Dear Colleagues,

An energy management system (EMS) is referred to as a platform to provide monitoring, analysis, and control capacities of energy demand and available energy sources, in a way that minimizes energy consumption and power system operation cost, improves power system reliability, efficiency, and security, as well as meets other service quality requirements.

In recent years, a variety of energy-related software applications have been developed for several purposes, e.g., energy consumption reporting in web applications or an onsite energy display, tracking, and trend analysis to identify cost-saving opportunities, real-time demand, and response via automated control or energy conservation module. Therefore, EMS must be able to deal with massive data volumes originating from smart monitoring and measurement sensors, which are deployed all over the smart grid network. Furthermore, the current EMS must undergo some improvement by adopting advanced computing techniques and technologies in order to:

• Provide on-demand computing resources for energy data lake and processing;
• Guarantee data privacy and security for information exchange and transfer;
• Maintain reliable data communication for real-time energy control and monitoring;
• Provide a service-oriented energy management platform with wide-ranging functionality;
• Detect anomalies and communicates abnormal energy consumption patterns, etc.

In this Special Issue, both academic and industrial researchers are kindly invited to contribute and address advanced technologies and theories as well as the most recent developments on the subject area of “Computing in Energy Management Systems”. Your contribution may describe design models, test campaigns, case-studies, and technological applications of advanced computing techniques and technologies in any scale of EMS implementation.

I look forward to receiving your submissions.

Prof. Deok-Jai Choi
Guest Editor

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• Future computing and applications for EMS
  • Cloud computing
  • Fog computing
  • Mobile edge computing
  • Big data-oriented computing
  • Computing high speed sensing data
  • High-performance computing
• Computational intelligence strategies for EMS
  • Cognitive computing
  • Neural computing
  • Intelligent computation
  • Intentional computing
  • Adaptive computation
• Database and big data management for EMS
  • Big data processing
  • Graph database scheme
  • Data privacy and security
  • Reliable data transfer protocol
  • Machine-learning-based analysis
• Next-generation software architecture for EMS
  • Service-oriented architecture
  • Microservice architecture
  • Software modularity
  • Application programming interface