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Vestas: Turning Climate into Capital with Big Data

Vestas: Turning Climate into Capital with Big Data
Vestas: Turning Climate into Capital with Big Data
Vestas: Turning Climate into Capital with Big Data
Vestas: Turning Climate into Capital with Big Data
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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.
Vestas Wind Systems is the largest manufacturer, seller, installer, and servicer of wind turbines in the world, with more than 17,000 employees globally.
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
Weather data including 178 parameters such as temperature, barometric pressure, humidity, precipitation, wind direction and wind velocity from the ground level up to 300 feet, future prediction data including global deforestation metrics, satellite images, historical metrics, geospatial data and data on phases of the moon and tides
Emerging (technology has been on the market for >2 years)
Data Processing Efficiency - 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.
System Flexibility - Ongoing application development and improvements are relatively quick and inexpensive to implement due to the system's flexibility.
Research Efficiency - Response time for wind forecasting information was reduced by approximately 97% which helped cut development time.
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.
The IBM System x iDataPlex supercomputer enables the company to use 40% less energy while increasing computational power.
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.
The IoT ONE Radar indicates the mix of hardware, software and services used in an IoT solution.
Processors provide the intelligence behind IoT systems and are often integrated into system-on-a-chip designs.
Products used by end users that contain IoT technologies. Examples include enabled equipment, wearables, hand-held scanners, and tracking devices.
Horizontal applications are standardized (e.g., asset tracking). Vertical applications are tailored to specific needs (e.g., delivery fleet management).
Middleware integrates the diverse components of an IoT application by structuring communication, workflows, and business rules.
IoT analytics includes real-time or edge computing and batch analysis. Analytics can be behavioral, descriptive, predictive, or prescriptive.
Data management solutions capture, index and store data in traditional database, cloud platforms, and fog systems for future use.
IoT data management consultancies help to make sense of big data, decide which data to maintain and for how long, and troubleshoot IT issues.
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