Transforming Renewable Energy and Educational Technologies Through AI, Machine Learning, Big Data Analytics, and Cloud-Based IT Integrations

Abstract

This article presents the roles of artificial intelligence (AI) and Internet of Things (IoT) technologies in the renewable energy sector for the optimization and management of smart grids. With rapid advances in AI and IoT, there is enormous potential for developing smart energy systems that integrate clean energy sources and power generation, storage, and consumption. This has been an area of intense research and development activity and broad interest from governments and interested parties to adopt green energy sources and promote the use of electric vehicles (EVs). Affordable access to cloud computing platforms has enabled the development of intelligent technologies and architectures for data storage, processing, analytics, and machine learning with minimal costs. Cloud-based services can significantly reduce the price of sustainable energy systems and the risks associated with smart grid management by protecting the integrity of sensitive data and fostering collaboration among users. Smart meters and a growing number of individual IoT-based sensors and actuators at households, factories, parks, fleets, streets, and polluted areas collect data by constantly taking measurements. These data are transmitted to cloud servers for analytics, for developing AI-enabled algorithms for the prediction, optimization, and control of renewable energy systems.

Distributed energy resource (DER) systems introduce a significant challenge to managing and optimizing renewable energy systems. With the rapid rise of EVs, there is a need to integrate and manage the grid, battery EV charging stations, and solar panels, not only to maintain SWEs but also to optimize GWEs. Similar challenges exist in the management of charging stations and the optimization of distributed generating units, considering demand response. Lots of interesting contributions from control and optimization perspectives have focused on energy dispatching and flexible operations of these systems. However, these efforts are limited by centralized architectures that assume full knowledge of, and computations capabilities over, the grid and DER systems. New distributed approaches are necessary to implement the effectiveness of some beyond the state-of-the-art intelligence. These efforts should also go beyond just IT-enabled automation and augment human cognition. Smart energy systems must be distributed, with lots of computing happening at decentralized autonomous nodes and devices where data and decisions are generated. Such designs call for new machine learning and AI methods capable of constructing intelligent agents from minimal data and computing resources, as well as methods for automatically optimizing human-AI collaborative systems.