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Review

A Review of Artificial Intelligence Techniques for Low-Carbon Energy Integration and Optimization in Smart Grids and Smart Homes

by
Omosalewa O. Olagundoye
1,2,
Olusola Bamisile
1,2,3,*,
Chukwuebuka Joseph Ejiyi
1,2,
Oluwatoyosi Bamisile
1,2,
Ting Ni
4 and
Vincent Onyango
5
1
College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chenghua District, Chengdu 610059, China
2
Sichuan Engineering Technology Research Center for Industrial Internet Intelligent Monitoring and Application, Chengdu University of Technology, Chenghua District, Chengdu 610059, China
3
Energy and Environment Science Division, CEPMLP, University of Dundee, Dundee DD1 4HN, UK
4
College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610059, China
5
Architecture and Urban Planning, Duncan of Jordanstone College of Art and Design, University of Dundee, Dundee DD1 4HN, UK
*
Author to whom correspondence should be addressed.
Processes 2026, 14(3), 464; https://doi.org/10.3390/pr14030464
Submission received: 18 November 2025 / Revised: 15 January 2026 / Accepted: 17 January 2026 / Published: 28 January 2026

Abstract

The growing demand for electricity in residential sectors and the global need to decarbonize power systems are accelerating the transformation toward smart and sustainable energy networks. Smart homes and smart grids, integrating renewable generation, energy storage, and intelligent control systems, represent a crucial step toward achieving energy efficiency and carbon neutrality. However, ensuring real-time optimization, interoperability, and sustainability across these distributed energy resources (DERs) remains a key challenge. This paper presents a comprehensive review of artificial intelligence (AI) applications for sustainable energy management and low-carbon technology integration in smart grids and smart homes. The review explores how AI-driven techniques include machine learning, deep learning, and bio-inspired optimization algorithms such as particle swarm optimization (PSO), whale optimization algorithm (WOA), and cuckoo optimization algorithm (COA) enhance forecasting, adaptive scheduling, and real-time energy optimization. These techniques have shown significant potential in improving demand-side management, dynamic load balancing, and renewable energy utilization efficiency. Moreover, AI-based home energy management systems (HEMSs) enable predictive control and seamless coordination between grid operations and distributed generation. This review also discusses current barriers, including data heterogeneity, computational overhead, and the lack of standardized integration frameworks. Future directions highlight the need for lightweight, scalable, and explainable AI models that support decentralized decision-making in cyber-physical energy systems. Overall, this paper emphasizes the transformative role of AI in enabling sustainable, flexible, and intelligent power management across smart residential and grid-level systems, supporting global energy transition goals and contributing to the realization of carbon-neutral communities.
Keywords: artificial intelligence; smart grids; home energy management systems (HEMS); smart homes; low-carbon energy integration; sustainable energy systems; machine learning and optimization artificial intelligence; smart grids; home energy management systems (HEMS); smart homes; low-carbon energy integration; sustainable energy systems; machine learning and optimization

Share and Cite

MDPI and ACS Style

O. Olagundoye, O.; Bamisile, O.; Joseph Ejiyi, C.; Bamisile, O.; Ni, T.; Onyango, V. A Review of Artificial Intelligence Techniques for Low-Carbon Energy Integration and Optimization in Smart Grids and Smart Homes. Processes 2026, 14, 464. https://doi.org/10.3390/pr14030464

AMA Style

O. Olagundoye O, Bamisile O, Joseph Ejiyi C, Bamisile O, Ni T, Onyango V. A Review of Artificial Intelligence Techniques for Low-Carbon Energy Integration and Optimization in Smart Grids and Smart Homes. Processes. 2026; 14(3):464. https://doi.org/10.3390/pr14030464

Chicago/Turabian Style

O. Olagundoye, Omosalewa, Olusola Bamisile, Chukwuebuka Joseph Ejiyi, Oluwatoyosi Bamisile, Ting Ni, and Vincent Onyango. 2026. "A Review of Artificial Intelligence Techniques for Low-Carbon Energy Integration and Optimization in Smart Grids and Smart Homes" Processes 14, no. 3: 464. https://doi.org/10.3390/pr14030464

APA Style

O. Olagundoye, O., Bamisile, O., Joseph Ejiyi, C., Bamisile, O., Ni, T., & Onyango, V. (2026). A Review of Artificial Intelligence Techniques for Low-Carbon Energy Integration and Optimization in Smart Grids and Smart Homes. Processes, 14(3), 464. https://doi.org/10.3390/pr14030464

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