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#smart-grids News & Analysis

7 articles tagged with #smart-grids. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

7 articles
AI × CryptoNeutralarXiv – CS AI · Apr 107/10
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Blockchain and AI: Securing Intelligent Networks for the Future

A comprehensive academic synthesis examines how blockchain and AI technologies can be integrated to secure intelligent networks across IoT, critical infrastructure, and healthcare. The paper introduces a taxonomy, integration patterns, and the BASE evaluation blueprint to standardize security assessments, revealing that while the conceptual alignment is strong, real-world implementations remain largely prototype-stage.

AINeutralarXiv – CS AI · Jun 256/10
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Securing Time Integrity in Energy IoT Against Clock Drift and Y2K38 Failures

Researchers introduce STGAT, a spatio-temporal graph attention network designed to detect timing anomalies in energy IoT systems caused by clock drift, synchronization failures, and Y2K38 Unix overflow events. The framework achieves 95.7% accuracy in identifying temporal inconsistencies that traditional anomaly detection systems miss, with 26% faster detection speeds.

AINeutralarXiv – CS AI · Jun 96/10
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Outage Detection in Self-Healing Smart Grids Using Reinforcement Learning with Spectral Graph Neural Networks

Researchers propose a spectral graph neural network combined with reinforcement learning to optimize power grid recovery during outages, enabling real-time decision-making for network reconfiguration. The approach demonstrates near-optimal performance across IEEE test systems while generalizing effectively to diverse outage scenarios, addressing computational inefficiencies in traditional machine learning methods for smart grid management.

AIBullisharXiv – CS AI · Jun 16/10
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Scalable Constrained Multi-Agent Reinforcement Learning via State Augmentation and Consensus for Separable Dynamics

Researchers present a distributed multi-agent reinforcement learning method that uses state augmentation and consensus algorithms to enforce global constraints while maintaining linear scalability. The approach enables thousands of agents to coordinate through local communication alone, outperforming centralized training methods that scale quadratically and fail on real-world constraint satisfaction problems like smart grid management.

AIBullisharXiv – CS AI · May 296/10
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Uncertainty-Aware Transfer Learning for Cross-Building Energy Forecasting: Toward Robust and Scalable District-Level Energy Management

Researchers developed an uncertainty-aware transfer learning framework using Temporal Fusion Transformers to enable energy forecasting models trained on one building to work effectively on different buildings with minimal retraining. The approach achieved 93.2% prediction interval coverage and demonstrated that freezing most model parameters while fine-tuning only output layers produces superior cross-building generalization compared to full model retraining.

AIBullisharXiv – CS AI · Apr 76/10
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Towards Intelligent Energy Security: A Unified Spatio-Temporal and Graph Learning Framework for Scalable Electricity Theft Detection in Smart Grids

Researchers have developed SmartGuard Energy Intelligence System (SGEIS), an AI framework that combines machine learning, deep learning, and graph neural networks to detect electricity theft in smart grids. The system achieved 96% accuracy in identifying high-risk nodes and demonstrates strong performance with practical applications for energy security.