###Why don’t EPC predictions agree with metered energy consumption?
Project ID: 2228bd1185 (You will need this ID for your application)
Research Theme: Energy and Decarbonisation
UCL Lead department: Bartlett School of Environment, Energy and Resources
Lead Supervisor: Tadj Oreszczyn
Project Summary:
EPCs are a cornerstone of domestic energy demand policy. However, recent work identifies a systematic over-prediction by the EPC model, with an increasing discrepancy in lower efficiency bands (bands D-G), suggesting that many homes could be in the wrong EPC band. This potentially affects their value, potential to access grants for energy efficiency improvements (such as the new ECO+ scheme), and fuel poverty classification (A-C homes cannot be classed as in fuel poverty). Moreover, the implications for the current government ambition that all homes are upgraded to EPC-C by 2035 are currently unclear. It is therefore imperative that reasons for the discrepancy are understood, and improvements made in a timely manner. Moreover, the current government attention via the EPC Action Plan means this an opportune moment to pursue this research. This PhD would seek to identify causes of the discrepancy and identify practical improvements to the EPC process. Depending on the strengths and interests of the candidate, the research could be purely technical or include sociotechnical aspects. Technical work could explore the effect of the use of default values in the EPC model, the breakdown of energy demand for different uses including heating, hot water, and appliance use, or quantification of the impact of mis-rating homes. Moreover, the process of rating a home is complex sociotechnical phenomenon in which the EPC assessors and the customers play a key (but often overlooked) role. A sociotechnical dimension could seek to understand how EPC assessors use the assessment methodology in practice and help to identify steps for improving the practical implementation of EPC assessment process. The analysis would align with ongoing research interests within the Smart Energy Research Lab (SERL) team and would tap into SERL Observatory dataset – an innovative dataset which includes linked smart meter, EPC, sociotechnical survey, and weather data.