Turning climate into capital with big data
Company Size
1,000+
Region
- Europe
Country
- Denmark
Product
- IBM InfoSphere BigInsights Enterprise Edition
- IBM System x iDataPlex dx360 M3
- IBM System Storage DS5300
Tech Stack
- Big Data
- Data Modeling
- Supercomputing
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Customer Satisfaction
- Productivity Improvements
Technology Category
- Analytics & Modeling - Big Data Analytics
- Infrastructure as a Service (IaaS) - Cloud Computing
Applicable Industries
- Renewable Energy
Applicable Functions
- Discrete Manufacturing
- Product Research & Development
Use Cases
- Energy Management System
- Predictive Maintenance
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
About The Customer
Vestas is a Danish company that has been engaged in the development, manufacture, sale, and maintenance of wind power systems to generate electricity since 1979. The company has installed more than 43,000 land-based and offshore wind turbines in 66 countries on six continents. Today, Vestas installs an average of one wind turbine every three hours, 24 hours a day, and its turbines generate more than 90 million megawatt-hours of energy per year—enough electricity to supply millions of households. The company's success depends on its ability to respond quickly and deliver business value, which includes determining the optimal location for wind turbines to maximize power generation and reduce energy costs.
The Challenge
Vestas, the world's largest wind energy company, relies on the precise placement of wind turbines to maximize power generation and reduce energy costs. The optimal location for a wind turbine can significantly affect its performance and lifespan. However, the process of determining the best location for a turbine is complex and time-consuming, often taking up to three weeks. This delay is unacceptable in a competitive industry where quick responses and delivering business value are crucial. Furthermore, the cost of placing a turbine in a sub-optimal location can be significant, potentially leading to underperformance, increased warranty costs, and loss of customers.
The Solution
Vestas partnered with IBM to implement a big data solution that significantly reduces data processing times and improves the accuracy of turbine placement decisions. The solution combines open source Apache Hadoop software with unique technologies and capabilities from IBM to enable Vestas to process very large data sets. This allows Vestas to reduce the base resolution of its wind data grids from a 27x27 kilometer area down to a 3x3 kilometer area, providing more immediate insight into potential locations. The solution also includes IBM InfoSphere BigInsights software running on an IBM System x iDataPlex system, which enables Vestas to use 40 percent less energy while increasing computational power. This supercomputing solution is one of the world's largest to date and allows Vestas to run twice the number of servers in each of the system's 12 racks, reducing the amount of floor space required in its data center.
Operational Impact
Quantitative Benefit
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