Complementary use of DCM and microcomponent ...

Complementary use of DCM and microcomponent records for domestic water demand forecasting Patrick Sim, Adrian McDonald, John Parsons, Philip Rees (School of Geography, University of Leeds)

Introduction There are two leading sources of domestic water consumption data; the first is micro-component based and the other is domestic consumption monitoring (DCM).

Micro-component measurements have the

advantage of simplicity and breakdown consumption into components of specific water use (e.g. toilet flushing). DCM based analysis has the potential to model demand by estimating the total consumption for a particular household profile (e.g. occupants, no. of bathrooms etc.). A challenge of the research is to employ the strengths of both approaches to enable more descriptive and robust demand forecasting models for UK regions into the medium term.

Microcomponent Strengths The major strength of micro components is the breakdown of water consumption into idealized components (e.g. bathing, washing, toilet flushing etc.). This reductionist approach aids deterministic modeling of consumption components. If the survey is representative and/or the resulting water-use components properly balanced such that it reflects the demographic average it can be applied to the larger region to calculate the proportions of water used by the larger populace for specific purposes.

Microcomponent Limitations The idealized microcomponents are best applied to fairly large and demographically representative groupings of people. However there is usually little detail as to the demographic profile that the ‘typical’ quantities of microcomponents are based on. Thus microcomponents may deviate significantly from reality when applied to areas which differ demographically. It should be emphasized that microcomponents are idealized quantities for the averaged population or housing sample.

The quantities generally do not

specifically correspond to any occupancy and/or house type combination. Though microcomponents are sometimes presented as per-capita figures because of the non-linear relationship between consumption and occupancy the act of doubling the figures does not yield the average component consumption of a two person home. Variations also exist between microcomponent surveys, the quantities maybe small on a domestic scale however these may have large supply balance implications. With regard to new developments in the South-East this is problematic as these environs are expected to have smaller than average household sizes (and differing household appliances and, perhaps, lifestyles from that of the established population). In particular the lack of socio-economic context prevents comparison

between two areas. This distortion tends to grow when applied to smaller scales, where deviation from demographic norms becomes statistically more likely. Due to the effort and expense of carrying out microcomponent surveys they tend to be carried out on fairly small samples of housing (a few hundred sometimes, but usually less). As the sample is small it maybe subject to local bias effects if conducted on one local site, this can introduce errors if used for modeling demand in another region. Significant consumption variations exist between national regions {OFWAT, 2005} and this is a barrier to a general microcomponent model. As microcomponents collection is fairly elaborate there is a significant probability that the household occupants will be subject to monitoring based bias {UKWIR, 1999}; in particular the Hawthorne effect caused by a subject’s awareness of the monitoring regime.

The ‘clean-ness’ of microcomponent consumption figures also obscures other aspects of demand. These figures do not indicate the scale or nature of ‘natural’ variability in household demand. Moreover, extreme usage is unlikely to be exhibited by a participant of a microcomponent survey as participant selectionmotivation and the Hawthorne effect generally influence consumption such that microcomponent surveys record moderated habits. Also microcomponent surveys do not indicate the effect of metering on optant households; this is partly because of the small size of the survey.

DCM Strengths DCM surveys tend to have the benefit of size and long duration. Though they ideally should be developed to be cross-sectionally representative of the wider region the large number of households involved (typically many hundred) mean that analysis can select groups and types of housing to better achieve a desired representation. Most DCM surveys adopt a panel survey approach involving the participant household completing an annual questionnaire. This annual data enables demand to be analysed with demographic, socio-economic and household information, some which are known to have an influence on demand {Achttienribbe, 1993}{Wijst, 1999}. The detail in the surveys can be very fruitful, most surveys collect data about house type, appliance, occupant and water use information. This allows consumption to be correlated with household attributes. Moreover, some DCMs contain a metered cohort or enabling a monitor of households converting to metering (and thus leaving the survey). As the consumption is recorded from a single meter or logging devices for the whole household the survey process is less obtrusive than microcomponent surveys and thus participant bias is reduced. A consequence is that DCM’s exhibit far greater variation in observed consumption and also cases of extreme water usage – known to exist in the real world, but difficult to record and elucidate. What is more the panel-survey type approach may give some clues as to the likely cause of extreme and non-typical consumption.

DCM Weaknesses Issues relating to the administration of DCM’s generally affect their accuracy. For example questionnaires are usually completed in a roughly annual periodicity, though changes occurring in-between questionnaires are only picked up at the next. Although in the past there has been no uniform policy for the construction and administration of DCM, each water company applying its own set of questions and set of managerial rules more recent DCM surveys demonstrate better practice. The range of variation of household consumption can be so great that error removal is important. Though bias is expected to be reduced in a DCM it cannot be completely factored out {UKWIR, 1995}. The contact between the water company and the household, the means of data collection and the selection process all contribute to the potential of socio-economic and Hawthorne effect bias.

Approach It is intended that both DCM and microcomponent data be used in a complimentary and advantageous manner. To do so involves recognizing the inherent strength of each. In the case of microcomponents, their forte is a robust top-down breakdown of water use. Their use will be most beneficial when analyzing the consumption of small sites where accurate information is available regarding the number of occupants and specification of water use appliances. The knowledge of average microcomponent volumes allows a ‘bottom-up’ approach of resolving total demand by summing component estimates (e.g. washing, toilet use). In contrast, DCM surveys provide insights into temporal, socio-economic and regional variations (this project is fortunate to have access to DCM surveys from a number of UK water companies). This information is best used when estimating demand at a large scale using a ‘top-down’ approach by summing up specific house type demand averages multiplied by numbers of housing. With the multivariate approach required to make long term scenario based forecasts domestic demand will need to be resolved into microcomponents to facilitate behavioral understanding. However the drivers of the microcomponent change require an understanding of natural variation, appliance penetration, metering trends and socio-economic influence which exist in DCM records.

References Achtienribbe, G.E. (1993), Household Water Consumption in the Netherlands, Aqua 24(6), p347-350 OFWAT (2005), Security of Supply, Leakage and the Efficient Use of Water 2004-05 UKWIR (1995), Demand Forecasting Methodology UKWIR (1999), Best Practice for Unmeasured Per Capita Consumption Monitors Wijst, M.A.J.E, Groot-Marcus, J.P. (1999), Consumption and Domestic Waste Water Demographic Factors and Developments in Society, Water Science and Technology, 39(5), p41-47