A Review of Artificial Intelligence Techniques for Low-Carbon Energy Integration and Optimization in Smart Grids and Smart Homes
Обзор методов искусственного интеллекта для интеграции и оптимизации низкоуглеродной энергетики в интеллектуальных электросетях и умных домах
2026-01-28
SCID: 54.1/zq937kus
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artificial intelligencedistributed energy resourceslow-carbon energy integrationsmart gridssmart homes
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Abstract (AI)
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.
Key Findings
1
AI improves demand-side management, dynamic load balancing, and renewable-energy utilization efficiency across distributed energy resources.
2
AI techniques—including machine learning, deep learning, and bio-inspired optimization—support forecasting, adaptive scheduling, and real-time energy optimization in smart grids and homes.
3
AI-based home energy management systems enable predictive control and coordination between grid operations and distributed generation.
4
Future systems require lightweight, scalable, and explainable AI models enabling decentralized decision-making in cyber-physical energy systems.
5
Key barriers to deployment include heterogeneous data, computational overhead, and the absence of standardized integration frameworks.
Research Object
AI-enabled smart grids and smart homes integrating renewable generation, energy storage, and distributed energy resources
Research Subject
AI-driven sustainable energy management and low-carbon technology integration, including forecasting, adaptive scheduling, real-time optimization, demand-side management, load balancing, and renewable-energy utilization
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2026-01-28
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