Dryer usage patterns and energy saving potential during normal use. Lloyd Harrington, Energy Efficient Strategies

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1 Dryer usage patterns and energy saving potential during normal use Lloyd Harrington, Energy Efficient Strategies

2 Introduction Clothes dryers have had mandatory energy labelling in Australia since 1989 Australia is looking to adopt IEC61121 as the test method for clothes dryers so a round robin of four laboratories was conducted in 2016 Very little normal usage data has been collected in Australia end use measurement data for 28 homes has been collated and reappraised with respect to frequency of use and load size Energy savings from the replacement of conventional dryers with heat pump dryers have been assessed and are very impressive

3 Background Dryer ownership in Australia is about 55% and this value has been steady for many years Air drying using clothes lines is common in Australia Dryer usage is fairly low in most households, but usage levels vary widely by house New label was introduced in 2016, which acknowledges heat pump dryers as super efficient

4 Laboratory round robin IEC61121 AS/NZS is based on US AHAM A197.6 (HLD-1) Australia and NZ are looking to adopt IEC61121 for mandatory labelling A robin of 2 dryers in 4 test laboratories was used to assess the method and give laboratories experience in the test method A full report has been released (see paper references) Includes 14 recommendations to the IEC

5 Laboratory round robin IEC61121 Key recommendations include: Improved energy correction based on measured initial and final moisture content (current linear approach is flawed) Improvement instrument specifications Improved method for preparing loads using the bone dry method Removal of requirement for a series of 5 tests Tests also showed that age load (which is closely controlled in the standard) does not appear to have any significant impact on dryer performance, so could be relaxed

6 Typical dryer performance curve

7 Modelled dryer performance

8 Modelled with real data

9 Analysis of field data In order to analyse field data, it was necessary to develop an estimate of the load size as a function of the measured energy consumption for each load Round robin data and routine testing by Choice (Australian Consumer s Association) enabled a function to be developed This is partly dependent on the spin performance of the clothes washer in each house, which was estimated based on stock average by washer age and type load load Load dried kg (Mod1) = E E + Loadrated Erated Erated

10 Load as a function of measured energy

11 Analysis of field data Energy data loggers were installed in 24 houses over the past 5 years for various projects Data covered 4101 days and 2805 separate dryer cycles Average cycles per day was 0.9, ranging from 0.13 to 3.23 (note this is an average across houses = non-linear) Average load for all houses was 1.54 kg of dry clothes Average dryer capacity was 5.0 kg Average loading level for all dryers was 31% Household with the heaviest average loading was 61% of rated capacity, one household was 7% This shows that average usage levels are very much less than rated capacity

12 Load distribution for one house (7kg dryer)

13 Analysis of field data While dryer energy does scale with load size to a fair extent, the relative efficiency does decline for small loads Almost no houses loaded their dryers in excess of half the rated capacity on a routine basis This may be cultural and, to some extent, limited to Australia, where outdoor line drying is quite prevalent However, these loading levels broadly match a wide range of independent data (3 studies) that suggest that washer loads are on average around 3 kg (irrespective of rated capacity of the machine) and that many loads are line dried This suggests we need to rethink how we test and rate dryers testing at rated capacity is of limited value

14 Measured annual energy 12 sites

15 Retrofit Program Sustainability Victoria undertook a program of replacing convention dryers with heat pump dryers in 4 households Heavier dryer users were targeted Existing dryers were measured for 6 weeks, dryers were replaced and new dryers measured for 8 weeks Most of the monitoring was in the cooler winter months, so higher usage is expected (c.f. summer) Data loggers recorded energy data each 1 min Temperature and humidity data was recorded each 10 min

16 Normalised energy savings House ID Ref loads/ week Ref load size kg Energy before kwh/y Energy after kwh/y Energy savings kwh/y Energy savings kwh/y CD % CD % CD , % CD , ,112 71% Average %

17 Retrofit Program These households appeared to maintain consistent behaviour and use with the old and new dryer There did not appear to be any significant rebound (some variation in loading and frequency, but complicated by changes in capacity and seasons) Note that the program targeted heavy users Annual energy savings were estimated using a standardised seasonal profile based on before/after data Energy savings averaged 69%, despite variations in loading and usage (normalised profiles) Analysis of humidity showed that all dryers increased room humidity by around 0.5 g/kg dry air when operating (less than 0.7K increase in dew point), which is modest - including condensing types

18 Conclusions Heat pump dryers can achieve substantial energy savings when replacing conventional dryers However, the energy saved depends on frequency of use and loading levels Loading levels for most households are very low, which suggests we need to reappraise how these appliances are tested and rated The IEC standard, while robust in many respects, needs to be more versatile to allow energy to be estimated for very small loads that are typical during normal use without unduly increasing the test burden IEC corrections for initial and final moisture content could be improved

19 Acknowledgements Details of the dryer retrofit project are contained in the report by Sustainability Victoria titled Clothes Dryer Retrofit Trial available from SV website (see paper refs) The author was commissioned by Sustainability Victoria to undertake detailed analysis of their data and assist in the report preparation Additional data was collected and analysed by the author