About the MasterClass
You are invited to join the new online master class in Data Science Essentials for Wind Energy!
In this class, you will explore answers to questions such as:
1. What are the key data science practices that can enhance wind energy research and development?
2. How can metadata, data visualisation, and anonymisation improve data quality and usability in industrial settings?
3. How can machine learning and AI be effectively applied to solve real-world challenges in wind energy?
4. What does working with wind energy data really look like?
You will also gain hands-on experience with Python libraries tailored for data analysis, feature engineering, and model evaluation, and learn how to manage and process data from wind turbines, hybrid power plants, and numerical experiments. You will also explore APIs, data platforms, and licensing for data-driven tools.
If you're working in the wind energy industry and want to strengthen your data science skills to drive innovation and performance, this is the course for you.
If you want to read more about the course, click here.
You will leave the masterclass with
An overview of fundamental concepts and key libraries for data-driven wind energy applications
Practical skills in time-series analysis and hands-on forecasting
Feature engineering and applied machine learning using numerical and observational data
Introduction of several APIs and data platforms relevant for wind and energy systems
Insights into licensing for data-driven tools
Speaker and Faculty
Connect with leading scientists
driving innovation in wind energy.
Nikolay Dimitrov is a Senior Scientist in the Department of Wind and Energy Systems at DTU, widely recognised for his work in wind turbine reliability, digital twin technology, and uncertainty quantification. Dr. Dimitrov’s research drives advancements in lifetime assessment, stochastic modelling, and asset management, and he has led or contributed to multiple European research collaborations and published extensively in international journals. He brings his expertise directly into the classroom, teaching specialised graduate-level courses and supervising master’s and doctoral students in areas bridging theoretical methods, practical engineering, and digital innovation for the wind energy sector.
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Matti Koivisto is a Senior Researcher in the Department of Wind and Energy Systems at DTU. He is highly regarded for his research on energy system integration of variable renewable energy (VRE), time series modelling, and the statistical analysis of wind and solar power generation. Dr. Koivisto’s published work has influenced the understanding of VRE variability and uncertainty in continental-scale renewable energy systems. As an educator, he develops and delivers advanced modules on data analysis and renewable energy integration, as well as supervising MSc and PhD students focused on integration of VRE to large-scale energy systems.
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Tuhfe Göçmen is a Senior Researcher in the Department of Wind and Energy Systems at the Technical University of Denmark (DTU). An accomplished specialist in wind farm operation and control, particularly via data analytics. Dr. Göçmen focuses on the development and application of advanced methods for modeling, optimizing, and forecasting wind power production in complex operational environments. Her research spans robust SCADA-based flow and turbine(s) response modeling, as well as big data analytics for wind energy, with contributions to international research collaborations such as the DigiWind and TWAIN projects. As an educator, she actively teaches and supervises at graduate and doctoral levels, and is dedicated to mentoring emerging engineers in data-driven approaches and digital transformation within wind energy.
MasterClass Programme
1.
Quick welcome and introduction by Tuhfe Göçmen, DigiWind project coordinator
2.
30-40 minute presentation of the masterclass by Tuhfe Göçmen, Matti Juhani Koivisto and Nikolay Dimitrov
3.
20-minute Q&A about the masterclass.
Do you have any questions?
