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

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.

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Publication Details

Journal
Energies
Published
2025-10-02
DOI
https://doi.org/10.3390/en18195243
Citations
2
Primary Topic
Smart Grid Energy Management
Type
article
Field-Weighted Citation Impact
1.46
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article

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

Gulshan Sharma, Rajesh Kumar, T. Adefarati, Pitshou Ntambu Bokoro
2 citations
Energies
Smart Grid Energy Management
1.46
article

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

Gulshan Sharma, Rajesh Kumar, T. Adefarati, Pitshou Ntambu Bokoro
article en
2 citations

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.

EnergiesVol. 18(19)
University of Johannesburg (ZA), Malaviya National Institute of Technology Jaipur (IN)
Openalex Percentile: Top 15%
Smart Grid Energy Management
1.46
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Advancing Renewable-Dominant Power Systems Through Internet of Things and Artificial Intelligence: A Comprehensive Review — Gulshan Sharma, Rajesh Kumar, et al. · Energies (2025) | TGRS Research Map | TGRS