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Cliff Elwell is Lecturer in Energy Demand and the Built Environment at UCL Energy Institute, where he is Deputy Director of the Centre for Doctoral Training in Energy Demand and the Built Environment, and Departmental Graduate Tutor for the Bartlett School of Environment, Energy and Resources. Cliff’s research interests are primarily in smart energy systems, measurement and analysis of energy, and related, data.
Cliff’s research interests build on his experience in physics, materials science and energy systems. He has an active interest in applying the tools and techniques – measurement, experimental design, data analysis etc – of these disciplines in the energy demand domain, in relation to relevant policy and economic issues. Aside from a core interest in the measurement of energy use, drivers and consequent factors, and relation to physical principles, Cliff is actively involved in research on smart energy systems and the analysis and interpretation of energy use data.
Smart energy systems
Cliff has a broad interest in smart energy systems, spanning aspects of policy, consumer response to service offerings, data and security, electrical systems and grid operation, energy management and smarter heating. Cliff was previously seconded to the Energy Technologies Institute (ETI), a public private partnership between global industries and the UK Government tasked with developing ‘mass-scale’ technologies that will help the UK meet its 2050 carbon reduction targets, where he developed a holistic vision of smart energy systems.
Cliff is currently particularly interested in the integration of heat, heating systems and thermal storage (“smarter heat”), and in the response of consumers to smart-enabled service offerings and technologies.
Analysis and interpretation of energy use data
Energy research is experiencing a data boom, through national datasets, smart meters and detailed monitoring programmes. It is essential to deliver a robust analysis of such data, to provide scientific and policy insight. Cliff, in collaboration with a number of researchers, is undertaking analysis and interpretation of energy data using a range of techniques, with a recent interest in Bayesian analysis.