International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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The Role of Nanotechnology and Artificial Intelligence in Next-Generation Smart and Flexible Energy Networks

Author(s) Dr. Srinivasa Rao Kadari, Dr. N. Kiranmai, Dr. N. Aruna Kumari, C. Swathi
Country India
Abstract The convergence of nanotechnology and artificial intelligence (AI) is revolutionizing the architecture of next-generation smart and flexible energy networks. Nanotechnology plays a pivotal role in designing advanced materials with superior electrical conductivity, thermal stability, and mechanical strength, enabling improvements in energy generation, conversion, storage, and transmission. Materials such as graphene, carbon nanotubes (CNTs), metal oxides, perovskites, and quantum dots have proven highly efficient in enhancing photovoltaic performance, catalyzing hydrogen production, and developing high-capacity batteries and supercapacitors. Nanostructured electrodes have improved ionic transport and increased electrode surface area, significantly accelerating charging rates and enhancing cycle life. Nanosensors embedded within energy systems enable real-time monitoring of temperature, pressure, and chemical parameters, thereby supporting predictive maintenance and minimizing power losses. Nano-coatings further protect infrastructure from corrosion and environmental degradation, extending system durability and performance. Simultaneously, AI provides the cognitive foundation for the operation of decentralized energy environments, including microgrids and distributed energy resources (DERs). Machine learning and deep learning algorithms facilitate accurate forecasting of renewable energy generation, consumption patterns, grid loads, and pricing trends. AI-based optimization frameworks enable dynamic control of energy flow, enhancing grid stability despite fluctuations from solar and wind sources. Furthermore, AI enhances predictive maintenance by identifying faults before failure and strengthens cybersecurity through anomaly detection and intelligent threat mitigation. Reinforcement learning techniques are increasingly being applied to allow autonomous energy networks to self-optimize under changing environmental and demand conditions. The synergy of nanotechnology and AI offers unprecedented benefits. AI accelerates nano material discovery and enhances performance prediction, reducing the time and cost associated with experimental development. In return, nanotechnology provides ultra-sensitive nanosensors, high-efficiency batteries, and advanced nanomaterial-based energy harvesters that capture real-time data essential for AI-assisted decision-making. This bidirectional relationship supports the evolution of self-healing, adaptive, and highly efficient energy networks.
Keywords Nanotechnology, AI Smart Grids, Flexible Energy Networks, Nanomaterials, Quantum Dots, Graphene, Perovskites, Distributed Energy Resources (DERs), Energy Storage
Published In Conference / Special Issue (Volume 8 | Issue 3) - National Seminar on Sustainability in Focus: Innovations for Greener Tomorrow: A Multidisciplinary Approach (NSSFIGTMA-2025) (June 2026)
Published On 2026-06-24
DOI https://doi.org/10.36948/ijfmr.2026.NSSFIGTMA-2025.2016
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CrossRef DOI prefix of IJFMR is 10.36948/ijfmr

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