SPATIAL BUILDING STOCK MODELLING TO ASSESS ENERGY- EFFICIENCY AND RENEWABLE ENERGY IN AN URBAN CONTEXT

Abstract

The building stock has a decisive impact on energy consumption, greenhouse gas (GHG) emissions and sustainable development of urban areas. In previous studies the authors have assessed these impacts by applying a bottom up building stock model (BSM) including construction components and energy systems for different development scenarios. The goal of this work is to present novel improvements of the BSM which include a spatial differentiation of building characteristics, energy infrastructure and local potentials of renewable energy. As such the model is suitable to be used in the context of urban energy planning and to simulate different scenarios, for instance with regard to the feasibility of long term carbon mitigation and (primary) energy consumption reduction goals. To simulate energy demand, carbon emissions and further key indicators of the building stock the bottom-up simulation methodology has been enhanced in terms of spatial differentiation. So far, to deal with the great complexity of building stock modelling, different building types of an urban context have been clustered into cohorts with similar characteristics. As compared to these earlier approaches, we combine specific data known on the level of each individual building with generic data that is known or assumed on the level of building or spatial cohorts. These cohorts are defined by variables such as construction period, building type and utilisation, and building location. However, model calculations are done on the level of specific building archetypes, building technologies, and building components. As such assumptions on technical characteristics and building stock alterations may be brought much closer to the decision processes. This new approach of spatial building stock modelling (SBSM) offers the advantage of being able to include specific, geo-referenced building data (e.g. from surveys or from building registers) and to represent results at the level of details needed. Hence, results may be represented by building type or other building attributes, but also by hectare or neighbourhoods, using geographical information systems (GIS). Scientific results are achieved both with regard to methodology and with regard to content. Methodologically we demonstrate the feasibility of combining data and assumptions on different levels of aggregation such as building elements, individual buildings and cohorts. With regard to content we present results from a case study about feasibility of achieving the goals of the 2000-Watt- and 1-ton-CO2-society in the city of Zurich, referring to spatially differentiated potentials of renewable energy and energy-efficiency.