4.0 Review

Big data analytics in smart grids: state-of-the-art, challenges, opportunities, and future directions

期刊

IET SMART GRID
卷 2, 期 2, 页码 141-154

出版社

INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-stg.2018.0261

关键词

Big Data; data analysis; smart power grids; power system planning; power engineering computing; big data analytics; power system planning; operational decision framework; power grid sector; power grid technologies; heterogeneous big data sets; computational complexity; data security; data integration

向作者/读者索取更多资源

Big data has potential to unlock novel groundbreaking opportunities in power grid that enhances a multitude of technical, social, and economic gains. As power grid technologies evolve in conjunction with measurement and communication technologies, this results in unprecedented amount of heterogeneous big data. In particular, computational complexity, data security, and operational integration of big data into power system planning and operational frameworks are the key challenges to transform the heterogeneous large dataset into actionable outcomes. In this context, suitable big data analytics combined with visualization can lead to better situational awareness and predictive decisions. This paper presents a comprehensive state-of-the-art review of big data analytics and its applications in power grids, and also identifies challenges and opportunities from utility, industry, and research perspectives. The paper analyzes research gaps and presents insights on future research directions to integrate big data analytics into power system planning and operational frameworks. Detailed information for utilities looking to apply big data analytics and insights on how utilities can enhance revenue streams and bring disruptive innovation are discussed. General guidelines for utilities to make the right investment in the adoption of big data analytics by unveiling interdependencies among critical infrastructures and operations are also provided.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.0
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据