IBM Case Studies Turning climate into capital with big data

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	Turning climate into capital with big data IBM

Turning climate into capital with big data

IBM

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
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

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

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.

About 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.

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.

Impact #1	The big data solution has reduced the response time for wind forecasting information by approximately 97 percent, from weeks to hours, helping to cut development time.
Impact #2	The solution has improved the accuracy of turbine placement by enabling the analysis of a greater breadth and depth of data.
Impact #3	The solution has reduced the IT footprint and costs, and decreased energy consumption by 40 percent, all while increasing computational power.

Impact #1

The big data solution has reduced the response time for wind forecasting information by approximately 97 percent, from weeks to hours, helping to cut development time.

Impact #2

The solution has improved the accuracy of turbine placement by enabling the analysis of a greater breadth and depth of data.

Impact #3

The solution has reduced the IT footprint and costs, and decreased energy consumption by 40 percent, all while increasing computational power.

Benefit #1	Reduced response time for wind forecasting information by approximately 97 percent.
Benefit #2	Decreased energy consumption by 40 percent.
Benefit #3	Increased computational power, allowing twice the number of servers to be run in each of the system's 12 racks.

Benefit #1

Reduced response time for wind forecasting information by approximately 97 percent.

Benefit #2

Decreased energy consumption by 40 percent.

Benefit #3

Increased computational power, allowing twice the number of servers to be run in each of the system's 12 racks.

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Overview

Turning climate into capital with big data

Operational Impact

Quantitative Benefit