Aug 2026· International Conference on Circuit, Power and Computing Technologies· pp. 1485-1490· 0 citations· 22 references
Abstract
Smart grids are becoming more complicated with distributed energy resources, dynamic load changes, real-time operating requirements. The conventional black-box artificial intelligence (AI) models lack in trust, transparency and compliance with regulations, thereby limiting their use in practical deployment. To resolve these issues, this paper suggests a conceptual unified GenAI-XAI framework for smart grids. Explainable Artificial Intelligence (XAI) offers an interpretable and reliable decision-making ability for existing scalability and real-time integration related issues. Recent advances in Generative AI (GenAI) complement XAI by supporting in scenario modeling, intelligent decision assistance, and synthetic data generation. The study presents a comparative analysis of XAI methods and a systematic taxonomy of GenAi applications in anomaly detection, load forecasting, energy optimization, and predictive maintenance The proposed conceptual framework help to improves adaptability, transparency,, and operational efficiency, thereby facilitating the development of a sustainable, resilient, and smart grid systems.
Smart grid infrastructures have become increasingly complex due to the proliferation of renewable energy sources, Internet of Things (IoT) devices and distributed energy systems. The availability of reliable fault prediction, fault recovery and continuous power supply is great challenge in a dynamic smart grid environm...
C. V. Jayaram, Kirti Sharma, P. Supriya et al.· International Conference on...· 0 citations
This study proposes an integrated IoT-edge-cloud framework to improve fraud detection, analyze electricity usage patterns, and enhance data reliability in distributed smart grids, using a hybrid machine learning method that combines classification and clustering.
F. Otosi, Celestine A. Udie, F. Faithpraise· E3S Web of Conferences· 0 citations
The rapid growth of large-scale interconnected systems, such as smart cities, industrial automation, and environmental monitoring, demands intelligent decision-making frameworks that are resilient, scalable, and resource-efficient. Traditional centralized intelligence approaches suffer from communication bottlenecks, h...
M. Kishore, N. Velmurugan· 2026 7th International Confe...· 0 citations
This work proposes an Energy-Efficient Graph Intelligence Framework (EEGF) to accurately predict smart grid stability in highly dynamic operating conditions, while maintaining interpretability and resilience. The growing share of renewable energy, distributed energy resources, and cyber-physical interactions have mad...
T. Susan, M. Marshell, B. Chandra et al.· International Journal of Com...· 0 citations
Photovoltaic (PV) power forecasting is critical for grid stability and renewable energy integration. However, existing approaches prioritize predictive accuracy while neglecting operational requirements: physical validity under noisy data, transparent decision-making, and scalable multi-site deployment. This paper prop...
Leïla Sakli, S. Ben Elghali, Yacine Merrad· IEEE Access· 0 citations
Artificial intelligence (AI) has shown significant promise in improving power grid sustainability; however, a co-evolutionary framework is needed for sustainable AI and sustainable grid operation. There is a need for “Grid friendly AI', that guarantees a sustainable power grid. AI is expected to increasingly rely on re...
C. I. Nwakanma, A. Srivastava· IEEE Energy Sustainability M...· 0 citations
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