ACSPRI Conferences, RC33 Eighth International Conference on Social Science Methodology

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An agent-based approach to modeling the impact of policy scenarios on the energy demand of residential and commercial buildings in Australian cities

Kwok Keung Yum, Greg Foliente, Seongwon Seo

Building: Law Building
Room: Breakout 1 - Law Building, Room 024
Date: 2012-07-11 11:00 AM – 12:30 PM
Last modified: 2011-12-22


This paper discusses a method of simulating aggregate and distributional effect of energy policy scenarios, by implementing the provision of the policy on a representative of households and commercial firms, and then summing up the energy demands across the households and firms respectively.
The traditional approach to the assessment of energy demand of buildings is based on end-use analysis of the residential and commercial building sectors (e.g. energy uses in space heating and cooling, water heating, lighting, equipment operation, etc.). Such sectoral breakdowns of end use can deliver a top level view of energy consumption of the sectors, but they may not be suitable for bottom-up policy impact analysis.
We propose to adopt an agent-based approach based on building stock modeling. We need a building stock model for residential buildings, and another, different stock model for commercial buildings. Each building stock model is characterised by properties that related to the following aspects of assessment: (a) building locations, in terms of Local Government Area (LGA) down to Census Collection District (CCD); (b) building characters that can be easily obtained from various surveys (including social and technology adoption surveys) from ABS, ABARES, etc.; (c) building characteristics that are used in building energy simulation programs as inputs (e.g. wall construction, roof construction, etc.); (d) building characteristics that can used in the output analysis.
We shall develop case studies to demonstrate the expressiveness of the agent-based approach in the development of policy scenarios and their assessment.