Resource assessment - part 3
When: Wednesday, 3 April 2019, 16:15 - 17:45
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 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 and therefore it is vital they are thoroughly understood.
Invited speakers from outside the Resource Assessment community will present: the potential impacts of climate change on the wind resource; insights on measurements from the data user perspective; and how to improve annual energy production using operational data.
Part 3 will take the audience through a range of topics ranging from wake modelling improvements, multiscale modelling, machine learning applications through to innovative uses of drones. The invited presentation will bring an OEM prespective on optimizing Annual Energy Production.
View the rest of the programme: Part 1 - Part 2
- Attendees will gain understanding of new machine learning applications to reduce uncertainty in energy yield assessment;
- Attendees will be able to learn about new innovative uses for drones in resource assessment;
- Attendees will be able to apply a new implementation of a wake model;
- Attendees will learn about new approaches to atmospheric boundary layer multiscale modelling
- Attendees will be able to understand the benefits of community model development
Senior Specialist - Energy Meteorology, Vestas Wind Systems A/S
How drones can improve topography inspections, terrain modelling and energy yield assessment
Senior Renewable Energy Consultant, Wood
On the meso-to-microscale modeling of the atmospheric boundary layer in a complex terrain site: an open-science framework.
Novel machine learning methods to reduce uncertainty in energy yield assessment
Analyst, K2 Management
Engineering models for turbine wake velocity deficit and wake deflection. A new proposed approach for onshore and offshore applications.
Researcher, DNV GL