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Agent-based modelling of consumer energy choices

Abstract

Strategies to mitigate global climate change should be grounded in a rigorous understanding of energy systems, particularly the factors that drive energy demand. Agent-based modelling (ABM) is a powerful tool for representing the complexities of energy demand, such as social interactions and spatial constraints. Unlike other approaches for modelling energy demand, ABM is not limited to studying perfectly rational agents or to abstracting micro details into system-level equations. Instead, ABM provides the ability to represent behaviours of energy consumers — such as individual households — using a range of theories, and to examine how the interaction of heterogeneous agents at the micro-level produces macro outcomes of importance to the global climate, such as the adoption of low-carbon behaviours and technologies over space and time. We provide an overview of ABM work in the area of consumer energy choices, with a focus on identifying specific ways in which ABM can improve understanding of both fundamental scientific and applied aspects of the demand side of energy to aid the design of better policies and programmes. Future research needs for improving the practice of ABM to better understand energy demand are also discussed.

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Figure 1: Common elements of an ABM.

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Acknowledgements

V.R. acknowledges support from the US Department of Energy under its Solar Energy Evolution and Diffusion Studies (SEEDS) programme within the SunShot Initiative (award no. DE-EE0006129) and from The University of Texas at Austin's Summer Research Assignment. A.D.H. also acknowledges support from the US Department of Energy SEEDS programme (award no. DE-AC36-08GO28308), as well as from the University of Arizona's Institute of the Environment.

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V.R. and A.D.H. conceived, designed and co-wrote the paper.

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Correspondence to Varun Rai.

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The authors declare no competing financial interests.

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Rai, V., Henry, A. Agent-based modelling of consumer energy choices. Nature Clim Change 6, 556–562 (2016). https://doi.org/10.1038/nclimate2967

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