Advancing Renewable-Dominant Power Systems Through Internet of Things and Artificial Intelligence: A Comprehensive Review

Temitope Adefarati, Gulshan Sharma (), Pitshou N. Bokoro and Rajesh Kumar
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Temitope Adefarati: Department of Electrical and Electronics Engineering Technology, University of Johannesburg, Johannesburg 2094, South Africa
Gulshan Sharma: Department of Electrical and Electronics Engineering Technology, University of Johannesburg, Johannesburg 2094, South Africa
Pitshou N. Bokoro: Department of Electrical and Electronics Engineering Technology, University of Johannesburg, Johannesburg 2094, South Africa
Rajesh Kumar: Department of Human Anatomy and Physiology, Faculty of Health Sciences, University of Johannesburg, Johannesburg 2094, South Africa

Energies, 2025, vol. 18, issue 19, 1-54

Abstract: The sudden increase in global energy demand has prompted the integration of Artificial Intelligence and the Internet of Things into the utility grid. The synergy of Artificial Intelligence and the Internet of Things in renewable energy sources has emerged as a promising solution for the development of smart grids and a transformative catalyst that restructures centralized power systems into resilient and sustainable systems. The state-of-the-art of the Internet of Things and Artificial Intelligence is presented in this paper to support the design, planning, operation, management and optimization of renewable energy-based power systems. This paper outlines the benefits of smart and resilient energy systems and the contributions of the Internet of Things across several applications, devices and networks. Artificial Intelligence can be utilized for predictive maintenance, demand-side management, fault detection, forecasting and scheduling. This paper highlights crucial future research directions aimed at overcoming the challenges that are associated with the adoption of emerging technologies in the power system by focusing on market policy and regulation and the human-centric and ethical aspects of Artificial Intelligence and the Internet of Things. The outcomes of this study can be used by policymakers, researchers and development agencies to improve global access to electricity and accelerate the development of sustainable energy systems.

Keywords: artificial intelligence; deep learning; greenhouse gas emissions; internet of things; machine learning; renewable energy sources; smart grid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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