Energy and Utilities - Switching to Big Data
Energy and utility companies face increasing pressure to accurately predict the supply of energy attributable to renewable resources. By factoring in weather and other key variables, utilities can determine their capital investments and where and when to deploy new generation assets. They also seek to understand and control how distributed generation resources can best be utilized in the network.
The good news is that big data can make a big difference in how we generate, use and control our energy needs.
To improve wind turbine performance, Vestas, a leader in wind energy systems, needed to expand its library of wind data more than 10-fold to include a larger range of weather data over a longer period of time. They also needed a more powerful computing platform to forecast global weather more quickly. This historical data is critical as precise placement of a wind turbine can affect its performance and the length of its useful life.
By implementing the IBM big data platform, Vestas has created a wind library that holds 18 to 24 petabytes of weather and turbine data at various levels of granularity. Their big data library increases accuracy by reducing the geographic grid area used for modeling by 90 percent. With their big data platform, Vestas can now forecast optimal turbine placement in 15 minutes instead of three weeks. The ability to accelerate forecasting has slashed the time required to deploy a wind turbine system by nearly a month.
The IBM big data platform has helped Vestas reduce response time for wind forecasting information by approximately 97 percent—from weeks to hours. The company has also decreased energy consumption by 40 percent. Vestas now enjoys greater business case certainty, quicker results and increased predictability and reliability in wind power generation.
Watch as Lars Christian Christensen, Vice President, Vestas Wind Systems, talks about the benefits the company and its customers experienced with the IBM big data platform.
- Read the Vestas case study
- IBM big data platform
- InfoSphere Streams product information
- IBM Netezza product information