Smart Grid Big Data Analytics: Survey of Technologies, Techniques, and Applications

Syed, Dabeeruddin; Zainab, Ameema; Ghrayeb, Ali; Refaat, Shady S.; Abu‐Rub, Haitham; Bouhali, O.

doi:10.1109/access.2020.3041178

Cited by 97 publications

(44 citation statements)

References 102 publications

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“…Secondly, the accuracy of simple ML models can not be improved with large amounts of data [16], [17]. To tackle these problems, the learning paradigm shifts to DL as the most dazzling flagship of ML for exploiting Big Data (BD) abundance with hierarchical feature extraction, high efficiency, and timely manner [18]. Deep Neural Networks (DNNs) have quickly ascended to the spotlight due to the improvement of computing performance and data capacity.…”

Section: B Emergence Of Deep Learningmentioning

confidence: 99%

Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

et al. 2021

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The current electric power system witnesses a significant transition into Smart Grids (SG) as a promising landscape for high grid reliability and efficient energy management. This ongoing transition undergoes rapid changes, requiring a plethora of advanced methodologies to process the big data generated by various units. In this context, SG stands tied very closely to Deep Learning (DL) as an emerging technology for creating a more decentralized and intelligent energy paradigm while integrating high intelligence in supervisory and operational decision-making. Motivated by the outstanding success of DL-based prediction methods, this article attempts to provide a thorough review from a broad perspective on the state-of-the-art advances of DL in SG systems. Firstly, a bibliometric analysis has been conducted to categorize this review's methodology. Further, we taxonomically delve into the mechanism behind some of the trending DL algorithms. We then showcase the DL enabling technologies in SG, such as federated learning, edge intelligence, and distributed computing. Finally, challenges and research frontiers are provided to serve as guidelines for future work in the futuristic power grid domain. This study's core objective is to foster the synergy between these two fields for decision-makers and researchers to accelerate DL's practical deployment for SG systems. INDEX TERMSSmart grid, deep learning, deep neural networks, edge computing, distributed and federated learning, power systems. NOMENCLATURE Abbreviations DDL Distributed deep learning DL Deep learning DRL Deep reinforcement learning DRN Deep residual network EI Edge intelligence EPS Electric power systems FL Federated learning IoT Internet of things LSTM Long short-term memory neural network The associate editor coordinating the review of this manuscript and approving it for publication was Shadi Alawneh . NN Neural network PVPF Photovoltaic power forecasting D. RESEARCH METHODOLOGY AND SYSTEMATIC REVIEW PROTOCOLStarting from September 2019, the multiple-methods approach was conducted [24]. The collection of the mainstream research papers on SG/AI from Web of Science (WoS), Scopus, IEEE Xplore, Science Direct, and Google scholar was conducted as the largest databases of peerreviewed articles. Only peer-reviewed articles written in English, providing experimental results, and having a unique identifier from the mentioned databases were taken into consideration, including reviews, research articles, patent reports, and conference proceedings. The adopted methodology for conducting this review article employs a combination of keywords categorized into three main groups, specifically, 'Deep Learning', 'Smart Grid', and 'Prediction'. The search methodology focuses on the recent research articles from 2015-2020 to identify the comprehensive statues of the AI applications on SG. The filtering process results in 220 research papers from 600 related papers selected based on their relevance by reading the title, abstract, conclusion,

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Section: B Emergence Of Deep Learningmentioning

confidence: 99%

Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

et al. 2021

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“…It uses distributed computing and grid simulation technology to realize the whole process of power grid simulation, electrical calculation, spatial analysis, topology analysis, etc. Moreover, data are deeply analyzed using approaches, like deep learning and reinforcement learning, to enhance intelligent applications, grid operations, and customer insight (Syed et al, 2021).…”

Section: Platform Layermentioning

confidence: 99%

Smarter Grid in the 5G Era: A Framework Integrating Power Internet of Things With a Cyber Physical System

et al. 2021

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As the energy infrastructure of smart cities, smart grid upgrades traditional power grid systems with state-of-the-art information and communication technologies. In particular, as the full deployment of the Internet of Things in the power grid (a.k.a. power Internet of Things or PIoT), the newly introduced information flow together with inherent energy flow makes it more efficient for power generation, transmission, distribution, and consumption. To further exploit the precious energy and the latest 5G technologies, this article boosts to add a value flow in the smart grid, mainly including the value created by innovative services and market mechanisms and the value added by the information flow. Specifically, by integrating PIoT with cyber-physical systems, this article sketches a conceptual framework of the cyber-physical power system (CPPS). The CPPS carries out holistic perception and ubiquitous connection of distributed energy sources and electrical facilities and builds up a smarter power grid with global information interaction, intelligent decision-making, and real-time agile control. Finally, for illustration purposes, we conduct a case study regarding an intelligent home management system.

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“…These include utility monitoring, integration of smart homes to maximize energy usage, utilization and generation management, etc., [5]. Further, the use of data analytics in smart grids will manage it effectively and increase the system resiliency by providing bidirectional power/information between utilities and customers [10], [11].…”

Section: Importance Challenges and Opportunities Of The Data Analytics In Smart Grid Contextmentioning

confidence: 99%

Simple and effective descriptive analysis of missing data anomalies in smart home energy consumption readings

Prakash¹,

Kumar²

2021

Journal of Energy Systems

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Smart grids evolution is ramping up in the global energy scenario by offering deregulated markets, demandside management, prosumer culture, demand response, contingency forecasting, outage management, etc., functionalities. These functionalities help to manage the grid effectively by taking informed decisions timely. Further, the progressive developments in information and communication technologies improve smartness in the power grids. Especially, smart homes are playing a key role, which possesses the communication between various devices/appliances and collect their functional data in terms of energy consumption readings, timestamp, etc. However, the availability of high-quality data is always desired to achieve superior benefits with respect to all the above-mentioned functionalities. But, the failures of communication networks, metering devices, server station issues, etc., create anomalies in the data collection. Hence, there is a dire need of identifying the ways of analyzing the smart home data to find the irregularities that occurred because of aforesaid failures. Especially, it has been a common problem to see missing data at some particular instants in the overall database captured. In this view, this paper proposes a simple and effective descriptive analysis to find missing data anomalies in smart home energy consumption data. A real-time dataset is used to execute the proposed method. For which, a clear enumeration of missing data is visualized using comprehensive simulation results. This helps to realize the actual problems that are hidden in the energy consumption data.

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Smart Grid Big Data Analytics: Survey of Technologies, Techniques, and Applications

Cited by 97 publications

References 102 publications

Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

Smarter Grid in the 5G Era: A Framework Integrating Power Internet of Things With a Cyber Physical System

Simple and effective descriptive analysis of missing data anomalies in smart home energy consumption readings

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