Sessions details



Media Partners


SESSION 1: Deriving value from Big Data

Session Chair: Claudia Puyals, Senior Analyst, Performance Assessment, IBLA, AWS Truepower

Session Description

In this session, several Big Data experts will explain the application of Big Data to the wind industry and how wind farm operators can take advantage of its full potential. Big Data contains valuable information, and its proper analysis can give owners and operators a deep insight into plant performance while revealing key elements for O&M cost reduction. During the session, the audience will learn how to overcome the complexity of Big Data analysis and will discover a number of innovative applications to get more efficient plants, effective power curve tests and improved power forecasting.

Learning objectives
  • Innovative Big Data applications for the wind industry
  • Analysis techniques for Big Data
  • Performance improvement and costs reduction through Big Data

SESSION 2: Enhancing turbine performance using data

Session Chair: Lars Landberg, Director, Group Leader, Renewables, Strategic Research and Innovation, DNV GL

Session Description

This session will explain various ways of enhancing turbine performance using data. We will get perspectives from many places around the globe, and illustrate findings with real wind-farm data from thousands of wind farms. Methods for enhancing the performance will include root-cause analysis, ways of using yaw control, and data-based and data-derived systems.

Learning objectives
  • Understand what turbine performance is
  • Learn how data can be used to enhance turbine performance
  • See how real wind-farm-derived data can be used to enhance turbine performance

SESSION 3: Lowering operational costs

Session Chair: Mike Anderson, Former Chief Technical Officer, RES Ltd.

Session Description

The under- or over-performance of a wind farm can be attributed to many causes: wind speed, turbine performance, accessibility or a poor estimate of the operational loss adjustment factors. This session will review a number of methodologies using the review of historical operational data to improve the accuracy of the future energy yield of a project.  For offshore wind farms, accessibility is seen as a significant potential source of energy loss and, therefore, we will discuss strategies which attempt to mitigate this loss.

Learning objectives
  • Innovative techniques for understanding operational data.
  • An understanding of the German FGW TR10.
  • Novel vessel and access technologies for offshore wind farms.

SESSION 4: Back to the future: from post-construction yield analysis to life extension

Session Chair: Hans Ejsing Jørgensen, Head of section Meteorology & Remote Sensing Program manager – Siting & Integration, DTU

Session Description

This session is divided into two parts. Firstly, we will look back on the performance of wind farms using different data-sets, real-time computing and data analysis to provide surveillance and control for how wind farms are performing.
Secondly, we will look into the future, using tools that are investigating the wind farm load patterns  with the aim of increasing the lifetime of wind farms, estimating the remaining life time of a wind farm, and redesigning O&M strategies for wind farm lifetime extension.


Part 1 – Post-construction yield analysis and reanalysis

Part 2: from performance analysis to life extension decisions

SESSION 5: Innovations in operations & hybrid systems

Session Chair: Christian Jourdain, Head of Marketing, Services, Siemens Gamesa

Session Description

Participants will learn more about some of the innovations tested in operations. Illustrated with examples, you will understand how O&M diagnostics can be more and more automatized thanks to machine learning and fine-tuned SCADA analysis, which will maximize O&M efficiency. In addition, you will learn more about various onshore and offshore wind-storage systems and how this impacts project’s ROI and LCOE.

Learning objectives
  • Understand how machine learning can lead to higher decision automating
  • How much you can get from the data you already have
  • Learn more about storage