| A fog computing industrial cyber-physical system for embedded low-latency machine learning Industry 4.0 applications P O'donovan, C Gallagher, K Bruton, DTJ O'Sullivan Manufacturing letters 15, 139-142, 2018 | 176 | 2018 |
| A comparison of fog and cloud computing cyber-physical interfaces for Industry 4.0 real-time embedded machine learning engineering applications P O’Donovan, C Gallagher, K Leahy, DTJ O’Sullivan Computers in industry 110, 12-35, 2019 | 147 | 2019 |
| A robust prescriptive framework and performance metric for diagnosing and predicting wind turbine faults based on SCADA and alarms data with case study K Leahy, C Gallagher, P O’Donovan, K Bruton, DTJ O’Sullivan Energies 11 (7), 1738, 2018 | 68 | 2018 |
| The suitability of machine learning to minimise uncertainty in the measurement and verification of energy savings CV Gallagher, K Bruton, K Leahy, DTJ O’Sullivan Energy and Buildings 158, 647-655, 2018 | 66 | 2018 |
| Issues with data quality for wind turbine condition monitoring and reliability analyses K Leahy, C Gallagher, P O’Donovan, DTJ O’Sullivan Energies 12 (2), 201, 2019 | 62 | 2019 |
| Development and application of a machine learning supported methodology for measurement and verification (M&V) 2.0 CV Gallagher, K Leahy, P O’Donovan, K Bruton, DTJ O’Sullivan Energy and Buildings 167, 8-22, 2018 | 60 | 2018 |
| Automatically identifying and predicting unplanned wind turbine stoppages using scada and alarms system data: Case study and results K Leahy, C Gallagher, K Bruton, P O’Donovan, DTJ O’Sullivan Journal of Physics: Conference Series 926 (1), 012011, 2017 | 42 | 2017 |
| IntelliMaV: A cloud computing measurement and verification 2.0 application for automated, near real-time energy savings quantification and performance deviation detection CV Gallagher, K Leahy, P O’Donovan, K Bruton, DTJ O’Sullivan Energy and buildings 185, 26-38, 2019 | 22 | 2019 |
| Cluster analysis of wind turbine alarms for characterising and classifying stoppages K Leahy, C Gallagher, P O'Donovan, DTJ O'Sullivan IET Renewable Power Generation 12 (10), 1146-1154, 2018 | 21 | 2018 |
| Utilising the Cross Industry Standard Process for Data Mining to reduce uncertainty in the Measurement and Verification of energy savings CV Gallagher, K Bruton, DTJ O’Sullivan Data Mining and Big Data: First International Conference, DMBD 2016, Bali …, 2016 | 7 | 2016 |
| Issues with data quality for wind turbine condition monitoring and reliability analyses, Energies, 12, 201 K Leahy, C Gallagher, P O’Donovan, DT O’Sullivan | 6 | 2019 |
| From M&V to M&T: An artificial intelligence-based framework for real-time performance verification of demand-side energy savings CV Gallagher, P O’Donovan, K Leahy, K Bruton, DTJ O’Sullivan 2018 International Conference on Smart Energy Systems and Technologies (SEST …, 2018 | 5 | 2018 |
| Industrial Big Data Pipeline for Wind Turbine PHM in a Large Manufacturing Facility K Leahy, C Gallagher, P O’Donovan, DTJ O’Sullivan International Journal of Prognostics and Health Management 10 (1), 2019 | 1 | 2019 |
| A data science solution for measurement and verification 2.0 in industrial buildings CV Gallagher University College Cork, 2019 | 1 | 2019 |
| Cluster analysis of wind turbine alarms for characterising and classifying K Leahy, C Gallagher, P O'Donovan, D O'Sullivan | | 2018 |
| Utilising the Cross Industry Standard Process for Data Mining to reduce C Gallagher, K Bruton, DTJ O'Sullivan | | 2016 |
Dominic O'SullivanLecturer, University College CorkVerified email at ucc.ie