Wind turbine electricity production data is sensitive for Energinet (and for the wind turbine producers). Energinet would like to publish wind turbine electricity production data sets that can be used to train relevant models and to develop innovative applications, without giving away sensitive data. The goal of the project is to explore various data publishing methods for that purpose.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, data publication

Energinet has a model that describes the electricity production of a given wind turbine given wind conditions. The current model based on kNN is trained with DMI weather data and historical electricity production data for the wind turbine. The goal of the project is to improve the current model with lifelong learning, extended weather data and different models for a range of different wind …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Data Analysis

The goal of the project is to explore the accuracy of electricity production predictions based on historical data and weather predictions. This may be tackled as a sequence prediction problem using recurrent neural networks The long term goal is to incorporate wind turbines in the reserve market for electricity.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Forecasting, Machine Learning, Deep Learning

The goal of the project is to explore new ways of gathering data about wind turbines as well as local wind/weather conditions. To this end, sound/vibration-based and/or image-based instrumentation as well as innovative experiments such as balloons and light weight weather stations might be considered.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Instrumentation, Sensors