Dr Conor Lynch is a Research Fellow & Group Lead at the Nimbus research centre with interests including system modelling, machine learning, data analytics and Artificial Intelligence (AI) to optimally integrate and allocate renewable energy resources. He completed two level 8 honorary Bachelor of Engineering degrees in Structural Engineering and Sustainable Energy Technology at the Munster Technological University (formerly Cork Institute of Technology) in 2006 and 2011.
In 2013, Conor was awarded the Irish Research Council for Science Engineering and Technology (IRCSET) Enterprise Partnership PhD Scholarship. Partnering with United Technologies Research Centre (UTRC), he concluded his PhD degree in model predictive control, focusing on Kalman Filtering optimisation techniques to optimally schedule the renewable energy contribution within a micro-grid in the Process, Energy and Transportation Department at Munster Technological University in 2016. During his PhD, he completed a term with the Applied Control Technology Consortium (ACTC) under the guidance of Prof Mike Grimble through the University of Strathclyde, Glasgow, Scotland. In that time, he studied System Modelling, Identification and Parameter Estimation Methods for the Predictive Control for Linear and Nonlinear Systems.
In 2017, he was awarded the Bridge Network Technology Transfer Consortium Invention of the Year award for Engineering, ICT and Physical Sciences for his work on the Nimbus Energy Optimisation System (EOS) project. He now leads the AI, Machine Learning and Data Analytics team within Nimbus with expertise in the aforementioned applied to forecasting, predictive maintenance, anomaly detection, sentiment analysis, natural language processing (NLP), pattern recognition and data classification. Additionally, he is a task participant of the International Energy Agency (IEA) Technology Collaboration Programme (TCP) for Task 36 – Forecasting for Wind Energy.
His research interests include: Optimisation, Micro-Grids, System Modelling, Machine Learning, Data Analytics and Artificial Intelligence for Forecasting and Predictive Control for the integration and allocation of renewable energy resources.
