Agent-Based Modelling Key To Energy Demand

This graphic is from the Nature Climate Change paper and shows the common elements of an agent-based model (ABM). Three essential conceptual steps of an ABM exercise are depicted, using the household adoption of an alternative energy technology (such as solar PV) as an example. a, A modeller will specify the general factors that drive the decisions and behaviour, which may be derived from complementary or competing theories of human behaviour. For instance, a theory of rational choice might emphasize the importance of economic costs and benefits to adoption whereas a theory of social influence will emphasize the importance of having other social contacts who have adopted. b, A modeller will specify a specific decision rule, such as the probability of adoption of agent i at time t (Pi,t) specified in the equation. Variables E(i )and N(i,t) represent the ith agent’s economic benefit of adoption and the proportion of social contacts who have adopted before time t, respectively. A model parameter (a) controls the relative importance of economic versus social influence factors. c, Varying model parameter a yields different emergent outcomes — in this case different adoption curves, which describe the saturation of the technology in the system over time. Courtesy: authors and Nature Climate Change.This graphic is from the Nature Climate Change paper and shows the common elements of an agent-based model (ABM). Three essential conceptual steps of an ABM exercise are depicted, using the household adoption of an alternative energy technology (such as solar PV) as an example. a, A modeller will specify the general factors that drive the decisions and behaviour, which may be derived from complementary or competing theories of human behaviour. For instance, a theory of rational choice might emphasize the importance of economic costs and benefits to adoption whereas a theory of social influence will emphasize the importance of having other social contacts who have adopted. b, A modeller will specify a specific decision rule, such as the probability of adoption of agent i at time t (Pi,t) specified in the equation. Variables E(i )and N(i,t) represent the ith agent’s economic benefit of adoption and the proportion of social contacts who have adopted before time t, respectively. A model parameter (a) controls the relative importance of economic versus social influence factors. c, Varying model parameter a yields different emergent outcomes — in this case different adoption curves, which describe the saturation of the technology in the system over time. Courtesy: authors and Nature Climate Change.

Climate change mitigation strategies require a rigorous understanding of the factors that drive energy demand and agent-based modelling is a powerful tool that helps to do that, according to a paper published today (9 May 2016).

An agent-based model (ABM) is a computer model for simulating the actions and interactions of autonomous agents (both individual and organizations or groups) to assess their effects on the system as a whole. The authors of this paper, published in Nature Climate Change, argue that an ABM approach better reflects the real world because it allows for a detailed representation of complex agent systems, including the behaviour of agents, their social interactions and the physical and economic environments surrounding them.

Authors Varun Rai and Adam Douglas Henry point out that a crucial aspect of ABM is that decisions are endogenous to the agent and that there is no central ‘control lever’ governing agent behaviours other than the simple decision rules they are programmed with.

The paper, entitled “Agent-based modelling of consumer energy choices”, concludes that “ABM is a promising approach for helping to build better theories and models of energy demand, the understanding and prediction of which is important for addressing climate change”.

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 speci c ways in which ABM can improve understand- ing of both fundamental scienti c 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.

Citation

Varun Rai and Adam Douglas Henry; Agent-based modelling of consumer energy choices; Nature Climate Change, DOI: 10.1038/nclimate2967.

Source

Nature Climate Change.

Be the first to comment on "Agent-Based Modelling Key To Energy Demand"

Leave a comment

Your email address will not be published.


*