Eunshin Byon receives funding from National Science Foundation
Title: BIGDATA: IA: Collaborative Research: From Bytes to Watts – A Data Science Solution to Improve Wind Energy Reliability and Operation
Funding: National Science Foundation
Project summary: 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. Furthermore, the productivity of a wind turbine comprises two aspects: its ability of converting wind into power during its operation and the availability of wind turbines. To enhance wind energy reliability and productivity, modern wind farms are equipped with a large number and variety of sensors. However, all these data are currently analyzed only in their respective domains. This project will address the big data challenges, including how to best use spatio-temporal data for wind forecast and how to use data of different nature (wind, power, load etc.) and data of different sources (physical data versus computer simulation data) for power production assessment in a computationally efficient manner. The proposed research activities will demonstrate how dramatically data science innovations can benefit the wind industry.
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