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## Big data analytics for smart grids

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The Internet of Things (IoT) has recently emerged as an enabling technology for the next-generation electricity grid, namely, smart grid (SG). The efficient operation of the smart electricity grid depends on the efficient acquiring, analyzing, and processing of a large volume of data generated by the utilized smart sensors, individual smart meters, energy-consumption schedulers, aggregators, solar radiation sensors, wind-speed meters, and relays. In order to deal with the extreme size of data, the adoption of advanced data analytics, big data management, and powerful monitoring techniques is required. This approach creates huge opportunities and challenges, especially considering the real-time monitoring, load, renewable energy, and prices forecasting, identification and prediction of faults, and integration of electric vehicles, functioning in a mobile SG environment. Among others, intelligent algorithms, robust data analytics, high performance computing (HPC), efficient data network management, and cloud computing (CC) techniques are critical toward the optimized operation of SG. This chapter presents the big data issues faced by SG networks and the corresponding solutions.

Chapter Contents:

• 8.1 Introduction
• 8.2 Dynamic energy management
• 8.2.1 Demand side management
• 8.2.2 Data-driven DEM
• 8.3 Failure protection
• 8.4 Load and price forecasting
• 8.4.3 Renewable generation forecasting
• 8.4.4 Price forecasting
• 8.4.5 Predictive control for electric vehicles power demand
• 8.5 Efficient processing of extreme size of data
• 8.5.1 Avoidance of redundancies
• 8.5.2 Dimensionality reduction
• 8.5.3 Data summarization
• 8.5.4 MapReduce parallel processing
• 8.5.5 Distributed data mining
• 8.5.6 Efficient computing
• 8.5.7 Testbeds and platforms
• 8.6 Security and privacy issues in the smart grid
• 8.6.1 Privacy
• 8.6.2 Security
• 8.7 Conclusions
• References

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