Friday October 2nd, 2020
The CTCE is pleased to invite you to the 13th W-ENER session on October 6th at 9:30 Chilean time given by Dr. Cesar Astudillo, which will be held in English and has the title: Machine Learning for Renewable Energies: An Interdisciplinary perspective.
Here is the summary of the presentation as well as the biography of the main presenter and the panelists:
Power converters are essential for the use of renewable energy resources. For example, a photovoltaic system produces DC energy that is transformed into AC by the VSI. This power is used by a motor drive that operates at different speeds, generating variable loads. Two parameters, namely, resistance and inductance, are essential to correctly adjust the model predictive control (MPC) in a VSI. On the other hand, Machine Learning (ML) is a field within Artificial Intelligence that focuses on algorithms that learn from data. The motivation is to support the design and development of non-intrusive models to predict the resistance and inductance of a voltage source inverter (VSI) under different conditions. We will explain how we generated data comprising simulations varying the inductance and the resistance within a VSI, and how we benchmark ML methods to predict those variables, finding accurate models. We will also give our insights on how interdisciplinary research is essential for solving complex problems in engineering.
Dr. Cesar Astudillo
Improve the wellness of people and society through artificial intelligence is one of his main flags. This is because, unlike the paradigm of the techy guy being solitary, César is outgoing. He enjoys soccer in grass pitch and crying defeats with the crew. He loves playing music and sharing with his family. The Ph.D. taught him that Canadian winter is excellent for ice skating as well as unveiling the secrets of pattern recognition and artificial intelligence. César loves teaching and research, which empowers his academic role at the Universidad de Talca, where he is currently the head of the Computer Science Department.
Prof. Dr. Yamisleydi Salgueiro received her B.Sc. degree in Computer Science from the Universidad de Oriente, Cuba in 2006, her M.Sc. in Applied Informatics from the Universidad de Camaguey in 2011, and her Ph.D. from the Central University de las Villas, Cuba in 2016. She is currently a Lecturer at the Computer Science Department, University of Talca, Curicó, Chile. Her main research areas are Machine Learning, Deep Learning, Multi-objective Optimization and Smart Microgrids.
Dr. Colin Bellinger is a Research Scientist in Data Science for Complex Systems at the National Research Council of Canada. He received a Ph.D. from the University of Ottawa in 2016. Dr. Bellinger held a Postdoctoral Fellowship with the Alberta Machine Intelligence Institute (AMII) at the University of Alberta in Edmonton, Canada, and the Donald Hill Fellowship in Computer Science at Dalhousie University in Halifax, Canada. He has co-authored more than 20 articles in international conferences and journals focused on machine learning, data mining, health and epidemiology, and received two best paper awards. Dr. Bellinger was the Program Chair for the Graduate Students’ AI Symposium in 2017 and 2019, has participated on numerous program committees and reviewed for over 10 international journals.