Open Access
ARTICLE
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:
Journal of Quantum Computing 2020, 2(4), 193-202. https://doi.org/10.32604/jqc.2020.016151
Received 08 November 2020; Accepted 27 December 2020; Issue published 07 January 2021
Abstract
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.
Keywords
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. https://doi.org/10.32604/jqc.2020.016151
Citations