Compression of smart meter big data: A survey. (August 2018)

• Record Type: Journal Article
• Authors: Wen, Lulu; Zhou, Kaile; Yang, Shanlin; Li, Lanlan
• Is Part Of: Renewable & sustainable energy reviews. Volume 91(2018)
• Page Start: 59
• Page End: 69
• Publication Date: 2018-08
• Subjects: Smart grid -- Smart meter -- Energy big data -- Data compression; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2018.03.088
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: In recent years, the smart grid has attracted wide attention from around the world. Large scale data are collected by sensors and measurement devices in a smart grid. Smart meters can record fine-grained information about electricity consumption in near real-time, thus forming the smart meter big data. Smart meter big data has provided new opportunities for electric load forecasting, anomaly detection, and demand side management. However, the high-dimensional and massive smart meter big data not only creates great pressure on data transmission lines, but also incur enormous storage costs on data centres. Therefore, to reduce the transmission pressure and storage overhead, improve data mining efficiency, and thus fulfil the potential of smart meter big data. This study presents a comprehensive study on the compression techniques for smart meter big data. The development of smart grids and the characteristics and application challenges of electric power big data are first introduced, followed by analysis of the characteristics and benefits of smart meter big data. Finally, this study focuses on the potential data compression methods for smart meter big data, and discusses the evaluation methods for smart meter big data compression. Highlights: A survey on smart meter big data compression is presented. Characteristics of electric power big data are introduced. Characteristics and benefits of smart meter big data are discussed. Compression methods for smart meter big data are compared. Results evaluation methods for smart meter big data compression are analyzed.