A probabilistic energy model for non-domestic building sectors applied to analysis of school buildings in greater London

The diversity of non-domestic buildings at urban scale poses a number of difficulties to develop models for large scale analysis of the stock. This research proposes a probabilistic, engineering-based, bottom-up model to address these issues. In a recent study we classified London's non-domestic buildings based on the service they provide, such as offices, retail premise, and schools, and proposed the creation of one probabilistic representational model per building type. This paper investigates techniques for the development of such models. The representational model is a statistical surrogate of a dynamic energy simulation (ES) model. We first identify the main parameters affecting energy consumption in a particular building sector/type by using sampling-based global sensitivity analysis methods, and then generate statistical surrogate models of the dynamic ES model within the dominant model parameters. Given a sample of actual energy consumption for that sector, we use the surrogate model to infer the distribution of model parameters by inverse analysis. The inferred distributions of input parameters are able to quantify the relative benefits of alternative energy saving measures on an entire building sector with requisite quantification of uncertainties. Secondary school buildings are used for illustrating the application of this probabilistic method. © 2012 Elsevier B.V. All rights reserved.

Air quality and climate impacts of alternative bus technologies in Greater London.

The environmental impact of diesel-fueled buses can potentially be reduced by the adoption of alternative propulsion technologies such as lean-burn compressed natural gas (LB-CNG) or hybrid electric buses (HEB), and emissions control strategies such as a continuously regenerating trap (CRT), exhaust gas recirculation (EGR), or selective catalytic reduction with trap (SCRT). This study assessed the environmental costs and benefits of these bus technologies in Greater London relative to the existing fleet and characterized emissions changes due to alternative technologies. We found a >30% increase in CO2 equivalent (CO2e) emissions for CNG buses, a <5% change for exhaust treatment scenarios, and a 13% (90% confidence interval 3.8-20.9%) reduction for HEB relative to baseline CO2e emissions. A multiscale regional chemistry-transport model quantified the impact of alternative bus technologies on air quality, which was then related to premature mortality risk. We found the largest decrease in population exposure (about 83%) to particulate matter (PM2.5) occurred with LB-CNG buses. Monetized environmental and investment costs relative to the baseline gave estimated net present cost of LB-CNG or HEB conversion to be $187 million ($73 million to $301 million) or $36 million ($-25 million to $102 million), respectively, while EGR or SCRT estimated net present costs were $19 million ($7 million to $32 million) or $15 million ($8 million to $23 million), respectively.