Project name: DASEL, Ciencia de Datos para Redes Eléctricas
Project reference: TED2021-131390B-I00
AEI: TED2021-131390B-I00/ AEI/10.13039/501100011033 DASEL
Funding source: MINECO. Ministerio de Ciencia e Innovación- Proyectos de Transición Ecológica y Transición Digital 2021
Duration: December 2022 – December 2024
Principal Investigator (PI): Enrique Zuazua
About the Project
Digital technologies have become an important part of our daily routine, impacting the way we interact with people and do business. A more productive and environmentally friendly economy requires a methodical research and innovation strategy and implementation with an integrated vision of the future. However, to fully embrace and enhance the many benefits of this technological revolution, it is critical to address the challenges it poses by creating policies and implementing new solutions that give society the confidence, skills and resources it needs to digitize and grow. Data science and machine learning are players in this technological innovation. Today, companies and organizations have become aware of the importance of using these tools to transform data into a competitive advantage by redefining their products and services.
In recent years, data science has become a priority for our society, as well as for companies, which are investing heavily in this sector. At the basis of any technological innovation is a deep and complete understanding of the fundamental principles that govern it. While data science and machine learning are basically present today in any kind of industrial and technological applications, many constituent aspects of these disciplines are still only partially understood. This has opened a new and very fertile field of research for applied mathematicians who, in recent years, have widely exploited this possibility to develop new branches of pure and applied innovative research. Under this perspective, this project aims to contribute to the green and digital transition by addressing some specific issues in data science and machine learning from the point of view of applied mathematics in the field of energy. On the one hand, we will consider key constitutive aspects of these fields in order to strengthen their mathematical foundations. On the other hand, we will consider practical applications in selected but very relevant problems in fields such as electrical engineering and electrical network design. These applications, related to the integration of distributed generation systems from renewable sources such as photovoltaic, wind or hydroelectric power, represent a priority strategic objective for a more sustainable society of the future.
Summary
Digital technologies have become an important part of our daily routine, impacting the way we interact with people and do business. A more productive and environmentally friendly economy requires a methodical and applied research and innovation strategy with an integrated vision of the future. However, to fully embrace and enhance the many benefits of this technological revolution, it is essential to meet the challenges it poses, by creating policies and implementing new solutions that provide society with the confidence, skills and resources it needs to digitize and grow. Data science and machine learning are players in this technological innovation. Nowadays, companies and organizations have become aware of the importance of using these tools to transform data into a competitive advantage by redefining their products and services. In recent years, data science has become a priority for our society, as well as for companies, which are investing heavily in this sector. At the base of any technological innovation is the deep and complete understanding of the fundamental principles that govern it. Although data science and machine learning are present today basically in any type of industrial and technological applications, many constitutive aspects of these disciplines are still only partially understood. This has opened a new and very fertile field of research for applied mathematicians who, in recent years, have widely taken advantage of this possibility to develop new branches of pure and applied innovative research. Under this perspective, this project aims to contribute to the ecological and digital transition by addressing some specific questions in data science and machine learning from the point of view of applied mathematics in the field of energy. On the one hand, we will consider key constitutive aspects of these fields, in order to strengthen their mathematical foundations.