Ørsted Optimizes Offshore Wind Farms with Predictive Analytics
Ørsted, a Danish renewable energy leader, relies on Microsoft Azure and advanced analytics to transform massive data from 1,300 offshore wind turbines into actionable insights. The company’s digital strategy embraces artificial intelligence to optimize turbine maintenance, improving resource allocation and reducing downtime. AI-driven analysis of thousands of real-time sensor data points per turbine enables predictive maintenance scheduling and operational efficiency. Ørsted’s transition from fossil fuels, through divestment and coal reduction, aligns with a vision for global green energy, supported by scalable, cloud-based Microsoft tools. Cloud-enabled engineering collaboration has cut the computation for wind farm foundation designs from weeks to hours. Microsoft’s solutions support Ørsted’s 5,900 staff, making offshore energy production more sustainable, lower cost, and more reliable, helping power over 11 million people with clean energy.
- Organization
- Ørsted
- Industry
- Energy & Utilities
- Location
- Denmark
- Published
- January 2019
Reported outcomes
Strategic outcomes
Primary read
Use case focus
Showing 3 of 3
- 1Predictive Maintenance of Wind Turbines
- 2Real-Time Sensor Data Analytics for Renewable Energy
- 3Cloud-Based Engineering Workflow Optimization
- Need to reduce reliance on fossil fuels and accelerate transition to green energy.
- Maintenance of offshore wind turbines is resource-intensive, with costly downtime.
- Vast amounts of operational data require advanced analytics for effective use.
- Complex engineering tasks historically required long computation times.
- Efficiency demanded for managing global renewable infrastructure.
- Adoption of Microsoft Azure and Azure AI for analytics and predictive maintenance.
- Real-time sensor data analysis from over 1,300 wind turbines using AI models.
- Cloud-enabled collaboration tools to engineer and operate wind farms efficiently.
- Advanced analytics streamlining performance across wind assets and engineering workflows.
- Reduced wind turbine maintenance time and resources.
- Improved operational efficiency and lower overall energy production costs.
- Accelerated engineering computations (from weeks to hours).
- Sustained clean energy supply for millions (over 11 million people served).
- Significantly advanced renewable energy transition objectives.
Architecture
Sensor data from each turbine is transmitted to the Azure cloud, where AI and analytics models process the data in real time for predictive maintenance and operational optimization. Cloud-based tools support remote engineering collaboration for wind farm rollout and maintenance planning.
Sources & evidence1
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