AI-DRIVEN ADAPTIVE LOAD BALANCING AND FAULT PREDICTION FRAMEWORK FOR SMART RENEWABLE POWER DISTRIBUTION SYSTEMS
スマート再生可能エネルギー配電システムのためのAI駆動適応負荷分散および故障予測フレームワーク (AI 翻訳)
Mrs. Chitra Vinodhkumar
🤖 gxceed AI 要約
日本語
本論文は、スマートグリッドにおける再生可能エネルギーの統合課題に対応するため、IoTセンサーと機械学習・深層学習を組み合わせた適応負荷分散と故障予測のフレームワークを提案する。実データを用いた評価により、負荷分散効率の向上、電力損失の低減、故障検出精度の改善を実証し、再生可能エネルギーの利用最大化と系統信頼性向上を達成した。
English
This paper proposes an AI framework integrating IoT sensors, ML, and deep learning for adaptive load balancing and fault prediction in smart grids with high renewable penetration. Experimental results show improved load distribution efficiency, reduced power losses, and enhanced fault detection, leading to better renewable energy utilization and grid reliability.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では再生可能エネルギーの大量導入に伴い、系統安定化が急務となっている。本フレームワークは、配電レベルでのリアルタイム負荷制御と故障予測を可能にし、日本のスマートグリッド政策や地域エネルギー管理に応用が期待される。SSBJや有報での直接的な開示要件ではないが、エネルギー効率改善とレジリエンス強化を通じてGX実践に貢献する。
In the global GX context
As grids worldwide integrate more renewables, stability challenges grow. This framework offers distribution system operators a scalable AI solution for real-time load balancing and predictive maintenance, directly supporting the energy transition. While not disclosure-focused, its operational improvements align with TCFD/ISSB resilience goals and reduce climate-related physical risks.
👥 読者別の含意
🔬研究者:Demonstrates a practical AI approach for real-time load balancing and fault prediction in renewable-rich grids, with experimental validation on operational data.
🏢実務担当者:Provides a framework that grid operators can adopt to improve renewable utilization, reduce outages, and lower operational costs.
🏛政策担当者:Highlights the importance of AI-enabled grid management for achieving renewable energy targets and enhancing grid resilience.
📄 Abstract（原文）
ABSTRACT The rapid integration of renewable energy sources into modern power distribution networks introduces significant challenges related to load variability, system reliability, and fault management. This paper presents an AI-Driven Adaptive Load Balancing and Fault Prediction Framework for Smart Renewable Power Distribution Systems designed to enhance operational efficiency and grid stability. The proposed methodology combines Internet of Things (IoT)-enabled sensors, real-time data acquisition, machine learning algorithms, and deep learning-based predictive analytics to continuously monitor power generation, consumption patterns, voltage fluctuations, and equipment health. An adaptive load balancing module dynamically redistributes electrical loads based on demand forecasts and renewable energy availability, while the fault prediction engine identifies potential failures before their occurrence. Historical operational data and real-time measurements are processed through intelligent data preprocessing, feature extraction, and predictive modeling stages to support autonomous decision-making. Experimental evaluation demonstrates that the framework significantly improves load distribution efficiency, minimizes power losses, and enhances fault detection accuracy compared with conventional grid management approaches. Results indicate improved renewable energy utilization, reduced outage duration, faster response to grid disturbances, and increased overall system reliability. The integration of artificial intelligence with smart power infrastructure provides a scalable and sustainable solution for next-generation renewable energy distribution networks, supporting resilient, efficient, and intelligent energy management in dynamic operating environments. Keywords: Artificial Intelligence, Smart Grid, Renewable Energy Distribution, Adaptive Load Balancing, Fault Prediction, Predictive Analytics..
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/20685944first seen 2026-06-15 04:13:58 · last seen 2026-06-16 04:15:42
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