Electrical Engineering and Systems Science > Systems and Control
[Submitted on 4 Sep 2026]
Title:AgentHomeID - Agent-based modelling of building stock transformation: A multi-scale framework for policy assessment and infrastructure planning
View PDF HTML (experimental)Abstract:Decarbonising the building sector is central to meeting climate targets, yet existing models rarely capture the interaction between system-level transformation dynamics and heterogeneous individual investment decisions. This work presents AgentHomeID, an agent-based model of building stock evolution in which owner behaviour, techno-economic constraints, and regulatory frameworks are represented explicitly at the level of individual buildings and their owners. The model differentiates owner-occupiers, private landlords, and institutional owners, using willingness-to-pay (WTP) parameters estimated from empirical decision-maker studies, and operates on both representative building archetypes and real building data derived from geographic information systems (GIS). We demonstrate this versatility across three applications. At national scale, scenario analysis for Germany to 2045 shows that removing binding renewable heating requirements substantially raises final energy demand even where envelope refurbishment is unchanged, and that subsidy allocation and investment activity diverge sharply across owner types and income quartiles, with the lowest quartiles persistently underinvesting. At regional scale, bottom-up simulation for a German distribution grid planning region yields spatially concentrated heat pump uptake at NUTS-3 level that differs from aggregated top-down projections in both magnitude and spatial distribution. At urban block level, the same simulations resolve substation-level load heterogeneity and show that integrated system peaks driven by heat pumps, electric vehicles, and photovoltaics do not coincide with individual technology peaks. Across all three scales, owner heterogeneity and local structure materially shape transition pathways, indicating that they should be represented explicitly in models used for policy assessment and infrastructure planning.
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Facts Only
* AgentHomeID is an agent-based model for building stock transformation.
* The model explicitly represents owner behavior, techno-economic constraints, and regulatory frameworks.
* Three owner categories are modeled: owner-occupiers, private landlords, and institutional owners.
* Willingness-to-pay (WTP) parameters are estimated from empirical decision-maker studies.
* The model utilizes representative building archetypes and real building data from geographic information systems (GIS).
* National-scale scenario analysis was conducted for Germany with a timeline to 2045.
* Regional-scale simulation was conducted for a German distribution grid planning region at the NUTS-3 level.
* Urban block-level simulations analyzed substation-level load heterogeneity.
* The model examines the integration of heat pumps, electric vehicles, and photovoltaics.
* Analysis shows lowest income quartiles persistently underinvest in building transformations.
Executive Summary
Decarbonizing the building sector requires a transition from aggregated top-down projections to granular, agent-based modeling that accounts for individual decision-making. AgentHomeID addresses this by simulating the interactions between regulatory frameworks and the heterogeneous behaviors of owner-occupiers, private landlords, and institutional owners. By utilizing empirical willingness-to-pay data and GIS-derived building information, the framework identifies critical gaps in transition pathways, specifically noting that the lowest income quartiles consistently underinvest regardless of general policy trends.
The application of this model across national, regional, and urban scales reveals significant divergences in energy demand and infrastructure load. National projections for Germany indicate that removing renewable heating requirements increases final energy demand. At the regional and urban levels, the spatial distribution of heat pump uptake and the resulting electrical grid peaks differ from aggregated estimates, as integrated system peaks for heat pumps, electric vehicles, and photovoltaics do not coincide. These findings suggest that local structure and owner diversity are primary drivers of the energy transition.
Full Take
This scholarship employs a multi-scale agent-based modeling (ABM) approach to bridge the gap between macro-policy goals and micro-economic realities. The methodology is robust in its integration of GIS data and empirical behavioral parameters (WTP), moving beyond the "rational actor" fallacy often found in energy modeling. A peer reviewer would likely examine the sensitivity of the WTP parameters—specifically how stable these empirical estimates remain across different economic cycles—and whether the "representative archetypes" sufficiently capture the extreme outliers that often drive grid instability.
The claims are generally proportionate to the evidence provided. The finding that the lowest income quartiles underinvest is a critical observation that transforms a technical engineering problem into a socio-economic one. This extends existing knowledge by demonstrating that "top-down" policy targets may be mathematically achievable on paper but socially impossible without targeted intervention. The novelty is justified because it resolves the "spatial mismatch" between where policy assumes technology will be adopted and where the physical grid can actually support it.
For these findings to matter outside the research context, policymakers must shift from blanket subsidies to stratified support systems that account for owner type and income. The real-world implication is a necessary move toward "precision policy."
Bridge Questions: How would the model react to a sudden volatility in energy prices or a shift in interest rates? Would the integration of rental-cost pass-through mechanisms for landlords significantly alter the underinvestment pattern of low-income tenants?
The content is a technical contribution to systems science and does not match the structural patterns of an influence campaign.