Sodium-Ion Energy Storage System Operational State Transition Prediction Dataset
#Change point detection #State transition prediction #Time-series evaluation #Sodium-ion energy storage #Energy storage monitoring #Operational state analysis #State transition prediction
- 4200
- 0.3GB
- JSON
- CATL
MOBIUSI INC
Updated:2026-11-02
AI Analysis & Value Prop
This dataset provides sequential JSON telemetry from sodium-ion battery energy storage systems, including timestamps, operating modes, charge and discharge power, and key operating parameters. It annotates key state-change points, change types, and the resulting system states, enabling evaluation of models that detect operational changes and predict the next-stage state. It is suitable for energy storage state monitoring, change analysis, and prediction research.
Dataset Insights
Sample Examples
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Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| next_stage_state | object | The target operating state for the next stage, specified based on the annotated change points and used to evaluate state transition prediction. |
| change_annotations | array | Key operational state-change points identified in the telemetry sequence, with the change type and the system state after each change. |
| telemetry_sequence | array | Time-ordered operational telemetry records from a sodium-ion energy storage system, including timestamps, operating modes, charge and discharge power, and key operating parameters. |
Compliance Statement
| Authorization Type | Proprietary - Commercial AI Training License (No Redistribution) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Frequently Asked Questions
- What does the dataset contain?
- The dataset is organized as JSON sequences containing time-ordered system telemetry, annotated key change points and change types, resulting system states, and target states for the next stage. Telemetry fields include timestamps, operating modes, charge and discharge power, and key operating parameters.
- Which tasks can this dataset support?
- It can evaluate whether models can detect operational state changes in continuous telemetry, identify change types, and predict the system state in the next stage. It can also support time-series analysis and state transition research for energy storage systems.
- What are the dataset size and quality specifications?
- The available information describes the sequence structure and annotations but does not specify the record count, time span, or sampling frequency. Review the product delivery specifications and sample data before purchase to confirm that the coverage and field formats meet your project requirements.
- How can I obtain the data, and what are the licensing terms?
- Availability, delivery format, and permitted uses are defined by the product page and applicable license terms. Before using the data for commercial projects, model training, or redistribution, verify that the license covers your intended use.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Sodium-Ion Energy Storage System Operational State Transition Prediction Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/b32ec9de30a983fefb46c375326b7751?dataset_task_cate_id=11},
urldate={2026-10-28},
keywords={sodium-ion energy storage dataset,energy storage state prediction,battery telemetry data,operational state transition,change point detection,charge and discharge power analysis,storage state evaluation,time-series prediction},
version={1.0}
}Using this in research? Please cite us.
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