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Housing environmental performance: predictive models Housing stock models are used widely to predict energy consumption and

Chapter 8 analyses the different forms of knowledge and skills that are brought together in renovation, and how material components intermediate in

2 The existing residential stock and renovations

2.6 Housing environmental performance: predictive models Housing stock models are used widely to predict energy consumption and

associated emissions based on various building and demographic variables (for example, Firth et al., 2010; Hinnells et al., 2007; Johnston et al., 2005;

Mata et al., 2010; Meijer et al., 2009; Nemry et al., 2010; Peacock et al., 2008;

Shorrock et al., 2005). Swan and Ugursal (2009) identify two distinct methodological approaches for modeling residential sector energy consumption by: a top-down approach utilizes historic aggregate energy values and regresses the energy consumption of the housing stock as a function of top-level variables such as macroeconomic indicators, energy price, and general climate; and a bottom-up approach extrapolates the

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estimated energy consumption based on a representative set of individual houses to regional and national levels, and consists of two distinct

methodologies: the statistical method and the engineering method. Each technique relies on different levels of input information, varies in the

calculation or simulation techniques used, and provides results with different applicability.

A bottom-up end use model was used to estimate energy consumption in the Australian residential sector (Energy Efficient Strategies and Department of the Environment Heritage and the Arts, 2008) employs. This study draws upon available data to establish a profile of housing in Australia to estimate energy consumption at a state level from 1986 to 2005 with projections to 2020. The model separately tracked four main categories of end use: space conditioning, water heating, cooking and appliances. In addition, the four main fuel types of electricity, mains (natural) gas, LPG and wood were also tracked. Attention is given to the interaction of the thermal performance of the building shell, heating and cooling regimes and the product type, fuel mix and energy

efficiency of space heating and cooling equipment together with climate data.

To achieve this, the model uses selected combinations of various dwelling types and construction to match known variants within the stock. Modelling of space conditioning load was conducted on a range of selected sample

dwelling types selected as representative of the building stock as a whole.

This study, which provides one of the most detailed assessments of residential end use energy consumption ever undertaken in Australia, nevertheless

identifies significant gaps in the knowledge base that underpins the estimates in the report, with a need for more end-use data for residential energy use in Australia, including the drivers behind householders’ energy consumption. The main findings of the study are summarised below:

a) A forecast increase in the number of occupied residential households from six million to almost ten million between 1990 and 2020, with rise in residential floor area from 685 to 1,682 million square metres.

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b) The study predicts a 56 per cent increase in residential sector energy consumption over the period 1990 to 2020 under the current trends, with an increased proportion of the total residential energy demand being met by electricity.

c) Projecting forward to 2020, a 6 per cent decline in energy consumption per household compared to 1990 levels is predicted, despite expected increases in service delivery to households, and trends previously outlined in sections 2.5 and 2.6 in house size, space conditioning equipment and appliances, and increase in standby energy

consumption. The forecast decline per household is ascribed to existing and planned energy efficiency programmes.

d) An increase in per capita energy consumption from 17 gigajoules (GJ) per person in 1990 to 20 GJ in 2020, or approximately a 20 per cent increase over the study period, driven partly by a decline in the number of persons per household. Lower than ABARES official data in which residential energy use per person increased from 18.3 GJ in 1989-90 to 19.9 GJ in 2006-07 (Sandu and Petchey, 2009), the divergence is attributed to uncertainties in national energy consumption data based on top-down methodology, and assumptions used.

Benefits of housing stock models include: reducing the requirement for time-consuming measurements for large numbers of dwellings; estimating savings based on current/predicted information; and providing information on hard-to-measure quantities, such as heat loss coefficients or ventilation rates of buildings. Bottom–up engineering models have high requirements for detailed data and computational intensity (Swan and Ugursal, 2009). Where models are used to identify optimum energy and CO2 reduction strategies by

employing a variety of scenarios, and varying the input parameters to the scenarios, it is argued that the model must accurately describe the

complexities of the housing stock including the physical, climatic, and household behavioural aspects, for such results to be of value in policy

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formation. Further, any model must be thoroughly validated against existing data sets and the uncertainties within the model fully quantified. Without rigorous testing, predictions will lack credibility. In particular, as inputs to a housing stock model will be inferred or estimated values (due to the size and complexity of the built environment, the limited data available, and the difficulty in making many of the necessary measurements), the model should clearly demonstrate the effect of the uncertainty in the model inputs on the model predictions (Firth et al., 2010). Although used for scenario planning to estimate the effects of technologies, policies, and future climates on overall energy consumption and CO2 emissions, the ability of models to identify the effectiveness of specific policy measures is regarded as ‘highly limited’ by Summerfield et al. (2009). For ascertaining the effectiveness of renovation as a strategy, it is vital to look beyond such models and to investigate dwellings and occupants.

Renovation, including retrofitting, is widely promoted for reducing energy and associated emissions in residential dwellings, and the next section examines data available on home-renovation to further understand the extent and nature of activities.