Benchmarking and assessing the energy performance of UK non-domestic building stock: Schools, Offices, Prisons and Higher Education Buildings
Dynamic and context driven benchmarking system – availability of large datasets and computational capacity provide opportunities to develop a system that provides energy benchmarks that depict the latest trends of energy performance of the stock and are more relevant to circumstances of individual buildings.
National school stock model based on empirical data – a model that utilises both top-down and bottom-up approaches based on empirical data would be beneficial in improving the design stage prediction of the operational performance of new buildings and assist in developing effective funding strategies for EFA.
Annual cross-sectional and longitudinal analyses of DECs and the energy benchmarks – observe the trends in the energy performance of public sector non-domestic buildings and examine the robustness of CIBSE TM46 benchmarks.
Validation of the energy consumption figures reported in DECs – meter readings from DECC provides opportunities to scrutinise the accuracy of the figures reported by DEC assessors, which is crucial for using the data for developing future benchmarks.
Analysis of the CarbonBuzz database- Insights gained from the analysis of detailed information on end-uses and the equipment would be highly beneficial for evolution of energy benchmarks.
- An analysis of the latest Display Energy Certificates (DEC) showed that majority of energy benchmarks in CIBSE TM46 that underpin the DEC scheme were out of date, suggesting that the benchmarks should be revised.
- Longitudinal analyses of the energy performance of buildings between 2009 and 2011 showed a clear decline in fossil-thermal energy use. These findings highlighted an imperative to develop a long-term strategy to provide robust energy benchmarks.
- A thorough analysis of the classification system of CIBSE TM46 showed numerous issues that hinder the robustness of the scheme. Evidences of misclassification of buildings were raised and empathised a need to revise the classification system.
- Exploration of the correlations between building characteristics (e.g built form, glazing ratio) and the energy performance of schools has shown a potential to improve the comparability of benchmarking by introducing new parameters (e.g. surface to volume ratio) and adopting new methods (e.g. multiple regression analyses).
Impact, Influence & Outreach
Contribution towards revisions of energy benchmarks in CIBSE Guide F and TM46 – Energy benchmarks in both documents have not been updated since their publication and evidence suggests that they are out-of-date, hence requires updating.
- Robertson, C., Mumovic, D., & Hong, S. M. (2015). Crowd-sourced building intelligence: the potential to go beyond existing benchmarks for effective insight, feedback and targeting. Intelligent Buildings International, 7 (2-3), 147-160. doi:10.1080/17508975.2014.987639
- Williams, J., Hong, S., Mumovic, D., & Taylor, I. (2015). Using a unified school database to understand the effect of new school buildings on school performance (submitted). Intelligent Buildings International.
- Hong, S. -. M., Paterson, G., Mumovic, D., & Steadman, P. (2014). Improved benchmarking comparability for energy consumption in schools. Building Research & Information, 42 (1), 47-61. doi:10.1080/09613218.2013.814746
- Burman, E., Mumovic, D., & Kimpian, J. (2014). Towards measurement and verification of energy performance under the framework of the European directive for energy performance of buildings. ENERGY, 77, 153-163. doi:10.1016/j.energy.2014.05.102
- Hong, S. -. M., Paterson, G., Burman, E., Steadman, P., & Mumovic, D. (2013). A comparative study of benchmarking approaches for non-domestic buildings: Part 1 – Top-down approach. International Journal of Sustainable Built Environment, 2 (2), 119-130. doi:10.1016/j.ijsbe.2014.04.001