Machine Learning Applications in Renewable Energy
Date: 14 November 2023
Time: 9h30 - 15h30
Venue: Nelson Mandela University
Cost: R250 (add to your registration)
Dr Chantelle Clohessy and Dr Warren Brettenny
This workshop will cover machine learning methods in R and Python specifically as they relate to real world applications in renewable energy. Machine Learning methods have become popular in this space owing to their ability to solve problems and model practical phenomena and be implemented at a large scale. Attendees of this workshop will receive hands on instruction in these methods to develop an understanding of the benefits and limitations of these methods, and to discover how these methods can be implemented in their own field. The methods covered in this workshop include neural networks, support vector machines, random forests, k-nearest neighbours and others. No prior knowledge of these methods is required to attend this workshop.
DR Chantelle May Clohessy is a senior lecturer at the Nelson Mandela University in Gqeberha (Port Elizabeth), South Africa. She obtained her PhD at the Nelson Mandela University in April 2017 in Statistics where her project title was “Statistical Viability Assessment of a Photovoltaic System in the Presence of Data Uncertainty”. She also completed her undergraduate and postgraduate training in both Physics and Statistics at the Nelson Mandela University. Her research focus is on renewable energy applications and statistical techniques used to assess these applications. She has knowledge in the fields of wind turbine noise, faults detection of solar panels using thermal an optical imagery (machine learning), fault detection and energy yield output estimation using tolerance intervals, solar resource forecasting, uncertainty assessments and statistical viability assessments of photovoltaic systems. Dr Clohessy has supervised various projects in the field of renewable energy.
Warren Brettenny, PhD, is a Senior Lecturer and Head of the Department of Statistics at Nelson Mandela University in South Africa and is served as president of the South African Statistical Association (SASA) from 2021-2022. Dr Brettenny received his PhD in Mathematical Statistics from the Nelson Mandela Metropolitan University in 2017 and has supervised postgraduate students and published papers in statistical modelling and machine learning applications in a range of areas including sports, finance, econometrics, and renewable energy. Of late, his research focus has been the use of machine learning techniques in the renewable energy sector, as well as on stochastic efficiency analysis applications.