Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

Increasing the Efficiency of Rule-Based Expert Systems Applied on Heterogeneous Data Sources

Nowadays, the proliferation of heterogeneous data sources provided by different research and innovation projects and initiatives is proliferating more and more and presents huge opportunities. These developments create an increase in the number of different data sources, which could be involved in the process of decisionmaking for a specific purpose, but this huge heterogeneity makes this task difficult. Traditionally, the expert systems try to integrate all information into a main database, but, sometimes, this information is not easily available, or its integration with other databases is very problematic. In this case, it is essential to establish procedures that make a metadata distributed integration for them. This process provides a “mapping” of available information, but it is only at logic level. Thus, on a physical level, the data is still distributed into several resources. In this sense, this chapter proposes a distributed rule engine extension (DREE) based on edge computing that makes an integration of metadata provided by different heterogeneous data sources, applying then a mathematical decomposition over the antecedent of rules. The use of the proposed rule engine increases the efficiency and the capability of rule-based expert systems, providing the possibility of applying these rules over distributed and heterogeneous data sources, increasing the size of data sets that could be involved in the decision-making process