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Brian Ó Gallachóir

External Associate

Biography

Prof. Brian Ó Gallachóir is elected Chair of the Executive Committee of the Energy Technology Systems Analysis Program (ETSAP), one of the longest running Technology Collaboration Programmes of the International Energy Agency (IEA). ETSAP represents more than 45 years of international cooperation on technology-rich energy systems optimization modeling. ETSAP comprises about 200 modeling teams in over 70 countries assisting policy makers by undertaking scenario analysis and modeling future energy system pathways.

Brian is also Professor of Energy Engineering at University College Cork, which is ranked in the top 2% of universities in the world, and in the top ten globally on sustainability. Brian is Director of the MaREI research center, Ireland’s center of excellence in energy, climate and marine research, with over 220 researchers and 80 industry partners. He is also an elected Fellow of the Irish Academy of Engineering.

Brian is an acknowledged international expert in energy systems modeling with over 30 years experience, and a highly cited academic. He has also achieved significant impact, translating research results into policy insights that have underpinned Irish and EU energy and climate mitigation policies. He has also co-produced strategies with energy companies to maximize the opportunities arising from the energy transition.

His work spans climate mitigation, energy efficiency, renewable energy, energy security, energy and climate policy, and engaged research on community based climate action. Brian has also increased the transdisciplinary nature of MaREI’s research and this is particularly evident in the rural energy transition multi-stakeholder Dingle Peninsula 2030 partnership project.

In joining as a Visiting Professor, Brian looks forward to engaging with research leaders in CGEP on improving how we use research and analysis to better inform energy and climate policy, and comparing how to deliver the energy transition and improved energy security through the different lenses of European and US perspectives

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Related

Relevant Articles

Transportation

A deep learning architecture for energy service demand estimation in transport sector for Shared Socioeconomic Pathways

Meeting current global passenger and freight transport energy service demands accounts for 20% of annual anthropogenic CO2 emissions, and mitigating these emissions remains a considerable challenge for climate policy. Pursuant to this, energy service demands play a critical role in the energy systems and integrated assessment models but fail to get the attention they warrant. This study introduces a novel custom deep learning neural network architecture (called TrebuNet) that mimics the physical process of firing a trebuchet to model the nuanced dynamics inherent in energy service demand estimation. Here we show, how TrebuNet is designed, trained, and used to estimate transport energy service demand. We find that the TrebuNet architecture shows superior performance compared with traditional multivariate linear regression and state of the art methods like densely connected neural network, Recurrent Neural Network, and Gradient Boosted machine learning algorithms when evaluated for regional demand projection for all modes of transport demands at short, decadal, and medium-term time horizons. Finally, TrebuNet introduces a framework to project energy service demand for regions having multiple countries spanning different socio-economic development pathways which can be replicated for wider regression-based task for timeseries having non-uniform variance.

External Publications with Brian Ó Gallachóir & James Glynn • Nature Scientific Reports • March 02, 2023

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