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.