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Big Data of Home Energy Management in Cloud Computing

Rizwan Munir1,*, Yifei Wei1, Rahim Ullah2, Iftikhar Hussain3, Kaleem Arshid4, Umair Tariq1

1 Beijing University of Posts and Telecommunications, Beijing, 10086, China
2 Higher Education Department, Peshawar, Khyber Pakhtunkhwa, 25000, Pakistan
3 School of Computer and Information Technology, Beaconhouse National University, Lahore, 53700, Pakistan
4 Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

* Corresponding Author: Rizwan Munir. Email: email

Journal of Quantum Computing 2020, 2(4), 193-202.


A smart grid is the evolved form of the power grid with the integration of sensing, communication, computing, monitoring, and control technologies. These technologies make the power grid reliable, efficient, and economical. However, the smartness boosts the volume of data in the smart grid. To obligate full benefits, big data has attractive techniques to process and analyze smart grid data. This paper presents and simulates a framework to make sure the use of big data computing technique in the smart grid. The offered framework comprises of the following four layers: (i) Data source layer, (ii) Data transmission layer, (iii) Data storage and computing layer, and (iv) Data analysis layer. As a proof of concept, the framework is simulated by taking the dataset of three cities of the Pakistan region and by considering two cloud-based data centers. The results are analyzed by taking into account the following parameters: (i) Heavy load data center, (ii) The impact of peak hour, (iii) High network delay, and (iv) The low network delay. The presented framework may help the power grid to achieve reliability, sustainability, and costefficiency for both the users and service providers.


Cite This Article

R. Munir, Y. Wei, R. Ullah, I. Hussain, K. Arshid et al., "Big data of home energy management in cloud computing," Journal of Quantum Computing, vol. 2, no.4, pp. 193–202, 2020.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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