Agent-Based Modeling of Energy Technology Adoption

Agent-based modelling (ABM) has emerged as a vital tool for understanding how households, firms and communities adopt energy technologies such as solar photovoltaics, heat pumps and insulation measures. By representing individual decision-makers as autonomous agents endowed with attributes and behavioural rules, ABMs capture heterogeneity in preferences, socio-economic status and social influence. Interactions among agents—through social networks, neighbourhood effects and information exchange—drive the diffusion of innovations in a bottom-up fashion. Unlike optimisation models that assume perfect rationality, ABMs accommodate bounded rationality, peer learning and non-economic motivators such as environmental attitudes and social norms. Spatially explicit ABMs can link demographic data, building characteristics and policy scenarios to explore how targeted incentives, information campaigns or regulatory changes alter adoption pathways. This modelling approach has global significance for designing locally tailored interventions that accelerate low-carbon transitions while accounting for regional diversity in technology costs, social structures and policy frameworks. Concrete applications range from simulating rooftop solar uptake in urban districts to assessing the impact of subsidies on heat pump deployment in rural areas. By integrating behavioural theory, empirical calibration and scenario analysis, ABMs offer policymakers granular insights on when and where to intervene to maximise technology diffusion and achieve climate targets.

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Agent-Based Modeling of Energy Technology Adoption publication trend

The graph below shows the total number of articles in agent-based modeling of energy technology adoption across all publications each year (not limited to Nature Index journals).

Technical terms

Agent-based model (ABM): A simulation framework in which individual entities (“agents”) operate according to defined rules and interact within an environment, yielding emergent system-level behaviour.

Diffusion: The process by which a technology or innovation spreads through a population of agents via social interactions, imitation and learning.

Agent heterogeneity: Variation among agents in attributes such as income, preferences, social connectivity or risk tolerance, which influences decision pathways.

Spatial microsimulation: A method that integrates detailed geographic data with population attributes to enhance spatial resolution in agent-based analyses.

Social norm: Shared expectations about acceptable behaviour within a group, which shape individual adoption decisions beyond economic considerations.

References

1. A comprehensive review of integrating behavioral drivers of technology adoption and energy service use in energy system models. Renewable and Sustainable Energy Reviews (2025).
2. Simulating households' energy transition in Amsterdam: An agent-based modeling approach. Energy Conversion and Management (2023).
3. Analysis of Rooftop Photovoltaics Diffusion in Energy Community Buildings by a Novel GIS- and Agent-Based Modeling Co-Simulation Platform. IEEE Access (2019).
4. Agent-Based Modelling of Urban District Energy System Decarbonisation—A Systematic Literature Review. Energies (2022).