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Modelling school building stocks in England and Wales to develop integrated school building assessment framework
The UK government sets the target to reduce CO2 emissions by at least 80% by 2050, compared to 1990 level. Most energy is consumed by existing buildings due to high volume and outdated construction technologies. It is widely-recognized that energy use in existing buildings can be reduced significantly through energy-efficient measures. However, they are not always good because some of them have negative impact on indoor environmental quality and therefore human thermal comfort level. For the propose of energy efficiency and sustainability, we face trade-off and raise questions in regard to achieving the balance of the energy consumption reduction and indoor environment improvement before the refurbishment interventions. There is also a need to better understand what future effects energy retrofitting efforts might have, and building stock modelling is a key phase to identify retrofit options. This research will focus on school building stocks because (1) There is no building stock model specialized in school buildings across the UK and even the world by now. (2) Approximately 10 million pupils spend almost 30% of their life in schools, but knowledge of the interactions between their health and cognitive performance and indoor environment conditions in classrooms is still limited. Hence, the main aim of the research is to develop a methodology enabling characterization of energy use and thermal environment in school building stocks.
Energy epidemiological approach will be applied in order to describe present and future energy use (final energy demand and CO2 emissions) and indoor thermal environment (Indoor temperature and ventilation rate) of school building stocks in England and Wales. About 22000 schools will be modelled single by single in dynamic simulation software - Energyplus. Crucial inputs of these school buildings will be automatically generated in Simstock and their models will be simulated in parallel by using HPC (high performance computing). The simulation outputs are expected to identify energy use patterns of buildings across the population and students’ health and cognitive performance as a function of indoor temperature and air quality. We will also explore the applicability of crowdsourcing platform to reduce the uncertainty in energy and environmental building performance simulation. The model will finally be available to compare various building-related efficiency interventions as well as guide policy development.