The UCL Energy Institute delivers world-leading learning, research and policy support on the challenges of climate change and energy security. Our approach blends expertise from across UCL, to make a truly interdisciplinary contribution to the development of a globally sustainable energy system. We are part of The Bartlett: UCL's global faculty of the built environment.
- An invited member of the Australian Greenhouse Office Baseline Study of Greenhouse Gas Emissions from the Residential Construction Sector 1990 to 2010.
- A PhD student at the University of Melbourne. PhD title: ‘Fitness Landscapes and the Precautionary Principle: The Geometry of Environmental Risk’.
- Working at the CSIRO Division of Building Construction and Engineering.
- Doing a Bachelor of Architecture (First class honours) Deakin University.
- Doing a Bachelor of Arts (Architecture) (Deakin University).
I am a Reader in Energy and the Built Environment and Director of Enterprise, at the UCL Energy Institute. I am currently Principle Investigator on the EPSRC/E.ON funded 'Carbon, Control and Comfort' consortium (~£2M 2009-2011) and on the ETI 'Technology Strategy in Smart Systems' project 2010/11. I am a Co-Investigator on the Energy Technology Institute 'Micro Distributed Energy' (Micro-DE) project (~£1M 2010-2011); the EPSRC/EDF 'People, Energy and Buildings: Distribution, Diversity and Dynamics' consortium (~1£M 2010-2013); on the Complex Built Environment Systems (CBES) Platform Grant 'The Unintended Consequences of Decarbonising the Built Environment' (~£1.4M 2011-2016); and am Knowledge Base Supervisor on the PassivSystems/UCL Knowledge Transfer Partnership (KTP) project (~£140k 2010-2012).
My work now focuses primarily on modelling occupant influences on building energy use with particular emphasis on occupant behaviour/technology interactions. My research is predominantly multidisciplinary (spanning the social and physical sciences), empirical (based on analysis of large data-sets), and addresses the uncertainty in the evidence-base on which the models are built. I am an an academic advisor to DECC on DECC/Ofgem/Utilities funded Energy Demand Reduction Project ('Smart Metering') trials, am a consultant to DECC/Ofgem on evaluation of the Smart Metering rollout in the UK. In the past I have done consultancy work for the International Council for Research and Innovation in Building and Construction (CIB); the Waste Resources Action Programme (WRAP); the UK Emissions Trading Group (UK-ETG); the Commission for Architecture and the Built Environment (CABE); and for English Heritage. I am a grant reviewer and panel member for EPSRC, ESRC, AHRC, NERC and the Leverhulme Trust, review papers for ten international journals, and am a member of the Carbon Technical Advisory Group for the London 2012 Olympic Games.
My substantive focus is on statistical modelling of energy use in buildings with a focus on modelling occupant influences on building energy use, particularly socio-demographic influences and occupant/technology interaction influences in occupied homes. The immediate goal of this work is to construct occupancy schedules for BREDEM class models which allow far better estimation of variables such as internal temperature, window opening behaviour and other occupant influences currently set as default parameters in building energy models. My methodological focus is on graphical models, in particular Bayesian graphical modelling methods and fitness landscape models. My work is based mainly on analysis of large data-sets, and seeks to explicitly address the uncertainty in the data-sets on which the models are built. My theoretical focus is on how models shape our understanding of and actions in the world, and on ways of modelling complex systems with uncertainty. Current models of energy use in buildings exogenize uncertainty - we exclude all our instrument error, real static and dynamic variability in parameters (e.g. u-values), and exclude factors we know to be important but don't know how to measure (e.g. occupant behaviour). This leaves current models incomplete, overly precise, and inaccurate, and provides no clue as to the source of the inaccuracy. My interest is in finding ways to endogenize uncertainty within models but in such a way that allows us to distinguish between instrument, aleatory and epistemic forms of uncertainty. I believe this is essential to building better, more robust and ultimately more useful and honest models for policy formulation.