Abstract
Smart grids have been gradually replacing the traditional power grids since the last decade. Such transformation is linked to adding a large number of smart meters and other sources of information extraction units. This provides various opportunities associated with the collected big data. Hence, the triumph of the smart grid energy paradigm depends on the factor of big data analytics. This includes the effective acquisition, transmission, processing, visualization, interpretation, and utilization of big data. The paper provides deep insights into various big data technologies and discusses big data analytics in the context of the smart grid. The paper also presents the challenges and opportunities brought by the advent of machine learning and big data from smart grids.
Highlights
The electrical power system has recently witnessed massive developments
The review paper presents a comprehensive study of technologies and techniques for big data processing
These technologies are applicable when dealing with data from smart grids
Summary
The electrical power system has recently witnessed massive developments. Technical developments have been witnessed in the power generation side and in the transmission and distribution sides. A. BIG DATA SOURCES Data from the smart grids are generated in real-time at a very high rate and volume [12]. An example of batch processing in smart grids includes the training of data-driven models using offline data for applications of topology identification, predictive maintenance, energy forecasting, etc. These models would require re-training if new data become available and need to be included in the modeling performance. The summarization of the different classes of machine learning techniques, that have been previously applied in smart grids, is presented in Table 4, Table 5, and Table 6
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