Presented By: U-M Industrial & Operations Engineering
Second NSF Big Data Project Annual Workshop
Abstract:
The critical barrier to cost-effective wind power is partly rooted in wind stochasticity, severely complicating wind power production optimization and cost reduction. Therefore, the long-term viability of wind energy hinges upon a good understanding of its production reliability, which is affected in turn by the predictability of wind and power productivity of wind turbines.
This Big Data workshop presents recent research updates to addresses the big data challenges, including how to best use spatio-temporal data for wind forecast and how to use data of different nature and data of different sources for power production assessment in a computationally efficient manner.
The critical barrier to cost-effective wind power is partly rooted in wind stochasticity, severely complicating wind power production optimization and cost reduction. Therefore, the long-term viability of wind energy hinges upon a good understanding of its production reliability, which is affected in turn by the predictability of wind and power productivity of wind turbines.
This Big Data workshop presents recent research updates to addresses the big data challenges, including how to best use spatio-temporal data for wind forecast and how to use data of different nature and data of different sources for power production assessment in a computationally efficient manner.
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