Physics-Based Data-Driven rSOC Modeling for Power System Operation: An Interpretability-Enhanced Hybrid Neural Network Agent (Supplementary Information)

This is supplementary information for our manuscript entitled "Physics-Based Data-Driven rSOC Modeling for Power System Operation: An Interpretability-Enhanced Hybrid Neural Network Agent."

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-10-20
DOI
https://doi.org/10.5281/zenodo.17396823
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.00
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article

Physics-Based Data-Driven rSOC Modeling for Power System Operation: An Interpretability-Enhanced Hybrid Neural Network Agent (Supplementary Information)

Zenodo (CERN European Organization for Nuclear Research)
Model Reduction and Neural Networks
article

Physics-Based Data-Driven rSOC Modeling for Power System Operation: An Interpretability-Enhanced Hybrid Neural Network Agent (Supplementary Information)

article en

Abstract

This is supplementary information for our manuscript entitled "Physics-Based Data-Driven rSOC Modeling for Power System Operation: An Interpretability-Enhanced Hybrid Neural Network Agent."

Zenodo (CERN European Organization for Nuclear Research)
Industry, innovation and infrastructure
Openalex Percentile: Top 48%
Model Reduction and Neural Networks
DOIOpen Access PDFOpenAlex
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Physics-Based Data-Driven rSOC Modeling for Power System Operation: An Interpretability-Enhanced Hybrid Neural Network Agent (Supplementary Information) · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS