Dr. Conor Lynch
Research Fellow & Group Lead
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 respectively.
In 2013, he 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. More recently, in 2024 he completed a Postgraduate Diploma in Teaching and Learning in Higher Education through the MTU Teaching and Learning Unit.
At the 2025 MTU Innovation awards, Conor was granted the Best Overall Invention Disclosure Form. Previously, 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
C. O’Leary, G. Smith and C. Lynch, “Speech Emotion Recognition from Audio Data using Machine Learning and Deep Learning Approaches”, 2025, 33rd International Conference on Artificial Intelligence and Cognitive Science (AICS 2025), Dublin, Ireland, December 2, 2025.
E. Asghar, M. Hill, I. Sengor and C. Lynch, “Deep Q-Network for Intelligent Energy Management System in an Energy Community”, 2025, Elsevier Journal of Energy Strategy Reviews https://doi.org/10.1016/j.esr.2025.101991
E. Asghar, M. Hill, I. Sengor, C. Lynch and P. Q. An, “Validation of a 24-hour-ahead Prediction Model for a Residential Electrical Load Under Diverse Climate”, 2026, GMSARN Greater Mekong Subregion Academic and Research Network International Journal – accepted for publication
K. Artiaga, M. Hasanuzzaman, H. Afli, C. Lynch, "Rethinking Sign Language Translation: The Impact of Signer Dependence on Model Evaluation ”, 2025 conference on Empirical Methods in Natural Language Processing (EMNLP), Suzhou, China, Nov 5-9, 2025 – acceptance rate of 22.16%
C. O’Leary, C. Lynch and F. Ghassemi Toosi, 2024, “A Comparative Analysis of Automated Machine Learning for Electricity Price Forecasting”, 2024, Journal in Applied Computer Systems, vol. 29, issue 2, pp. 43-52.
E. Asghar, M. Hill, I. Sengor, P. Q. An and C. Lynch, “Day-ahead Irradiance and Electrical Demand Prediction for Sustainable Energy Communities”, 2024, IEEE International Conference on Logistics and Industrial Engineering (ICLIE), Ho Chi Minh City, Vietnam, 01 December.
A. de Juan, C. Lynch and T. Hogan, 2024, “A Phenomenological Exploration of Mixed Reality Digital Twins”, 2024, 17th Irish Human Computer Interaction (iHCI) Symposium, Munster Technological University, Cork, Ireland, 08 November 2024
P. Markappa, C. O’Leary and C. Lynch, “A Review of YOLO Models for Soccer-Based Object Detection”, 2024, 6th International Conference On Intelligent Computing in Data Sciences (ICDS), Marrakech, Morocco, 23-24 October.
K. Artiaga, M. Hasanuzzaman, H. Afli and C. Lynch, "The Influence of Iconicity in Transfer Learning for Sign Language Recognition", 2024, The 29th International Conference on Natural Language & information Systems (NLDB 2024), Turin, Italy, 25-27 June
Besnier, B. Basu, C. O Leary, C. Lynch, S. McKeever and V. Lakshmi, “Understanding the Inniscarra reservoir fluctuations to predict downstream flooding through hydrological modelling”, 2023, American Geophysical Union (AGU) fall meeting, San Francisco, CA, December 11-15.
P. Q. An, C. O'Leary, G. Smith, M. Caballero and C. Lynch, “Evaluation of Perceptron Learning Algorithm for Day-Ahead Photovoltaic Power Forecasting”, 2023, International Symposium on Electrical and Electronics Engineering (ISEE), Vietnam, 19-20 Oct.
E. Asghar, M. Hill and C. Lynch, “A Review on Data-driven Energy Management Systems”, 2023, IEEE Power & Energy Society (PES) Generation Transmission and Distribution (GT&D) International Conference and Exposition, Istanbul, 23-25 May.
M. Caballero, G. Smith and C. Lynch, “Renew(able) or Die? An Optimised Approach to Minimise Cost in Upgrading an Outmoded Solar Energy System”, 2023, Embedded World Exhibition and Conference, Nuremberg, Germany, 14-16 March
C. O’Leary, F. Ghassemi Toosi and C. Lynch, “A Review of AutoML Software Tools for Time Series Forecasting and Anomaly Detection”, 2023, 15th International Conference on Agents and Artificial Intelligence (ICAART), 22-24 February, Lisbon, Portugal
E. Asghar, M. Hill and C. Lynch, “A Regularization-Based Long Short-Term Memory Architecture Design for Solar Irradiance Prediction”, 2nd International Conference on Electrical, Computer and Energy Technologies (ICECET), 20-22 July 2022, Prague, Czech Republic
R. Bain, C. Lynch, D. McDonnell and K. Witheephanich, “An XGBoost approach for industrial component degradation classification”, 2022, 33rd Irish Signals & Systems Conference (ISSC), Cork, Ireland, 9-10 June
C. O’Leary and C. Lynch, “An Evaluation of Machine Learning Approaches for Milk Volume Prediction in Ireland”, 2022, 33rd Irish Signals & Systems Conference (ISSC), Cork, Ireland, 9-10 June
C. Lynch, C. O’Leary, PGK. Sundareshan and Y. Akin, “Experimental Analysis of GBM to Expand the Time Horizon of Irish Electricity Price Forecasts”, Energies, 2021; 14(22):7587. https://doi.org/10.3390/en14227587
C. O’Leary, C. Lynch, R. Bain, G. Smith and D. Grimes, “A Comparison of Deep Learning vs Traditional Machine Learning for Electricity Price Forecasting”, ICICT 2021, 4th International Conference on Information and Computer Technologies, Kahului, Maui Island, Hawaii, United States, 11-14 March 2021. DOI: 10.1109/ICICT52872.2021.00009 - Awarded Best Conference Presentation Certificate
C. Lynch, C. O’Leary, G. Smith, R. Bain, J. Kehoe, A. Vakaloudis and R. Linger, “A review of open-source machine learning algorithms for Twitter text sentiment analysis and image classification”, WCCI 2020, IEEE World Conference on Computational Intelligence, Glasgow, Scotland, 19-24 July 2020. DOI: 10.1109/IJCNN48605.2020.9207544
C. Lynch, M. J. O’Mahony and R. A. Guinee, “Novel Kalman Filter Bank Methodology for Time-Series Prediction in Forecasting Applications” Proc. 2nd International Conference on Data-Driven Plasma Science (ICDDPS), 13 – 17 May 2019, Marseille, France.
C. Lynch, J. Kehoe, R. Bain, F. Zhang, J. Flynn, C. O’Leary, G. Smith, R. Linger, K. Fitzgibbon and F. Feijoo, “SVM-Based Day-Ahead Electricity Price Prediction Model for the Single Electricity Market in Ireland”, ISF 2019, 39th International Symposium on Forecasting, Thessaloniki, Greece, 16 -19 June 2019.
A. Baró Pérez, C. Lynch, A. L. Ferrer Hernández, I. B. Montejo and A.R. Rodríguez, 2018. “Kalman Filter bank post-processor methodology for the Weather Research and Forecasting Model wind speed grid model output correction”, International Journal of Sustainable Energy, vol. 38, issue 6, pp. 511–525.
C. Lynch, M. J. O’Mahony and R. A. Guinee. “Electrical Load Forecasting Using an Expanded Kalman Filter Bank Methodology.” 4th International Federation of Automatic Control (IFAC) Conference on Programmable Devices and Embedded Systems (PDES) 2016: Brno, Czech Republic, 5-7 October 2016. IFAC-Papers Online, vol. 49, no. 25, pp. 358–365, 2016.
C. Lynch, M. J. O’Mahony and R. A. Guinee, “24 Hour Step Ahead Wind Speed / Wind Power Prediction Using A Novel Kalman Filter Bank Prediction Estimator”, Proc. ASME 2015 Power Energy Conversion Conference. PowerEnergy2015, 28 June – 2 July 2015, San Diego, CA, 2015.
C. Lynch, M. J. O’Mahony, and R. A. Guinee, “A Novel 24 Kalman Filter Bank Estimator for Solar Irradiance Prediction for PV Power Generation”, Proc. 42nd IEEE Photovoltaic Specialists Conference (PVSC), 14 - 19 June, New Orleans, LA, 2015.
C. Lynch, M. J. O’Mahony and R. A. Guinee, “Accurate Day Ahead Temperature Prediction Using A 24 Kalman Filter Estimator”, Proc. IEEE 2015 PRIME Conference. 29 June – 2 July, Glasgow, UK, 2015.
C. Lynch, M. J. O’Mahony and T. Scully, 2014. “Simplified Method to Derive the Kalman Filter Covariance Matrices to Predict Wind Speeds from a NWP Model”, Energy Procedia, 62, pp. 676–685
M.A.R.S Shield - Mitigating, Automating, and Remediating Security, An AI-based automated incident response system
SERTS – Sentiment Analysis Evaluation of Real-Time Speech
Analog Devices International - AI solution to detect and segment wafer map anomalies
O-PENS – Optimise Predict Energy System – microgrid optimisation project
STEP4 – Short Term Electricity Price Forecaster
Clarke Analytics – AI Trustworthiness
Predictive Control Systems (PCS) – Energy system modelling and optimisation
SteriTrack – Development of a direct part marking (DPM) scanner for the healthcare sector