Supplement S1: Energy Storage Capability Classification with Response and Energy Memory

Supplement S1 to the conference paper "Energy Storage Capability Classification with Response and Energy Memory" (PE0053). Contents: (S1.1) scope of the supplement; (S1.2) data generator and class-dependent parameter ranges; (S1.3) resource split of 32 training, 8 validation and 10 test resources; (S1.4) the thirteen candidate configurations and their cumulative simplification scheme; (S1.5) acceptance criteria relative to the five-seed O64 validation mean; (S1.6) complete validation results for all candidates; (S1.7) parameter-counting definitions (stored, active, dormant, effective); (S1.8) training recipe including optimizer, schedule, batch size, clipping and seeds 11/22/33/44/55; (S1.9) timing procedure with the measurement environment and warm-up protocol; (S1.10) complete test results; (S1.11) parameter and latency savings. The supplement supports the reproducibility of the selection procedure reported in Tables II-V and Fig. 2 of the paper.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-08
DOI
https://doi.org/10.5281/zenodo.24047110
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
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Supplement S1: Energy Storage Capability Classification with Response and Energy Memory

Jianbo Nie, Wenjun Wu, Chao Zhu, Xiaoze Lin et al.
Zenodo (CERN European Organization for Nuclear Research)
Advanced Memory and Neural Computing
article

Supplement S1: Energy Storage Capability Classification with Response and Energy Memory

Jianbo Nie, Wenjun Wu, Chao Zhu, Xiaoze Lin, Youbing Zhang
article en

Abstract

Supplement S1 to the conference paper "Energy Storage Capability Classification with Response and Energy Memory" (PE0053). Contents: (S1.1) scope of the supplement; (S1.2) data generator and class-dependent parameter ranges; (S1.3) resource split of 32 training, 8 validation and 10 test resources; (S1.4) the thirteen candidate configurations and their cumulative simplification scheme; (S1.5) acceptance criteria relative to the five-seed O64 validation mean; (S1.6) complete validation results for all candidates; (S1.7) parameter-counting definitions (stored, active, dormant, effective); (S1.8) training recipe including optimizer, schedule, batch size, clipping and seeds 11/22/33/44/55; (S1.9) timing procedure with the measurement environment and warm-up protocol; (S1.10) complete test results; (S1.11) parameter and latency savings. The supplement supports the reproducibility of the selection procedure reported in Tables II-V and Fig. 2 of the paper.

Zenodo (CERN European Organization for Nuclear Research)
State Grid Zhejiang Electric Power Company (China) (CN), Zhejiang University of Technology (CN)
Affordable and clean energy
Advanced Memory and Neural Computing
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