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Data science essentials for the energy sector: the case of wind energy

Data science essentials for the energy sector: the case of wind energy

Harness the power of data in the digitalized energy sector

 

 

Upcoming cohorts

Data science essentials for the energy sector: the case of wind energy

08/04/2019 to 12/04/2019
Lyngby, Denmark | 5 days
2500

About this course

The green revolution is underway and it is fueled by an increasingly high share of weather dependent renewable energy sources, such as wind and solar photovoltaics. Renewables sources raise a number of challenges, stemming from their variability in time and space, broad spatial distribution, different capacities and the uncertainty in predicting their production. Some of the key challenges are: How should renewables be integrated into the existing power system; How can the renewables remain price competitive with respect to fossil fuels; and how can the efficiency and reliability of renewable power plants be improved across all life cycle stages.

Increasingly, these challenges are being investigated using digitalization, which is fueled by increases in computer connectivity and decreases in the cost of computer storage and processing power. The techniques used for these investigations are emerging from new fields of research, leading to new professions. A pivotal new area is the field of data science.

Data science combines data management tools, which help document, organize, and manage data, with statistical methods that are used to extract information and identify patterns that can be transformed into new insights, products, services, or processes, i.e., into value. To support data science, there is an ongoing effort to digitize infrastructures and assets, which helps to create the raw multi-disciplinary and multi-sectoral data that is needed for data science. Once this data is created, novel algorithms need to be identified to extract the key information from data.

Is this course for you?

This course is designed for future energy domain professionals, who wish understand essential data science tools, allowing you to select the best data science tool for different analyses of multi-disciplinary and multi-dimensional data sets. You will also learn about the data lifecycle, which will enable you to produce and distribute datasets that can be used by others in the industry.

 

The course comprises a pre-online course as preparation for the face-to-face session:

 

 

How will the course increase my opportunities?

In this new digitalization era, to be familiar with data stewardship and equipped with data science essentials is an added value as energy domain scientist when applying for a job in both industry and academia.

Data is an asset and skills in data stewardship and knowledge in data science are seen as a key competence to unveil new insights from new information and create competitive advantage. As a domain specialist your expertise is crucial in order to formulate a problem, identify and collect and analyse data, discuss and interpret results to foresee innovation products, services, or business processes.

What will I learn?

The course will provide you with the competences and knowledge necessary to extract important information from energy sector data, and encourage the innovative thinking required to make significant and strategic changes that minimize costs and maximize efficiency, outcomes, and values.

During the course you will be introduced to basic data science skills including Artificial Intelligence with  different types of Machine Learning techniques. As a relatively mature technology, wind energy will provide good case studies from different stages of the lifetime cycle of wind energy power plants that will allow you to put your new competences and knowledge to work on real life cases.  

Course experts

Anna Maria Sempreviva

Anna Maria Sempreviva is Senior Scientist at DTU in the Department of Wind Energy, in the Resource Assessment Modelling section.

Neil Davis

Neil Davis is currently Technical Lead for Wind Resource Assessment Applications at DTU Wind Energy in the Resource Assessment Modelling section.

Nikola Vasiljevic

Nikola Vasiljević is a researcher at DTU Wind Energy, in the section for Meteorology and Remote Sensing. 

Dr. Ju Feng

Dr. Ju Feng is currently a senior researcher at DTU Wind Energy, section of fluid mechanics. In 2011, he got his PhD in solid mechanics at Zhejiang University in China. He has been working at DTU Wind Energy since 2012, focusing his research on wind farms.

Dr. Nikolay Dimitrov

Dr. Nikolay Dimitrov is a Senior Researcher at DTU Wind Energy. He has been working in the Wind Energy industry for more than 10 years, and has been doing research at DTU Wind Energy since 2013.

Dr. Tuhfe Göçmen

Dr. Tuhfe Göçmen is currently a PostDoc in DTU Wind Energy focusing on the uncertainty quantification and validation of the SCADA based flow modelling and control. s description.

Laura Schröder

Laura Schröder is a PhD student at DTU Wind Energy in the section for Loads and Control. In her PhD project she is using model-supported data analytics for improving the operational performance of a wind farm

Henrik Stiesdal

Henrik Stiesdal is associate professor at DTU Wind Energy and adjunct research professor at the University of Maine.

Ignacio Marti

Marti Ignacio Marti is Chairman of the IEA Technology Collaboration Programme for Wind Energy, and Manager of the Offshore Wind Program and Head of Section at DTU Wind Energy in Denmark.

Hugo Maxwell Connery

Hugo Maxwell Connery  Since 2006  he has been  Head of Information Technology section at the Department of Environment at DTU. He is assisting the research community by educating researchers in various programming languages and the GNU/Linux operating system, as well as various software development tools e.g git.

Ole Winther

Ole Winther is Professor in Data Science and Complexity at DTU Compute, Technical University of Denmark and professor MSO in Genomic Bioinformatics at Dept. of Biology, KU/Rigshospitalet. Ole Winther works on deep learning methodology and genomic data science for cancer diagnosis and treatment. 

Pierre Pinson

Pierre Pinson is a Professor at the Centre for Electric Power and Energy (CEE) of the Technical University of Denmark (DTU, Dept. of Electrical Engineering), also heading a group focusing on Energy Analytics & Markets. He holds a M.Sc. In Applied Mathematics from INSA Toulouse and a Ph.D. In Energy Engineering from Ecole de Mines de Paris (France). 

Dr. Falco Hüser

Since 2015, he has been working with research data management in the central administration at DTU. With a background as scientist in the area of computational physics and chemistry at the Karlsruhe Institute of Technology, Germany, the Technical University of Denmark, DTU, and Copenhagen University, his ambition is to foster good practices for research data management and support researchers according to their needs.

Dr. Paula Martinez Lavanchy.

Senior Project Officer in the Office for Innovation and Sector Services Technical University of Denmark. She is a former scientist in the area of environmental microbiology and biotechnology, working for ten years in Germany at the Helmholtz-Centre for Environmental Research. In 2015, joined DTU's central administration to support researchers on research data management (RDM) and to help establishing relevant services around this topic at the University. 

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