Dear colleagues,

In recent years, advances and innovations in technology, regulation, and environmental concerns are driving changes in the behavior of electricity companies. A common denominator of all these innovations is the large amount of data involved, from which, once processed and structured, some conclusions can be reached in order to improve the efficiency of the system, reduce fraud, or diminish the level of emissions, among others. Application areas include but are not limited to improving security and quality of service, optimizing maintenance of equipment, forecasting renewable generation, participating in the wholesale market, etc., for which the development of smart grids, as a joint element of electrical technologies and communications, is essential. Most of these data are provided by smart meters and SCADA systems.

This framework is ideal for applying new machine learning techniques to large volumes of data, also known as Big Data. The topic of Big Data Analytics (BDA) refers to the set of these techniques in the field of Artificial Intelligence, although with important contributions from the parallelization of algorithms and distributed databases. The objective of the BDA in the Smart Grids field is to investigate the large volumes of data produced by the various components of the smart grid and transform the data into knowledge, such as operating patterns, alarm trends, and fault detection and control methods. For example, advanced machine learning applications for distribution transformers (a device that increases or decreases the voltage in electrical circuits while maintaining power) analyze the aggregated data in real time for each transformer. The results of these learning applications can help to identify some operational trends leading to failure patterns of these devices and to anticipate future failures and, consequently, provide timely and accurate information for predictive maintenance.

Until now, research efforts in smart grid deployments have focused on advanced metering infrastructure, such as smart meters, communication, information, control, and power management systems for service companies and consumer equipment. Examples would be smart home energy controllers and building monitoring systems. All this is aimed at improving the performance of the electrical network for cost reduction.

Currently, an important part of smart devices is related to the massive deployment of smart home meters that is currently taking place in many countries. The other part of smart grid devices refers to novel network devices such as sensors installed in electrical system networks to monitor key parameters and generation and consumption in real time, control energy flows, exchange of information between them, and allowing local decision making.

In this Special Issue, we invite papers which explore the contribution of data analysis to improving the efficiency of electric utilities, from whichever point of view (economic, environmental, operational, etc.). Contributions can focus both on theoretical developments with proposals for Big Data Analytics and on applications, based on real cases in which the improvements expected from the application of these tools are assessed.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• big data
• Artificial Intelligence
• machine learning, electric industry
• quality of service
• maintenance
• renewable
• network operation
• fraud detection
• smart grids
• losses