Resource assessment - part 1
When: Wednesday, 3 April 2019, 10:45 - 12:15
Where: Luxua 2
The Resource Assessment programme will span across 3 sessions. Each will take the audience through a range of topics. These will include: ice losses, turbulence intensity measurements, wake and wind flow modelling improvements, seasonal forecasting, machine learning applications, and the impact of real world conditions on turbine performance. Each of these topics has the potential to have a profound impact upon the energy yield. It is vital they are thoroughly understood. In part 1, participants will hear about how to account for the impacts of climate change on the wind resource and its assessment, and learn about advance weather prediction technology, complex flow and wake modelling and measuring and 3D LiDAR scanning and applications.
Attendees will gain an understanding of how to use available data to estimate potential climate change impact in wind energy estimations; Attendees will also learn how technology allows to use powerful wind flow models like LES to improve; Attendees will learn how Lidars can be used to validate wind flow techniques to get maximum value from Lidar measurements, including Turbulence Intensity. Attendees will learn about the importance and challenges of validatiing wake models.
Head of Section Iberia & Latin America, DNV GL
Technical Manager, 3E
The Copernicus Climate Change Service as a resource to facilitate the assessment of climate change impact on wind resource
Sectoral Information System Officer, European Centre for Medium-Range Weather Forecasts
Turbulence Intensity measurements from ground-based vertically-profiling lidar – a multi-site comparison between ZX300 and traditional anemometry
Wind & Verifications Engineer, Zephir Ltd
Breakthrough weather prediction technology enables wind turbine resolving resource assessments.
Director of Operations, Whiffle Weather Finecasting
Unravelling the wind flow over highly complex regions through computational modelling and three-dimensional lidar scanning
Professor, University of Porto
A multi-project validation study of a time series-based wake model
Senior Scientist, Vaisala