Intelligent management methods for industrial energy storage

What is Intelligent Energy Management?

1. Introduction Over the last few years, the term intelligent energy management, also called smart energy management, has emerged as a growing idea in the power systems literature. This is due to the rapid increase in energy consumption in today's applications, ranging from industrial to commercial (Nižetić et al., 2020).

Can intelligent energy management systems save energy?

Forecasts show that, over the next two decades, the world's electricity production is expected to increase by over 40 per cent and its demand by approximately 85 per cent (Newell and Raimi, 2020). This has prompted the researchers and vendors to develop various intelligent energy management systems (IEMSs) for saving energy.

Can intelligent energy management systems fill the existing gaps?

New perspectives in the field are proposed to fill the existing gaps. In the last decade, there have been significant developments in the field of intelligent energy management systems (IEMSs), with various methods and new solutions proposed for managing the energy resources intelligently.

What are the benefits of a smart energy management system?

Together, these technologies & intelligent solution patterns offer a powerful combination to track energy consumption, optimize energy usage, predict demand, demand response and demand-side management, micro-grid management, prevent power thefts and improve the efficiency of systems in real-time.

How can computational intelligence be used in energy management systems?

One way to do this is to integrate computational intelligence in energy management systems while enabling technologies, such as machine learning and deep learning, can be used to read the dynamic behaviors of energy consumers.

What are the challenges in implementing intelligent energy management systems?

Challenges in implementation of intelligent energy management systems. The output of an energy management systems is dynamic in nature and difficult to predict because of the dynamic behaviors of consumers and utilities (Yu et al., 2020).