Dynamic micro-simulation of domestic electricity consumption: a case of Beijing

Abstract

Agent-based modelling enables the simulation of household energy consumption at the individual level, considering heterogeneity, interactions and dynamics. However, existing agent-based domestic energy consumption models fail to capture the influence of associated urban subsystems (e.g., population and land use). To fill this gap, we develop an empirically based electricity consumption model and integrate it into an urban microsimulation platform, SelfSim. The resulting SelfSim-Energy model simulates the energy consumption of each household in the context of urban evolution, explicitly representing interlinked energy use and urban dynamics. We apply SelfSim-Energy to Beijing, simulating its urban system evolution from 2021 to 2030, with a focus on domestic energy consumption. We find total electricity consumption increases from 29.1 to 32.3 billion kWh mainly due to population growth. Homeownership declines from 82.1% to 56.8%, leading to a reduction in energy-efficient technology adoption in homes from 78.2% to 69.1% for the example of low-energy lighting. These within-household trends help explain the observed increase in domestic energy consumption. We use scenario analysis to show how changes in population structure, land use development, and socio-economic trends interact to influence domestic energy consumption. This emphasizes the importance of incorporating urban dynamics to better represent and estimate future energy demand in the built environment.

Keywords

Energy consumption / Urban micro-simulation / Agent-based modelling / Decision support system / SelfSim

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