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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



Data Science Essentials for the Energy Sector: The Case of Wind Energy

28/09/2020 to 02/10/2020 DTU Risø Campus | Roskilde
Duration: 5 days
2 500 €

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.

What is this course about?

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 right 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.

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.  

Who will teach you?

DTU Wind Energy Lecturers: 

Anna Maria Sempreviva
Coordinator of the course. She is Senior Scientist at DTU Wind Energy and has been in the Department of Wind Energy since 2014; previously she was Head of Section at the National Council of Research of Italy, CNR. Since the 1980s, during her 40 year long career in Wind Energy, she has been a domain scientist working with data and databases for wind resources assessment.  At present, her main interest is in data management and open data and innovation. 

Nikolay Dimitrov 
Nikolay Dimitrov  is a Senior Researcher at DTU Wind Energy, with more than 10 years of experience with wind energy. His current scope of work involves applied statistics and probabilistic methods for the purpose of lifetime assessment, structural integrity, fault assessment, risk-based operation & maintenance. Particular scientific interests include Machine Learning applications for design optimization and for data-driven load and power forecasting, as well as risk-based decision modelling.

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

Juan Pablo Murcia 
Juan Pablo Murcia is a Postdoc at DTU Wind Energy focusing on uncertainty quantification, model validation and parallel computing applied to flow modeling and large energy system simulations. He has 3 years of experience working in the wind energy industry on these topics at VESTAS. 

Andrea  Vignaroli 
Andrea  Vignaroli Senior Development Engineer at DTU Wind Energy - Risø Campus. Andrea is currently involved in research and commercial projects concerning the use of ground based and nacelle mounted lidars for resource assessment, power curve verification and turbine control. Also mechanical loads and vibration measurements are part of his daily tasks. 

Pierre-Elouan Réthoré 
Pierre-Elouan Réthoré is a senior researcher at DTU Wind Energy. He has been working for 15 years at the intersection between system engineering, computational fluid dynamics, optimization, uncertainty quantification and machine learning applied to the field of wind farm data analysis, flow modelling and design. He has also been working in wind energy Industry.

Matti Koivisto
Matti Koivisto is a Researcher at DTU Wind Energy, analyzing variability and uncertainty in large-scale wind and solar generation. He has experience in time series modelling, with focus on stochastic simulation including the copula method. He has also worked with machine learning algorithms.

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. 



Key Note Speakers:

Henrik Stiesdal 
Henrik Stiesdal is one of the pioneers of the modern wind industry. He built his first wind turbine in 1976 and in 1978 designed one of the first commercial wind turbines, licensed by Vestas in 1979. Stiesdal worked with Vestas until 1986 and joined Bonus Energy, later Siemens Wind Power in 1987. In 1988 he was appointed Technical Manager, and in 2000 Chief Technology Officer. He retired at the end of 2014. During his 40 years in the wind industry Stiesdal has worked with all aspects of wind turbine technology, including fundamental research, turbine design, manufacturing, sales, project implementation, service and quality management. Post-retirement activities include floating wind turbines, energy storage, carbon-negative fuels, Lidars, and avian deterrent technologies. Henrik Stiesdal is associate professor at DTU Wind Energy and adjunct research professor at the University of Maine. 

Ignacio Marti
Ignacio Marti is the Secretary 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. He has over 20 years of experience in research organisations working with wind energy in Spain, the United Kingdom and Denmark. As previous IEA Wind Chairman, he coordinates a common R&D agenda involving more than 20 countries in Europe, America, Asia and Pacific. IEA Wind publishes influential reports like recommended practices that are widely used by decision makers within the wind energy sector worldwide. As Program Manager at DTU Wind Energy he coordinates the research strategy of the Technical University of Denmark on offshore wind, focusing on excellent research able to create value to industry. Part of the activities in this area involve digitalization. In his role as Head of Section he leads a multidisciplinary research team on wind turbine structures and components.

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). He acts (or has acted) as an Editor for the IEEE Transactions on Power Systems, the International Journal of Forecasting and Wind Energy. His main research interests are centered around the proposal and application of mathematical methods for electricity markets and power systems operation, including forecasting. He has published extensively (>100 articles) in some of the leading journals in Meteorology, Power Systems Engineering, Statistics and Operations Research. In 2019, he has been a Simons fellow at the Isaac Newton Institute, Cambridge University, UK. Lately, he is focusing on novel views of renewable energy forecasting problems, including high-dimensional modelling (predicting at >1000 wind farms at once), forecast reconciliation, distributed learning and data markets." 

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

28/09/2020 to 02/10/2020
DTU Risø Campus | Roskilde
Duration: 5 days

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