Chair: Hamed Mohsenian-Rad @ University of California, Riverside, USA; Co-Chair: Marco Levorato @ University of California, Irvine

The ongoing digitalization, e.g. deployment of advanced metering infrastructure (AMI) and phasor measurement units (PMUs) as well as intelligent automation systems  is drastically increasing the amount, quality, and variety of data that utilities and grid operators are collecting on supply, transmission, distribution, and demand. Therefore, there are limitless opportunities for Big Data Analytics (BDA) in the electric power industry. Big Data (BD) is often defined as a high-volume, high-velocity and high-variety information asset that requires and demands cost-e ective, innovative forms of information collection, storage, and processing for enhanced insight and decision making. The science of BDA is involving and a wide range of methodologies are being developed across multiple disciplines to support BDA. These methodologies can facilitate predictive analytics and forecasting, classification, regression, clustering, cognitive simulation, expert systems, perception, pattern recognition, statistical analysis, natural language processing, and advanced data visualization.

The following is a list of topics that is proposed as the scope of this SIG:

• Strategies for BD visualization in smart grids
• Software and cloud architectures for BDA in smart grids
• Reliable and privacy-preserving data storage to support BDA in smart grids
• Communications technologies to support BDA in smart grids
• Data mining and machine learning for BDA in smart grids
• Applications of BDA in state estimation, event detection, resource aggregation, etc.