Thesis - Machine Learning for short-term wind-speed forecasting
IAV Automotive Engineering
Gifhorn
vor 12 Tg.

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Thesis - Machine Learning for short-term wind-speed forecasting

Your tasks

The steady progress in wind-turbine automation introduces high demands on the mathematical modelling of the turbine and its environment.

Especially new, sophisticated control methods like nonlinear model predictive control (NMPC) could benefit from a precise short-

term prediction of the relevant wind velocities. Compared to classical, statistical prediction methods (e.g. AR, ARMA, ARIMA), data-

based methods like machine- / deep-learning show promising room for improvement in wind prediction models.

Within the scope of your thesis you should develop and evaluate the suitability of machine- / deep-learning algorithms for the short-

term wind prediction ( 30s) of wind velocities, based on real measurement data. Your tasks include :

Comprehensive literature research regarding existing approaches

Development of a suitable neural network (NN) modelling approach

Implementation of the chosen NN in a suitable machine-learning framework (e.g. Tensorflow)

Performance evaluation and optimization with meaningful metrics

Quantitative assessment of the results with respect to state-of-the-art methods

Your skills

Studys in the field of mathematics, computer science or electrical engineering

Experience in the field of machine learning

Good programming skills in MATLAB and Python

Experience with Tensorflow desirable

Fluent in German and / or English

Autonomous working and goal-oriented style

Our offer

You will be working in a highly motivated team on demanding and trend-setting assignments with a great deal of scope for your ideas.

Our qualification programme promotes your professional and personal development. All of that offered in a modern, attractive working environment with a constantly growing, globally operating successful company.

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