Dr Eoghan Mckenna

Research summary

To achieve net zero and ensure a clean, secure, and affordable energy system, the buildings sector faces the unprecedented challenge of requiring the comprehensive and rapid upgrading of the entire building stock to 1) reduce energy usage and its associated green-house gas emissions, 2) electrify and decarbonise heating, and 3) replace flexibility of fossil fuel plant with the integrated operation of millions of smart distributed energy assets installed in homes such as batteries, heat pumps, and electric vehicles.

In my research I aim to help address this challenge by using smart meters to study energy usage in residential buildings.

My primary areas of research are:

• Smart meter data access: development of equipment and processes necessary to remotely access these smart meter data;
• Smart meter data studies: development and collection of smart meter data for experimental studies on residential energy usage in particular: ‘Observatory’ studies where the goal is to analyse observations from a sample of households and produce generalisable insights and estimates for a population; and ‘Laboratory’ studies where the goal is usually to estimate the effect of an intervention on a household or group of households e.g. estimate the performance of a retro-fit energy efficiency measure;
• Smart meter data analytics: development and application of methods to analyse smart meter data for net zero applications; and
• Open science: development of processes to make these data, equipment, and methods openly available for wider use by the UK research community.

My current research activities are:

Maintaining, improving, and expanding the Smart Energy Research Lab (SERL)

The Smart Energy Research Lab (SERL) provides the UK energy research community with a unique data resource consisting of high-quality, high-resolution domestic smart meter energy consumption data linked to rich contextual data for SERL’s Observatory longitudinal panel of >13,000 GB homes. This data provides a reliable evidence base for intervention, observational and longitudinal studies across a wide socio-technical spectrum of energy research.

I have worked on SERL since 2018 and led workstreams on research design, data governance (with a particular focus on compliance with the Smart Energy Code) and ethics, survey development, as well as developing foundational methods for analysing SERL data.

My focus now is to extend the value and use of SERL (continued collection and publishing of data, recruiting and adding new sub-samples and data variables to the Observatory, expanding the user base) and ultimately developing SERL into a national research facility.

Machine learning and smart meter data

Machine learning has had successes with the growth of big data in other sectors. There is an opportunity to learn from and build on what has worked elsewhere and apply it to the emergence of ‘big’ smart meter data in the buildings sector.

My research aims to develop and disseminate buildings sector-specific expertise and knowledge about the application of machine learning to smart meter datasets.

In addition to the above, I have secondary research interests and publication records in: solar photovoltaic self-consumption, ‘bottom-up’ building energy demand modelling, and integrating renewable energy into low-carbon power systems.

Teaching summary

I teach on the UCL Energy Institute’s MSc inSmart Energy and Built Environment (SEBE). I developed and deliver two lectures: one on Energy Grids for the Fundamentals module, and one on Neural Networks for the Data Analytics module. I am also a Teaching Assistant and help run the Fundamentals module’s workshops, its field trip, and do second marking for its exam.

I am subsidiary supervisor to two PhD students within BSEER and supervise BSEER MSc student research dissertations.

Publications

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