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Vestas: Turning Climate into Capital with Big Data

Making wind a reliable source of energy depends greatly on the placement of the wind turbines used to produce electricity. Turbulence is a significant factor as it strains turbine components, making them more likely to fail. Vestas wanted to pinpoint the optimal location for wind turbines to maximize power generation and reduce energy costs.

  • IBM
    IBM is an American multinational technology and consulting corporation that manufactures and markets computer hardware, middleware and software, and offers infrastructure, hosting and consulting services in areas ranging from mainframe computers to nanotechnology. IBM is intent on leading the development of a global data field. Year founded: 1911 Revenue: $92.7 billion (2014) NYSE: IBM
  • Analytics & Modeling - Big Data Analytics
  • Renewable Energy
  • Process Manufacturing
  • Outdoor Environmental Monitoring
  • Vestas Wind Systems is the largest manufacturer, seller, installer, and servicer of wind turbines in the world, with more than 17,000 employees globally.
  • Vestas Wind Systems
  • IBM InfoSphere BigInsights software running on an IBM System x iDataPlex system serves as the core infrastructure to help Vestas manage and analyze weather and location data in ways that were not previously possible. IBM InfoSphere BigInsights helps Vestas gain access to knowledge in an efficient and fast way and enables Vestas to use this knowledge to turn climate into capital. Software Components - IBM InfoSphere BigInsights software - IBM System x iDataPlex system - Apache Hadoop software

  • Asset Location, Energy Cost Per Unit, Energy Production, Power Output, Wind Speed
  • Impact #1
    [Data Management - Data Processing]
    Processing huge volumes of climate data and the ability to gain insight from that data enables Vestas to forecast optimal turbine placement in 15 minutes instead of three weeks.
    Impact #2
    [Efficiency Improvement - R&D]
    Ongoing application development and improvements are relatively quick and inexpensive to implement due to the system's flexibility.
    Impact #3
    [Efficiency Improvement - R&D]
    Response time for wind forecasting information was reduced by approximately 97% which helped cut development time.
  • Benefit #1

    Vestas reduces the base resolution of its wind data grids from a 27x27 kilometer area down to a 3x3 kilometer area, a nearly 90% reduction.

    Benefit #2

    The IBM System x iDataPlex supercomputer enables the company to use 40% less energy while increasing computational power.

    Benefit #3

    Implementing a big data solution enables Vestas to create a wind library to hold 18 to 24 petabytes of weather and turbine data and reduce the geographic grid area by 90% to increased accuracy.

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