Engn, MicroNano Lab, Lahore 54000, Pakistan
[2] King Abdullah Univ Sci & Technol KAUST, Innovat Technol Labs ITL, Thuwal 23955, Saudi Arabia
关键词
Smart grids; Artificial intelligence; Energy management; Climate change; smart meters; AMI; cloud; business intelligence; artificial intelligence; smart energy; ELECTRICITY THEFT DETECTION; DEMAND-SIDE MANAGEMENT; DISTRIBUTION NETWORKS; NEURAL-NETWORKS; DISTRIBUTION-SYSTEMS; FEATURE-SELECTION; STATE ESTIMATION; BIG DATA; IDENTIFICATION; OPTIMIZATION;
D O I
10.1109/ACCESS.2023.3326724
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Smart Energy (SE) has emerged as a critical technology in tackling global challenges like climate change while addressing the rising energy demands driven by today's data-intensive industrial revolution. SE integrates information and communication technologies into energy systems, optimizing them to meet these challenges effectively. At the core of SE operations are smart meters, playing a fundamental role in ensuring efficient functionality. These devices collect data, which is then leveraged to derive Business Intelligence (BI) for operations across the entire spectrum, from the sensing infrastructure to the cloud, primarily utilizing the Internet of Things (IoT) technology framework. With the increasing complexity of operations and the growing demand for optimization and enhanced functionality, the SE technology stack is evolving to integrate across all layers and domains. This integration has led to the stratification of computational load across IoT layers, intensifying the dependence on smart meter data for BI. Consequently, smart meters themselves have evolved to become more functional and complex. This paper's novelty lies in its comprehensive exploration of the integration of BI with smart meter data. It delves into various aspects, including the different layers of intelligent operations within SE systems, the current state of the art, and diverse implementations of smart meters and their applications across operational locations, ranging from consumers to fog computing. The paper concludes by identifying research gaps and future directions, offering insights into the evolving requirements for the next generation of SE systems and the necessary adaptations in smart metering infrastructure to support these roles. This work contributes to a better understanding of the evolving landscape of data and computation in the context of SE, facilitating more efficient and effective energy management solutions.
引用
收藏
页码:120001 / 120022
页数:22
相关论文
共 50 条
  • [31] A Review of Smart Meter Data Analytics for Distribution Network Applications
    Athanasiadis, Christos L.
    Papadopoulos, Theofilos A.
    Kryonidis, Georgios C.
    2023 IEEE BELGRADE POWERTECH, 2023,
  • [32] Interfacing applications for uncertainty reduction in smart energy systems utilizing distributed intelligence
    Nguyen, Phuong H.
    Blaauwbroek, Niels
    Cuong Nguyen
    Zhang, Xu
    Flueck, Alexander
    Wang, Xiaoyu
    RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2017, 80 : 1312 - 1320
  • [33] Smart meters make smart consumers
    Venables, Mark
    Engineering and Technology, 2007, 2 (04) : 23
  • [34] A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings
    Aguilar, J.
    Garces-Jimenez, A.
    R-Moreno, M. D.
    Garcia, Rodrigo
    RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2021, 151
  • [35] A Review on Machine Learning, Artificial Intelligence, and Smart Technology in Water Treatment and Monitoring
    Lowe, Matthew
    Qin, Ruwen
    Mao, Xinwei
    WATER, 2022, 14 (09)
  • [36] Evaluation and Replacement of Smart Meters
    Chen, Ming
    Gu, Xingchen
    Wang, Yunan
    Ma, Yuan
    Ye, Xinqing
    Li, Jiaqiao
    Liu, Shengyuan
    Lin, Zhenzhi
    Yang, Li
    2020 INTERNATIONAL CONFERENCE ON SMART GRIDS AND ENERGY SYSTEMS (SGES 2020), 2020, : 1005 - 1010
  • [37] Artificial Intelligence Applications in Smart Healthcare: A Survey
    Gao, Xian
    He, Peixiong
    Zhou, Yi
    Qin, Xiao
    FUTURE INTERNET, 2024, 16 (09)
  • [38] A Review of Deep Reinforcement Learning for Smart Building Energy Management
    Yu, Liang
    Qin, Shuqi
    Zhang, Meng
    Shen, Chao
    Jiang, Tao
    Guan, Xiaohong
    IEEE INTERNET OF THINGS JOURNAL, 2021, 8 (15): : 12046 - 12063
  • [39] Artificial Intelligence in Smart Cities-Applications, Barriers, and Future Directions: A Review
    Wolniak, Radoslaw
    Stecula, Kinga
    SMART CITIES, 2024, 7 (03): : 1346 - 1389
  • [40] Smart Energy Management System: Blockchain-Based Smart Meters in Microgrids
    Laayati, Oussama
    El Hadraoui, Hicham
    Bouzi, Mustafa
    El-Alaoui, Ali
    Kousta, Ahmed
    Chebak, Ahmed
    2022 IEEE 4TH GLOBAL POWER, ENERGY AND COMMUNICATION CONFERENCE (IEEE GPECOM2022), 2022, : 580 - 585