Counterfactual QA Dataset for Grid-Scale Energy Storage Capacity and Duration Planning

#counterfactual reasoning #constraint-based reasoning #feasibility assessment #grid-scale energy storage planning #energy storage capacity optimization #power grid feasibility assessment
  • 5000
  • 0.3GB
  • JSON
  • CATL
  • MOBIUSI INCMOBIUSI INC
Updated:2026-10-19

AI Analysis & Value Prop

Designed for grid-scale energy storage planning and reasoning training, this dataset covers storage power, energy capacity, and discharge-duration configurations alongside corresponding grid requirements. Samples compare a baseline configuration with a counterfactual alternative to analyze how power and energy constraints affect feasibility and suitable use cases. The data supports storage option comparison, planning question answering, and training models in counterfactual reasoning.

Dataset Insights

Sample Examples

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Technical Specifications

FieldTypeDescription
file_namestringFile name
final_answerstringAnswers the original question by clearly comparing the baseline and counterfactual configurations and distinguishing power constraints from energy constraints.
scenario_impactstringExplains how the configuration change affects the grid requirements the storage can meet and the use cases it can serve.
source_questionstringContains the planning scenario and the conditions to compare or the question to answer.
grid_requirementobjectSpecifies the grid-side power, energy, and duration requirements used to assess whether a storage configuration is feasible.
baseline_feasibilitystringAssesses whether the baseline configuration meets the grid requirements under power and energy constraints.
baseline_configurationobjectSpecifies the storage power, energy capacity, and duration design conditions before the counterfactual change.
power_constraint_analysisstringCompares the power capability of both configurations with the grid requirement and explains whether power is a limiting factor.
counterfactual_feasibilitystringAssesses whether the changed configuration meets the grid requirements under power and energy constraints.
counterfactual_configurationobjectSpecifies the storage power, energy capacity, and duration after the design conditions change.
energy_duration_constraint_analysisstringCompares the energy capacity and duration of both configurations with the grid requirement and explains whether energy or duration is limiting.

Compliance Statement

Authorization TypeProprietary - Commercial AI Training License (No Redistribution)
Commercial UseRequires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and AnonymizationNo PII, no real company names, simulated scenarios follow industry standards
Compliance SystemCompliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Frequently Asked Questions

What storage planning topics does the dataset cover?
Samples compare baseline and counterfactual grid-scale storage configurations against grid requirements. They cover power, energy capacity, and discharge duration, with feasibility judgments, constraint analysis, use-case impacts, and final answers.
How are power constraints distinguished from energy and duration constraints?
Power constraints determine whether storage can reach the instantaneous output or absorption power required by the grid. Energy and duration constraints determine whether it can deliver the required energy over the specified period. Both should be checked separately; rated power alone is not enough to establish overall feasibility.
Which training and application scenarios are suitable for this data?
The data is suitable for reasoning supervised fine-tuning, counterfactual comparison, and storage planning question-answering. It can also help evaluate whether a model can explain how configuration changes affect feasibility and applicable use cases.
How can the data be obtained, and where can licensing terms be checked?
Check the dataset listing or the provider's designated channel for available delivery methods, file formats, and access procedures. Licensing scope, commercial-use conditions, and redistribution rules should be confirmed in the provider's terms; no specific license or delivery format is stated here.

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Cite this Work

@dataset{Mobiusi2026,
  title={Counterfactual QA Dataset for Grid-Scale Energy Storage Capacity and Duration Planning},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/4bdb3b65f8ede2d6600e06c56ed3de23?dataset_scene_id=12},
  urldate={2026-10-12},
  keywords={grid-scale energy storage, storage capacity planning, storage duration planning, storage power constraints, storage energy constraints, counterfactual QA, energy storage dataset, reasoning training data},
  version={1.0}
}

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