Year

2021

Degree Name

Doctor of Philosophy

Department

School of Mechanical, Materials, Mechatronic and Biomedical Engineering

Abstract

Energy poverty is prevalent in Australia, and many other countries across the globe, with those people who receive an aged pension or are considered to have a low-income having a higher likelihood of being impacted (Nance, 2013). With an ageing national demographic and rising economic inequality it is expected that energy poverty will continue to grow and impact the lives of many Australians.

Over the past two decades the Australian governments, at National and State levels, have sought to understand the cost effectiveness of various policy options to improve energy efficiency within various sectors including residential buildings. In seeking to improve energy efficiency, various policies and programs have been initiated including the requirement for the Australian Energy Regulator (AER) to produce electricity consumption benchmarks, which are published on the Energy Made Easy website and provided to energy retailers for publishing on electricity bills of residential customers (AEMC, 2020; Australian Energy Regulator, 2012). This measure seeks to increase the awareness of how much energy a household consumes when compared to those in their neighbouring area with the aim of driving energy efficient practices.

The fundamental assumption with some of these programs is that low-income households are consuming more energy than necessary and that with some behaviour change or low-cost building retrofits, their overall energy consumption can be reduced whilst still maintaining quality of life and comfort.

The primary aim of this research was, therefore, to provide a rigorous investigation on the current level of electricity consumption and thermal comfort within the homes of low-income elderly people and identify the main building and occupant characteristics that can be used as predictors for informing future energy programs targeted for this type of demographic. This new evidence base was exploited to develop a series of innovative predictive models of how electricity consumption and thermal comfort are dependent on key household and building indicators, with the aim that the predictive models will be used in the development of future energy efficiency policies and programs.

FoR codes (2020)

330204 Building information modelling and management, 330206 Building science, technologies and systems, 339999 Other built environment and design not elsewhere classified

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Unless otherwise indicated, the views expressed in this thesis are those of the author and do not necessarily represent the views of the University of Wollongong.