A Data-Driven Framework for Quantifying Demand Response Participation Benefit of Industrial Consumers von Siddiquee, S M Shahnewaz, Agyeman, Kofi Afrifa, Bruton, Ken, Howard, Bianca, O'Sullivan, Dominic T.J.

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Development and application of a machine learning supported methodology for measurement and verification (M&V) 2.0 von Gallagher, Colm V., Leahy, Kevin, O’Donovan, Peter, Bruton, Ken, O’Sullivan, Dominic T.J.

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IntelliMaV: A cloud computing measurement and verification 2.0 application for automated, near real-time energy savings quantification and performance deviation detection von Gallagher, Colm V., Leahy, Kevin, O’Donovan, Peter, Bruton, Ken, O’Sullivan, Dominic T.J.

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The HyDAPI framework: a versatile tool integrating Lean Six Sigma and digitalisation for improved quality management in Industry 4.0 von Clancy, Rose, Bruton, Ken, O’Sullivan, Dominic T.J., Cloonan, Aidan J.

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Automatically Identifying and Predicting Unplanned Wind Turbine Stoppages Using SCADA and Alarms System Data: Case Study and Results von Leahy, Kevin, Gallagher, Colm, Bruton, Ken, O'Donovan, Peter, O'Sullivan, Dominic T.J.

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Demand response in smart grid – A systematic mapping study von SM Shahnewaz Siddiquee, Bianca Howard, Dominic TJ O'Sullivan, Ken Bruton

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