Can a simulation-based non-domestic stock model be developed to perform scenario analyses for new technologies and policies?
This project will create a simulation-based model of the non-domestic building stock for use in estimating the energy impacts of regulatory and technological change scenarios.
This project will create a simulation-based model of the non-domestic building stock, starting with the London borough of Camden as a case study. Two complementary modeling approaches are being pursued in parallel: the first uses incremental improvements to existing techniques involving EnergyPlus models of archetype buildings; the second is a more innovative experimental approach using automated generation of EnergyPlus models for the entire stock. For the first approach, a set of representative prototype or archetype models will be produced, with weighting factors based on numbers of buildings or floor areas assigned to them to represent the stock. The resulting model of the stock will allow users to investigate the potential impact of very detailed changes to different types of buildings, e.g. the introduction of completely new types of heating and cooling systems or the extensive use of fixed or automated exterior shades. For the second approach, a tool is being constructed that will automatically generate EnergyPlus models for each ‘self-contained unit’ (SCU) in the non-domestic buildings stock, based on publicly-available information for each building and an empirically-based probabilistic mapping from known to unknown parameters to fill in the remaining information needed to construct the model. The resulting stock model will be calibrated and varified at the most granular level for which energy data is publicly available (presently at the subsector level for most of the stock, but at the building level in some cases, and hopefully at a more granular level in the near future). The result is a virtual population of buildings represented as EnergyPlus models. The number of building models is much too large for exhaustive simulation. However, we may take the perspective of a social science researcher performing a longitudinal study of a large population: the EnergyPlus representations of buildings take the traditional place of individual humans, but the sampling and analysis methods (including confidence interval quantification) are otherwise similar. This model will allow for highly detailed analysis of technology dissemination and regulation changes.
Principal Investigator Brian Coffey