Applications of IoT and Cloud Computing in Smart Grids

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 2673

Special Issue Editors

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Guest Editor
School of Science and Tehnology, Nottingham Trent University, Nottingham, UK
Interests: big data applications; smart grid analytics; cloud computing

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Guest Editor
Ming Hsieh Department of Electrical and Computer Engineering,University of Southern California, Los Angeles, CA, USA
Interests: data driven analytics and optimization; smart grids; reinforcement learning

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Guest Editor
Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India
Interests: distributed systems; Internet of Things; cloud computing; edge computing; graph processing

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Guest Editor
School of Technology, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil
Interests: resource management in cluster; computational grids and clouds; parallel and distributed processing (high performance computing); virtualization; computer architecture

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Guest Editor
SLAC National Accelerator Laboratory, Menlo Park, CA, USA
Interests: data management; data security; cloud computing; cyber physical system security

Special Issue Information

Dear Colleagues,

We are on the verge of the so-called fourth industrial revolution. Recent advances in cyber-physical systems are enabling the acquisition, delivery, and analysis of increasingly large volumes of data. Cloud computing, with its seamless approach to scalable computing, has emerged as a suitable solution for businesses running data analytics involving machine learning tasks that would otherwise not be feasible using traditional computers. The increasing number of sensors has showcased the potential of IoT in making our world safer, more accessible, and comfortable. However, until the advent of 5G, there was no suitable means of making these two worlds communicate efficiently. The energy sector with its smart grids is such an example, where, for instance, (1) data from tens of thousands of sensors from customers are gathered by providers to gain meaningful insight on real-time demand optimization (2) and real-time monitoring and predictive maintenance of remote renewable energy sources is needed for optimizing the load balancing. For a significant period, transmitting this data in near real-time over Wi-Fi and wireless has been hindered by the network capability. Software-driven solutions involving on edge or fog preprocessing combined with periodic cloud-based analytics of the resulting metadata have been a key focus of research. The advent of 5G opens new opportunities for at-scale bi-directional delivery and analysis of real-time customer and utility data.

In this Special Issue, we aim at bringing together researchers and practitioners from the field of smart grids to showcase state-of-the-art cyber-physical solutions to current challenges in smart grid analytics. These should cover the full spectrum of hardware (sensors, networks), software (middleware, platforms, applications), and data (storage, privacy, security) aspects for fast scalable data analytics involving the entire cyber-physical stack, from IoT to cloud systems. We, therefore, invite contributors to present their models, frameworks, and applications with a focus on hybrid IoT/cloud computing for smart grids with all its challenges.

Dr. Marc Frincu
Dr. Sanmukh Rao Kuppannagari
Prof. Dr. Yogesh Simmhan
Prof. Dr. Cesar A. F. De Rose
Dr. Mayank Malik
Guest Editors

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  • Data analytics
  • Smart grid
  • Near real-time processing
  • Cloud computing
  • Edge computing
  • Machine learning
  • Data privacy and security

Published Papers (1 paper)

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20 pages, 20053 KiB  
A Solar Radiation Forecast Platform Spanning over the Edge-Cloud Continuum
by Marc Frincu, Marius Penteliuc and Adrian Spataru
Electronics 2022, 11(17), 2756; - 1 Sep 2022
Cited by 2 | Viewed by 1367
The prediction of PV output represents an important task for PV farm operators as it enables them to forecast the energy they will produce and sell on the energy market. Existing approaches rely on a combination of satellite/all-sky images and numerical methods which [...] Read more.
The prediction of PV output represents an important task for PV farm operators as it enables them to forecast the energy they will produce and sell on the energy market. Existing approaches rely on a combination of satellite/all-sky images and numerical methods which for high spatial resolutions require considerable processing time and resources. In this paper, we propose a hybrid egde–cloud platform that leverages the performance of edge devices to perform time-critical computations locally, while delegating the rest to the remote cloud infrastructure. The proposed platform relies on novel metaheuristics algorithms for cloud dynamics detection and proposes to forecast irradiance by analyzing pixel values taken with various filters/bands. The results demonstrate the scalability improvement when using GPU-enabled devices and the potential of using pixel information instead of cloud types to infer irradiance. Full article
(This article belongs to the Special Issue Applications of IoT and Cloud Computing in Smart Grids)
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