Sample representativeness and energy usage variations

The sample selection methodology described in 1.3.1 was chosen with the desire to measure

representative energy consumption for houses before and after changes to the BCA energy efficiency regulations. The extent to which this has been achieved is crucial in understanding the confidence that can be placed in the following energy efficiency data and analyses. Key questions that need to be asked include:

· Are the occupants in the lower and higher star-rating house cohorts sufficiently similar to allow us to expect that energy usage patterns would be the same? Preliminary results in Section 3 suggest a

slight difference in occupancy for lower-rated houses than for higher-rated houses. However, the results from assessments during peak usage periods of the day are consistent with the results reported for seasonal periods. This suggests that occupancy differences are not unduly influencing the seasonal results. However, further research is needed on the effects of star-dependent

occupancy to determine if they affect the star-rating dependence of energy consumption. · Are the houses in the lower and higher star-rating cohorts using the same heating and cooling

appliances? Reverse-cycle air conditioners were the dominant appliance in Brisbane and Adelaide.

In Melbourne, a larger fraction of the higher-rated houses had no cooling. Both Melbourne cohorts used gas for winter heating, with only a small amount of reverse-cycle air conditioning. A small number (~4%) of the lower-rated houses used electric-resistance heating, which was not evident in the higher-rated houses.

· Are the houses in the lower and higher star-rating cohorts the same size? Section 6.8 found that the

house sizes are generally fairly similar, with the exception of a number of very large (>300 m2₎

houses, which are all in the lower star-rating cohort. Inclusion of these larger houses may skew the lower star-rating cohort towards higher energy consumption.

· Is the energy usage behaviour of the occupants in the two cohorts representative of normal behaviour in the broader population? Section 3 found that around 15% of households are retiree

households (either single or couples) and 21% are working couples with no children. However, this study is limited in scope, and as previously described, is not representative of all households in Australia.

These measureable differences may provide some pointers towards possible differences. However, even in an otherwise perfect sample (with no outwardly measureable distinguishing features), the selected houses may still contain a biased group of individual occupants with electricity-using preferences that are not representative of the broader population.

The spread of household energy use by frequency (Figure 8-1) shows that energy consumption exceeds the median by factors of 3% for Brisbane, 15% for Adelaide and 11% for Melbourne. This suggests that energy use, behaviour, house size and household income may be significant factors, and that simple averaging is inappropriate for comparing star ratings.

Figure 8-1 Distribution of daily heating energy for houses in Brisbane in winter

Figure 8-2 Distribution of daily total energy for houses in Brisbane in winter

To further check if the sample looks broadly representative, the average whole-of-house electricity

consumption in the sample houses in each city was compared against the expected electricity consumption for houses in that city (Table 8-1). The expected annual electricity consumption was obtained using the Energy Made Easy website calculator (http://www.energymadeeasy.gov.au/bill-benchmark), using a comparable occupancy profile to the sample.

0.0 2.8 5.6 8.4 11.2 14.1 16.9 More Frequency 3 31 16 5 3 1 0 1 Cumulative % 5.00% 56.67% 83.33% 91.67% 96.67% 98.33% 98.33% 100.00% 0.00% 20.00% 40.00% 60.00% 80.00% 100.00% 120.00% 0 5 10 15 20 25 30 35 Fre qu en cy Energy consumption kWh/d

Brisbane - Distribution of Daily Heating Energy Consumption in

Winter

Frequency Cumulative % 3.4 11.35 19.3 27.24 35.18 43.13 51.07 59.02 More Frequency 1 8 24 21 7 4 0 2 1 Cumulative % 1.47% 13.24% 48.53% 79.41% 89.71% 95.59% 95.59% 98.53% 100.00 0% 20% 40% 60% 80% 100% 120% 0 5 10 15 20 25 30 Fre qu en cy Energy consumption kWh/d

Brisbane - Distribution of Total Electricity Consumption

in Winter

Frequency Cumulative %

Table 8-1 Comparison of electricity consumption in sample houses against average city houses (www.energymadeeasy.gov.au)

Brisbane Adelaide Melbourne

Measured (kWh) 7225±2641 6365±2748 6008±3131

Expected (kWh) 6791 6425 6048

A major bias in the results reported in this section is that the CSIRO data set is extrapolated to one year, from a sample that was taken over nine months. In the near future, we will have a year’s worth of data to make these estimates more accurate. However, 8.1 suggests that (i) annual electricity use in the Adelaide and Melbourne samples is broadly in line with the expected values, and that (ii) annual electricity usage in the Brisbane sample is above the expected values. However, as expected, there is large variability in energy usage between houses, and the ratios of standard deviations to mean values given in Table 8-1 are very close to such ratios measured in much larger studies (statistics for a New South Wales study of house total energy consumption: A. Higgins, CSIRO, personal communication). Average household gas energy

consumption in single dwellings in Melbourne is around 55,000 MJ/yr, whereas the sample of Melbourne houses consumed about 37,600 MJ/yr each. This reduction possibly reflects the earlier introduction of house efficiency regulations, and is consistent with an SPAusnet study of gas usage (Figure 1-1) (Centre for International Economics, 2012).

Figure 8-3 Melbourne average household gas use by year of connection

These issues go some way towards explaining the very large spread of values that may be encountered in a statistical analysis of energy consumption by householders. It is therefore important not to expect that comparisons of simple averages from restricted populations can yield definitive results. For statistically significant conclusions, it is necessary to use parameters that are, as far as possible, independent of human behaviour. We have found that it is also necessary, particularly with heating and cooling energy

consumption, to consider the skewed statistical distribution. The values given in this section are provided in response to the requirement for qualitative and indicative information. For statistically significant results, we advise the reader to use sections 10 and 12, and understand that this only represents a subset of the population of houses and householders.

In document The Evaluation of the 5-Star Energy Efficiency Standard for Residential Buildings (Page 70-73)