Hyper-Specific Sub-Field: Predictive Anomaly Detection in Smart Meter Data for Grid-Scale Energy Storage Optimization

**Research Paper: Dynamic Bayesian Network Fusion for Real-Time Predictive Anomaly Detection and Energy Storage Optimization in Smart Meter Grid Systems**

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-08-13
DOI
https://doi.org/10.5281/zenodo.16825028
Primary Topic
Smart Grid Security and Resilience
Type
article
Field-Weighted Citation Impact
0.00
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article

Hyper-Specific Sub-Field: Predictive Anomaly Detection in Smart Meter Data for Grid-Scale Energy Storage Optimization

Zenodo (CERN European Organization for Nuclear Research)
Smart Grid Security and Resilience
article

Hyper-Specific Sub-Field: Predictive Anomaly Detection in Smart Meter Data for Grid-Scale Energy Storage Optimization

article en

Abstract

**Research Paper: Dynamic Bayesian Network Fusion for Real-Time Predictive Anomaly Detection and Energy Storage Optimization in Smart Meter Grid Systems**

Zenodo (CERN European Organization for Nuclear Research)
Freed–Hardeman University (US)
Affordable and clean energy
Openalex Percentile: Top 46%
Smart Grid Security and Resilience
DOIOpen Access PDFOpenAlex
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Hyper-Specific Sub-Field: Predictive Anomaly Detection in Smart Meter Data for Grid-Scale Energy Storage Optimization · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS