Constructing an Urban Microsimulation Model to Assess the Influence of Demographics on Heat Consumption

Abstract

We present ongoing work on the construction of a spatial microsimulation model to assess the influence of demographics on residential heat consumption for Hamburg, Germany. Demographics are important for urban energy planning as: (1) Buildings are becoming more energy-efficient and building occupant behaviour accounts for a growing share in the variation of consumption; (2) building occupant needs are changing along with demographic change; and (3) the share of small decentralized district heating grids, in which fewer customers mean less averaging out of heterogeneous occupant profiles, is set to play a bigger role in the country’s heat supply. We construct a spatial microdata set for the city of Hamburg (of roughly 1.8 million inhabitants and 370 000 buildings), with households populating geo-referenced buildings, in three steps: (a) Synthesizing the population of small scale “statistical areas”, comprising up to around 2 000 people (we do this by selecting households recorded in the German microcensus and fitting them into the statistical areas); (b) assigning energy relevant properties to the geo-referenced buildings from the Hamburg digital cadaster (we do this by making use of a well-established building typology developed for energy assessment) and constructing dwelling units in these buildings; and (c) matching households to the dwelling units in these buildings (which we do again by using household data from the microcensus). This last step – allocating households to buildings – may be the most interesting and challenging task. As of to date, we use a combinatorial optimization algorithm to achieve this. Once we have a microsimulation model of buildings and households living in them, including their demographic composition, the range of questions that can be explored is immense. The illustration presented here is a simple heat balance computation of individual buildings, using the constructed socio-demographic data and the digital cadaster data as input parameters.