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Thesis title: Generating a physically based, smart meter data driven model to support efficiency decision making in individual homes
Primary supervisor: Tadj Oreszczyn
Secondary supervisor: David Shipworth
Starting date: 01/10/2013
Projected completion date: 01/02/2017
Dwelling energy demand assessment procedures through the standard EPC mechanism are slow, intrusive, and costly. Furthermore the validity of the EPC estimates is increasingly being called into question. Accurate characterisation of energy demand of homes is required for effective efficiency improvement policies to go forward. By using smart meter data together with big data analytic methods, this study aims to generate physically based empirical models of dwellings based on their energy consumption profiles and associated weather time series. The method will be established using a large smart meter dataset for the UK, and be designed to scale effectively to be potentially applied to all smart meter equipped dwellings.
Biography
Jonathan Chambers is a PhD student at the UCL Energy Institute.
Jonathan received a BSc in Physics and Philosophy at Durham University in 2009. From 2009 to 2012 he followed the MSc in Energy Science and Technology program at the Swiss Federal Institute of Technology, Zürich (ETHZ) with a focus on solar energy and energy efficiency.
In solar energy, he contributed to the high concentrating solar energy and solar fuels project at the Paul Scherrer Institute. He worked as scientific associate at Airlight Energy in Biasca (CH) on patent-pending optics for solar concentrating power. He also completed a policy-oriented internship at the UNFCCC in Bonn.
In energy efficiency, he completed his Masters Thesis on applying network theory to study behavioural changes in home energy consumption. Prior to joining UCL, Jonathan was the Data Analytics Engineer for BEN Energy, a start-up in home energy management.
His academic interests include complex systems, hybridisation of physical and machine learning, and big data. He takes particular interest in the development of scientific software and reproducible computational research, and in open data initiatives.
