TITLE:
Optimizing Energy Infrastructure with AI Technology: A Literature Review
AUTHORS:
Oyeniyi Richard Ajao
KEYWORDS:
Artificial Intelligence, Energy Infrastructure Optimization, Smart Grid Management, Predictive Maintenance, Renewable Energy Variability, Machine Learning, Cybersecurity in Energy, Public-Private Partnerships, Post-Quantum Cryptography, Elliptic Curve Cryptography
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.14 No.12,
December
13,
2024
ABSTRACT: The world’s energy industry is experiencing a significant transformation due to increased energy consumption, the rise in renewable energy usage, and the demand for sustainability. This review paper explores the potential for transformation offered by Artificial Intelligence (AI) in improving energy infrastructure, specifically looking at how it can be used in managing smart grids, predicting maintenance needs, and integrating renewable energy sources. Machine learning (ML) and deep learning (DL) are crucial AI technologies that have become necessary for enhancing grid stability, reducing operational costs, and improving energy efficiency. AI-powered predictive maintenance has proven to lower unexpected downtime by 40%, while AI-based demand forecasting has reached prediction accuracy of 90%, allowing utilities to efficiently manage supply and demand. In addition, AI helps tackle the issues of fluctuating renewable energy by playing a key role in enhancing energy storage and distribution in nations like Denmark and the US. Moreover, cryptographic frameworks such as Elliptic Curve Cryptography (ECC) and Post-Quantum Cryptography (PQC) offer robust security measures to protect AI-driven energy systems. ECC provides lightweight, efficient encryption ideal for IoT-enabled grids, while PQC frameworks, like the SIKE algorithm, ensure long-term resilience against quantum computing threats, safeguarding critical infrastructure. Nevertheless, obstacles like limited data access, cybersecurity weaknesses, and financial limitations continue to hinder widespread AI implementation, especially in less developed areas. This review emphasizes the significance of adopting essential strategies such as smart grid development, public-private collaborations, strong regulatory frameworks, and standardized data-sharing protocols. It is essential to have strong implementation and monitoring systems, improved cybersecurity measures, and ongoing investment in AI research in order to fully harness AI’s ability to revolutionize energy systems. By tackling these obstacles, AI has the potential to significantly impact the development of a more enduring, productive, and flexible worldwide energy system, hastening the shift towards a renewable-focused energy landscape.