Big Data Issues in Smart Grids: A Survey

Ghorbanian, Maedeh; Dolatabadi, Sarineh Hacopian; Siano, Pierluigi

doi:10.1109/jsyst.2019.2931879

Cited by 98 publications

(37 citation statements)

References 88 publications

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“…Usage of big data analysis [24] and intelligent systems in smart grid systems helps to solve the challenge of processing and to manage the huge amount of data. Various challenges associated with usage of big data analysis in smart grids are highlighted in [25]. The authors also enlist various applications of big data in smart grid systems.…”

Section: Literature Surveymentioning

confidence: 99%

A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid

et al. 2020

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The grid denotes the electric grid which consists of communication lines, control stations, transformers, and distributors that aids in supplying power from the electrical plant to the consumers. Presently, the electric grid constitutes humongous power production units which generates millions of megawatts of power distributed across several demographic regions. There is a dire need to efficiently manage this power supplied to the various consumer domains such as industries, smart cities, household and organizations. In this regard, a smart grid with intelligent systems is being deployed to cater the dynamic power requirements. A smart grid system follows the Cyber-Physical Systems (CPS) model, in which Information Technology (IT) infrastructure is integrated with physical systems. In the scenario of the smart grid embedded with CPS, the Machine Learning (ML) module is the IT aspect and the power dissipation units are the physical entities. In this research, a novel Multidirectional Long Short-Term Memory (MLSTM) technique is being proposed to predict the stability of the smart grid network. The results obtained are evaluated against other popular Deep Learning approaches such as Gated Recurrent Units (GRU), traditional LSTM and Recurrent Neural Networks (RNN). The experimental results prove that the MLSTM approach outperforms the other ML approaches. INDEX TERMS Multidirectional long short-term memory (MLSTM), machine learning (ML), smart grid (SG), cyber physical systems (CPS).

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Section: Literature Surveymentioning

confidence: 99%

A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid

et al. 2020

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“…In order to support real-time energy management, the authors in [54] intelligently process large amounts of power data in an efficient way. In the aspects of energy resources, load forecasting, disaster recovery, efficient data management, and analysis frameworks are critical to grid-optimized operations [55]- [57]. Public utilities use a variety of disaster recovery technologies to enhance customer engagement in power grid management [58], [59].…”

Section: A Intelligent Power Data Analysismentioning

confidence: 99%

Review of Power Spatio-Temporal Big Data Technologies for Mobile Computing in Smart Grid

Huang

Sun

et al. 2019

IEEE Access

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Mobile computing adds computing, storage, processing and other functions in wireless network ends to provide customized and differentiated services, so that it can be widely applied in the fields of internet of things, video, medical treatment, retail and so on. Recently, power spatio-temporal big data (PSTBD) technology of smart grid based on mobile computing has experienced explosive growth. This paper emphasizes the specific requirements, technologies, applications, and challenges of the current PSTBD for mobile computing in smart grid. Based on current development status of PSTBD companies in representative countries in the world, we introduce PSTBD technology based on the characteristics of mobile computing based smart grid, and conduct a comprehensive investigation and analysis of relevant articles in this field. After comparing the differences between the traditional and the PSTBD based platform in the aspects of important features, platform goal, and platform architecture, we describe the key technologies and algorithms of the current PSTBD in detail. Then, based on the requirements of each link and field of the power grid, the typical application of PSTBD technology in various aspects of smart grid application based on mobile computing is discussed. Finally, the development direction and challenges of PSTBD are given. Through data analysis and technical discussion, it hopes to provide technical support and decision support for relevant practitioners in the PSTBD field.INDEX TERMS Mobile computing, data processing, smart grids, spatio-temporal big data. I. INTRODUCTIONPower spatio-temporal big data based on mobile computing refers to ''power+mobile equipment+big data'', which collects and processes multi-source, heterogeneous, multi-dimensional and multi-form spatio-temporal big data in various links from generation, transmission, transformation, distribution, power consumption to dispatching power production and power service. The characteristics of Power Spatio-Temporal Big Data (PSTBD) meet the ''5V3E'' characteristics [4]-[6], as shown in Fig. 1. In addition to ''3E''The associate editor coordinating the review of this manuscript and approving it for publication was Xuxun Liu .which is energy, exchange, and empathy, the ''5V'' is as follows.Volume: Conventional power dispatching system includes hundreds of thousands of data collection points; the number of power distribution data centers often reaches tens of millions; data volume is often above TB and PB.Velocity: Decision support requires analysis of large amounts of data in a fraction of a second; real-time processing requires continuous real-time data generation.Variety: Data types are structured, semi-structured, and unstructured data, including real-time data, historical data, text data, multimedia data, time-series data and so on.

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“…For example, every one second there occur 8,910 new Tweets, 89,845GB usage of internet traffic, 81,734 Google searches, etc. [3] [3] [24] [23]. Furthermore, as depicted in Figure 2.1, the digital universe data is expected to grow by a factor of 10 by 2020s [4].…”

Section: Introductionmentioning

confidence: 99%

Introduction to Big Data Technology

Abu-Salih

Wongthongtham

Zhu

et al. 2021

Social Big Data Analytics

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Big data is no more "all just hype" but widely applied in nearly all aspects of our business, governments, and organizations with the technology stack of AI. Its influences are far beyond a simple technique innovation but involves all rears in the world. This chapter will first have historical review of big data; followed by discussion of characteristics of big data, i.e. from the 3V's to up 10V's of big data. The chapter then introduces technology stacks for an organization to build a big data application, from infrastructure/platform/ecosystem to constructional units and components. Finally, we provide some big data online resources for reference.

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Big Data Issues in Smart Grids: A Survey

Cited by 98 publications

References 88 publications

A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid

A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid

Review of Power Spatio-Temporal Big Data Technologies for Mobile Computing in Smart Grid

Introduction to Big Data Technology

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