Weather-Driven Curtailment State Dataset for Offshore Wind Turbines

#Operating state prediction #Event cause classification #Equipment anomaly detection #Time-series analysis #Offshore wind operations monitoring #Weather risk assessment #Curtailment event analysis #Turbine state management

8100
0.3GB
JSON
CATL
MOBIUSI INC

Updated:2026-11-04

AI Analysis & Value Prop

This JSON time-series dataset records offshore wind conditions such as wind speed, wind direction, wave height, temperature, lightning, and icing, alongside turbine control commands, power information, and curtailment events. It focuses on the relationship between changing weather and turbine operating-mode transitions, supporting the distinction between weather-driven curtailment and abnormal equipment responses. Typical uses include offshore wind state prediction, environmental risk assessment, curtailment analysis, and domain model fine-tuning.

Dataset Insights

Sample Examples

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Technical Specifications

Field	Type	Description
file_name	string	File name
anomaly_score	number	A score from 0 to 1 estimating the likelihood of an equipment anomaly based on consistency between environmental conditions and turbine power response.
curtailment_event	object	Indicates whether curtailment or another operating-mode event occurred during the current time window.
turbine_operation	object	Records turbine control commands, power limits, and the current operating state.
next_state_prediction	object	Predicts the turbine's operating state in the next phase and its prediction confidence using the current time-series context.
power_reduction_ratio	number	Power reduction ratio calculated from rated and actual power, typically as one minus actual power divided by rated power.
current_operating_mode	string	Current operating mode inferred from control commands, actual power, and event status.
event_cause_assessment	string	Classifies the cause of a curtailment or shutdown event using environmental observations, control commands, actual power, and turbine status.
environmental_risk_level	string	Weather-related operating risk level assessed from wind speed, wave height, temperature, lightning, and icing conditions.
environmental_observations	object	Records wind, sea, temperature, lightning, and icing conditions that affect turbine operating modes.

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 information does the dataset contain?: The JSON time series covers wind and sea conditions, temperature, lightning, and icing, along with turbine control commands, power information, curtailment events, operating modes, environmental risk, and event cause assessments.

Can it help distinguish weather-driven curtailment from equipment anomalies?: Yes. The data links environmental observations and control commands to turbine power responses. Fields for event cause assessment and equipment anomaly scoring can support training or evaluation for distinguishing normal weather-driven curtailment from abnormal changes.

How can I obtain the dataset?: Check the product listing or the provider's data service for available access methods, delivery formats, and dataset versions. No specific download link or delivery arrangement is provided in this description.

What are the licensing terms?: Refer to the product listing or the provider's license terms for permitted uses and restrictions. Verify the applicable terms before commercial deployment, redistribution, or external model release.

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Cite this Work

@dataset{Mobiusi2026,
  title={Weather-Driven Curtailment State Dataset for Offshore Wind Turbines},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/9847377dd8bf9d6ac987d94452d90971},
  urldate={2026-10-28},
  keywords={offshore wind dataset,wind power curtailment,weather-driven curtailment,turbine state prediction,offshore wind time series,wind turbine anomaly detection,wind weather risk,JSON wind data},
  version={1.0}
}

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