Dominic O'Sullivan
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Cited by
An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities
P O’Donovan, K Leahy, K Bruton, DTJ O’Sullivan
Journal of big data 2, 1-26, 2015
Big data in manufacturing: a systematic mapping study
P O’donovan, K Leahy, K Bruton, DTJ O’Sullivan
Journal of Big Data 2, 1-22, 2015
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
Improving building operation by tracking performance metrics throughout the building lifecycle (BLC)
DTJ O’Sullivan, MM Keane, D Kelliher, RJ Hitchcock
Energy and buildings 36 (11), 1075-1090, 2004
Multi-variable optimization of thermal energy efficiency retrofitting of buildings using static modelling and genetic algorithms–A case study
SN Murray, BP Walsh, D Kelliher, DTJ O'Sullivan
Building and Environment 75, 98-107, 2014
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
Review of automated fault detection and diagnostic tools in air handling units
K Bruton, P Raftery, B Kennedy, MM Keane, DTJ O’sullivan
Energy efficiency 7, 335-351, 2014
Diagnosing and predictingwind turbine faults from scada data using support vector machines
K Leahy, RL Hu, IC Konstantakopoulos, CJ Spanos, AM Agogino, ...
International Journal of Prognostics and Health Management 9 (1), 2018
The water energy nexus, an ISO50001 water case study and the need for a water value system
BP Walsh, SN Murray, DTJ O’Sullivan
Water Resources and Industry 10, 15-28, 2015
Development and alpha testing of a cloud based automated fault detection and diagnosis tool for Air Handling Units
K Bruton, P Raftery, P O'Donovan, N Aughney, MM Keane, DTJ O'Sullivan
Automation in Construction 39, 70-83, 2014
Deployment and control of adaptive building facades for energy generation, thermal insulation, ventilation and daylighting: A review
H Alkhatib, P Lemarchand, B Norton, DTJ O'Sullivan
Applied Thermal Engineering 185, 116331, 2021
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
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
Comparative analysis of the AHU InFO fault detection and diagnostic expert tool for AHUs with APAR
K Bruton, D Coakley, P Raftery, DO Cusack, MM Keane, DTJ O’sullivan
Energy Efficiency 8, 299-322, 2015
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
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
Optimization of distributed energy resources in an industrial microgrid
ST Blake, DTJ O'Sullivan
Procedia CIRP 67, 104-109, 2018
A computationally efficient method for fault diagnosis of fan-coil unit terminals in building Heating Ventilation and Air Conditioning systems
A Ranade, G Provan, AED Mady, D O'Sullivan
Journal of Building Engineering 27, 100955, 2020
An industrial water management value system framework development
BP Walsh, DO Cusack, DTJ O’Sullivan
Sustainable Production and Consumption 5, 82-93, 2016
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
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