AN URBAN PLANNING METHOD FOR MODELLING ENERGY USE WITH APPLICATION TO SELECTED CANADIAN CITIES

This study demonstrates a method of organizing, processing and modelling data for urban residential energy use and applies it to Canadian Plains cities. The method offers explicit control of factors such as housing mix, dwelling age and condition, residential density, travel distance, and climate. These factors are obscured in methods of urban energy analysis, which depend on extrapolation of data from national or regional sources, or data from prototypical dwellings.;An illustrative application of the method for three real cities shows that: (1) as areal residential density increases, energy consumption increases but less linearly; (2) areal residential energy use decreases with distance from the urban centre, at least initially, it may increase later due to concentrations of multiple units in the outer suburbs; (3) specific residential areas are identifiable from their energy use; (4) as residential energy efficiency increases, differences in residential energy use between older and newer areas diminish; and (5) areal residential transport energy use decreases with residential distance from the urban centre. The method offers an analytical and monitoring technique for urban planning which can be periodically reiterated using systematic time-series data to present three-dimensional change in urban energy use. It can also provide a first approximation of expected energy use characteristics for other selected cities and can identify where energy waste may be occurring.;The method disaggregates residential data by urban tracts, compares areal residential energy use in alternative prospectives and investigates residential energy consumption, travel distance from the urban centre, and areal residential density. Aggregated census and public utility data and systematic development of estimated data are used in a procedure which involves: (1) establishment of an urban laboratory of three large Plains cities; (2) determination of residential energy consumption from real and estimated data; (3) simulation of time-related energy objectives using scenarios in a three-dimensional matrix; (4) selection of data for comparison of residential transport energy and internal residential consumption; and (5) generation of three-dimensional representations of areal residential energy use for each selected city.