A Qualitative Study on the United States Internet of Energy: A Step Towards Computational Sustainability

Sakib, Nazmus; Hossain, Eklas; Ahamed, Sheikh Iqbal

doi:10.1109/access.2020.2986317

Cited by 30 publications

(9 citation statements)

References 143 publications

(154 reference statements)

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“…Therefore, DDL has potential applications in future smart grids for reducing ANN computation time and decreasing huge data centralization dependency. At the same time, IoT is a very essential network mechanism in the smart grid system [20]. Cloud of Things [21] is a cloud layer of IoT used for monitoring, managing, and analyzing data and information provided by using a cloud server [22] [23].…”

Section: Deep Learning Process Contains Artificialmentioning

confidence: 99%

HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

et al. 2020

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Load forecasting is a vital part of smart grids for predicting the required electrical power using artificial intelligence (AI). Deep learning is broadly used for load forecasting in the smart grid using the artificial neural network (ANN). Generally, computing the deep learning in the smart grid requires massive data aggregation or centralization and significant computational time. This paper presents a survey of deep learning-based load forecasting techniques from 2015 to 2020. This survey discusses the studies based on their deep learning techniques, Distributed Deep Learning (DDL) techniques, Back Propagation (BP) based works, and non-BP based works in the load forecasting process. Consequent to the survey, it was determined that data aggregation dependency would be beneficial for reducing computational time in load forecasting. Therefore, a conceptual model of DDL for smart grids has been presented, where the HSIC (Hilbert-Schmidt Independence Criterion) Bottleneck technique has been incorporated to provide higher accuracy.

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Section: Deep Learning Process Contains Artificialmentioning

confidence: 99%

HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

et al. 2020

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“…The main contribution of our paper is in demonstrating that 5G wireless networks might help to efficiently manage and enhance the demand-side response in the high-renewables energy systems and smart grids using a comprehensive evidence from the research literature and other sources which is meaningful and worth research efforts (see e.g., Xu et al, 2019;Sakib et al, 2020). Furthermore, the paper provides a comparison between advantages and challenges of 5G networks in demandresponse renewable energy grids.…”

Section: Introductionmentioning

confidence: 99%

5G Wireless Networks in the Future Renewable Energy Systems

et al. 2021

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This paper focuses on the strategies that employ the fifth generation (5G) wireless networks in the optimal management of demand-side response in the future energy systems with the high penetration of renewable energy sources (RES). It also provides a comparison between advantages and challenges of 5G networks in demand-response renewable energy grids. Large-scale renewable energy integration always leads to a mismatch between generation and load demand in the short run due to the intermittency. It is often envisioned that 5G wireless networks that were recently launched and would most likely be fully deployed worldwide by 2035 would bring many technological and economic benefits for a plethora of the future high-renewables grids featuring electric transport and heating as well as prosumers generating renewable energy and trading it back to the grid (for example, in the vehicle-to-grid (V2G) framework) and among themselves using peer-to-peer (P2P) networks. Our paper offers a comprehensive analysis of 5G architecture with the perspectives of optimal management of demand-side response in the smart grids of the future. We show that the effective deployment of faster and more reliable wireless networks would allow faster data transfers and processing, including peer-to-peer (P2P) energy trade market, Internet of Vehicles (IoV) market, or faster smart metering, and thence open the path for the full-fledged Internet of Energy (IoE). Moreover, we show that 5G wireless networks might become in the future sustainable energy systems paving the road to even more advanced technologies and the new generations of networks. In addition, we demonstrate that for the effective management of energy demand-side response with a high share of renewables, certain forms of governments funding and incentives might be needed. These are required to strengthen the support of RES and helping to shift to the green economy.

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“…In the power system, the IoT enabled smart grid is becoming more popular [10]. In smart grid topologies, the real-time monitoring system along with a high standard load forecasting model are very important [11].…”

Section: Introductionmentioning

confidence: 99%

TFBN: A Cost Effective High Performance Hierarchical Interconnection Network

et al. 2020

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In order to fulfill the increasing demand for computation power to process a boundless data concurrently within a very short time or real-time in many areas such as IoT, AI, machine learning, smart grid, and big data analytics, we need exa-scale or zetta-scale computation in the near future. Thus, to have this level of computation, we need a massively parallel computer (MPC) system that shall consist of millions of nodes; and, for the interconnection of these massive numbers of nodes, conventional topologies are infeasible. Thus, a hierarchical interconnection network (HIN) is a rational way to connect huge nodes. Through this article, we are proposing a new HIN, which is a tori-connected flattened butterfly network (TFBN) for the next generation MPC system. Numerous basic modules are hierarchically interconnected as a toroidal connection, whereby the basic modules are flattened butterfly networks. We have studied the network architecture, static network performance, and static cost-effectiveness of the proposed TFBN in detail; and compared static network and cost-effectiveness performance of the TFBN to those of TTN, torus, TESH, and mesh networks. It is depicted that TFBN possesses low diameter and average distance, high arc connectivity, and temperate bisection width. It also has better cost-effectiveness and cost-performance trade-off factor compared to those of TTN, torus, TESH, and mesh networks. The only shortcoming is that the complexity of wiring of the TFBN is higher than that of those networks; this is because the basic module necessitates some extra short length link to form the flattened butterfly network. Therefore, TFBN is a high performance and cost-effective HIN, and it will be a good option for the next generation MPC system.

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A Qualitative Study on the United States Internet of Energy: A Step Towards Computational Sustainability

Cited by 30 publications

References 143 publications

HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

5G Wireless Networks in the Future Renewable Energy Systems

TFBN: A Cost Effective High Performance Hierarchical Interconnection Network

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