Journal article

A review of Agent-Based Modelling of technology diffusion with special reference to residential energy efficiency

Energy consumption Heating ventilation and air conditioning Lighting technology diffusion agent-based modelling
Description

Highlights

  • Energy efficiency is important for reducing carbon emissions.
  • Policies are adopted to increase residential energy efficiency.
  • Policy formulation in this space can be supported by analytical tools.
  • Traditional equations based diffusion modelling is a limited approach.
  • Agent Based Modelling addresses limitations of previous methods.

Abstract

Residential energy efficiency is an important strategy for reducing greenhouse gas emissions. There are many technologies that help improve residential energy efficiency, and in fact, increased energy efficiency has already helped reduce global greenhouse gas emissions significantly in the past. However, with greater innovation, further improvements can be made and improving energy efficiency is an ongoing activity. Policymakers around the world are putting strategies in place to speed up the adoption of energy efficient technologies and practices, but ultimately this process is based on choice by residents themselves. Human decision making and choice however is a very complex issue, and complex computational tools are required in order to analyse and/or predict the impact of various policies. Traditionally, equation-based models such as Bass and Choice models have been used to describe the diffusion of technologies in a population, but certain limitations have been identified. This article explores what these limitations are in the context of energy efficient residential technologies and how an alternative computational and empirical paradigm, Agent-Based Modelling (ABM), can help resolve some of these limitations. As such, this is a review article into how ABM can support analysis of strategies to catalyse greater uptake of energy efficiency in the residential sector.

 

Publication Details
Volume:
31
Duration:
May 2017
Pagination:
173-182
Publication Year:
2017