Application of Data Mining in Smart Grid Technology

Abstract

Smart grid technology is a radical approach for improvisation in existing power grid. Some of the significant features of smart grid technology are bidirectional communication, AMI, SCADA, renewable integration, active consumer participation, distribution automation, and complete management of entire grid through wireless communication standards and technologies. Management of complex, hierarchical, and heterogeneous smart grid infrastructure requires data collection, storage, processing, analysis, retrieval, and communication for self-healing and complete automation. Data mining techniques can be an effective solution for smart grid operation and management. Data mining is a computational process for data analysis. Data scrutiny is unavoidable for unambiguous knowledge discovery as well as decision making practices. Data mining is inevitable for analysis of various statistics associated with power generation, distribution automation, data communications, billing, consumer participation, and fault diagnosis in smart power grid.

A Robust Data‐Driven Method for Multiseasonality and Heteroscedasticity in Time Series Preprocessing

“…Physical layer choice matters as the emergency level and importance level differ among transported data which should be optimized carefully [15,16]. When the data finally arrive at the centre, data mining algorithms could be applied [17] to analyse and conduct prediction in most cases.…”