A building energy demand and urban land surface model. (6th September 2018)

• Record Type: Journal Article
• Authors: Lipson, Mathew J.; Thatcher, Marcus; Hart, Melissa A.; Pitman, Andrew
• Is Part Of: Quarterly journal of the Royal Meteorological Society. Volume 144:Number 714(2018)
• Page Start: 1572
• Page End: 1590
• Publication Date: 2018-09-06
• Subjects: anthropogenic waste heat -- building energy demand -- urban meteorology; Meteorology -- Periodicals; 551.5
• DOI: 10.1002/qj.3317
• Languages: English
• ISSNs: 0035-9009

Abstract

Abstract : Cities are unique environments where anthropogenic waste heat from energy consumption changes the dynamics of the boundary layer, affecting temperatures, pollution dispersion and buoyancy‐driven flows. Although urban environments are important for societal wellbeing, there are relatively few models that link predictions of waste heat variability with urban climate interactions. This study presents the Urban Climate and Energy Model (UCLEM): a new physically based model representing important heat transfer processes between the atmosphere, external and internal urban environments, combined with a statistical model of human behaviours relating to energy use. The aim of UCLEM is to efficiently predict the climatology of different urban areas, as well as the energy used to maintain comfortable temperatures within buildings. The model is designed to be easily adaptable to a wide range of urban settings with adjustable parameters including building height, density, material thermal and radiative characteristics and vegetation fractions. We assess UCLEM's ability to predict energy consumption for a neighbourhood of Melbourne, Australia, forced by local flux tower observations and evaluated at half‐hourly intervals over 12 months. Results are presented in four development stages to assess various levels of physical and behavioural model complexity. We show that more complete physical representations can improve average daily energy consumption predictions; however, sub‐daily patterns of energy use are improved only by combining the physics‐based model with a statistical model of human behaviour. At the final stage, as well as predicting surface–atmosphere radiant and turbulent fluxes, UCLEM estimates neighbourhood energy demand with a normalised mean error of 11.5% and a computation time on a single processor of about 1 s per simulation year. Abstract : Weather conditions affect the energy consumption of buildings, and energy consumption in turn can change the weather of cities via waste heat emissions. To capture interactions between the two we have developed a physically based urban climate and building energy model together with a statistical model of human behaviour. UCLEM can accurately and efficiently predict diurnal and seasonal variability of building energy consumption at neighbourhood scales, and can represent urban land surfaces in regional climate simulations