Standard UKWIR Technical Report Format

WRMP19 METHODS – HOUSEHOLD CONSUMPTION FORECASTING SUPPORTING GUIDANCE

Report Ref. No. 15/WR/02/9

Programme Area & Reference

Water Resources: Supply WR/02

Report Title

WRMP19 Methods: Household Consumption Forecasting - Supporting Guidance

Project Management

Neil Whiter, on behalf of UKWIR

Collaborator

Natural Resources Wales

Contractor

Artesia Consulting Ltd.

Sub-Contractor

Waterforte Consulting Ltd.

Author of Report

Critchley, R Lawson, R Marshallsay, D

Period Covered

2014 - 2015

(Waterforte) (Artesia) (Artesia)

UK Water Industry Research Limited provides a framework for a common research programme to undertake projects, which are considered to be fundamental to water operators on ‘one voice’ issues. Its contributors are the water and sewerage companies and the water supply companies of England and Wales, Scottish Water and Northern Ireland Water. UKWIR Report Ref No. 15/WR/02/9

All statements contained in this document are made without responsibility on the part of UK Water Industry Research Limited and its Contractors, and are not to be relied upon as statements or representations of facts; and UK Water Industry Research Limited does not make or give, nor has any person authority on its behalf to make or give, any representation or warranty whatever in relation to the contents of this document or any associated software.

Published by UK Water Industry Research Limited 8th Floor, 50 Broadway, London SW1H 0RG First published 2015 ISBN 1 84057 805 X  UK Water Industry Research Limited 2015 No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise, without the prior written consent of UK Water Industry Research Limited. Printed by Webree.com Ltd.

UKWIR Report Ref No. 15/WR/02/9

UK WATER INDUSTRY RESEARCH LIMITED WRMP19 METHODS: HOUSEHOLD CONSUMPTION FORECASTING SUPPORTING GUIDANCE Executive Summary Objectives Understanding the household demand for water is fundamental to ensuring the resilience of future water resources systems. Guidance for previous water resources management plans (WRMPs) recommended using micro-component forecasting, however this method is regarded by some as too data intensive and complex. The preparation for the 2019 WRMPs provides an opportunity to reconsider demand forecast methods to see if a more cost effective approach can be developed that provides results with the appropriate level of confidence for all companies and stakeholders. The objectives of this Supporting Guidance are to: 

Review the household demand forecasting methods currently in use;



Identify methods not currently in use which could potentially provide a new approach to demand forecasting; and



Examine the relative merits of each method and present evidence to support the development of a manual of household consumption forecasting (separate report).

Conclusions The review of water company practices in household consumption forecasting found that: 

All companies used micro-component analysis in their 2014 WRMPs;



Many companies would like the option to use a method that is less complicated, particularly for water resource zones that have minimal risk of a supply demand deficit;



Concern was expressed that there is a large amount of uncertainty in the forecasting data for micro-components analysis and a lot of judgement calls are required; and



However, many companies value the benefits of transparency of approach and explicit account of assumed changes in behaviour provided by micro-components analysis.

Eight agreed requirements for household consumption forecasts have been identified, and used to evaluate the relative merits of alternative forecasting methods. The study initially identified 13 factors which could influence household consumption trends. This has been rationalised to five factors through the project. All 13 factors are presented here and the justification for the five that have been used in the final manual are presented.


A wide range of potential options for household consumption forecasting were identified from a literature review and experiences in other industries and countries. These included: a range of statistically-based trend models, micro-simulation or agent-based modelling, variable flow methods, macro- or micro-component analysis, or combinations of methods. The study has identified the importance of other other elements of household consumption forecasting that should also be addresesd by the manual. For example appropropriate choice of: customer segmentation, accounting for yearly variations in weather method and uncertainty analysis.

Recommendation That the evidence presented in this report be used to support the identification and description of good practice methods in the manual of household consumption forecasting.

Benefits This report provides evidence that enables demand forecasters to develop household consumptions forecasts for their 2019 WRMPs with improved robustness and greater confidence. The findings in this report support the development of the manual (separate report) that will enable water companies to identify good practice demand forecasting methods that are most suited to their specific situation and guide them in their correct application.

For further information please contact UK Water Industry Research Limited, Floor, 50 Broadway, London, SW1H 0RG quoting the report reference number

8th


Contents 1

2

3

Introduction

1

1.1 1.2

1 2

5

2

2.1 2.2 2.3 2.4

2 3 3 4

What is a ‘good’ forecast? What is household consumption? Why do we forecast household consumption? What happens if your forecast is wrong?

Review of methods used in WRMP14

4

The basis of the current micro-component approach A review of current UK practices in forecasting household demand Estimating base year micro-components Micro-component forecasting Estimating and forecasting occupancy forecast by segment Final planning forecasts Normal year / dry year / critical period uplift Uncertainty assessment approach A summary of practice Observations from the stakeholder review

4 6 7 8 9 9 10 10 13 14

Requirements of a forecast

15

4.1

16

Assessment criteria

Factors affecting household consumption models and forecasts 18 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 5.11 5.12 5.13 5.14

6

Background Purpose and structure of this report

Understanding household consumption forecasting

3.1 3.2 3.2.2 3.2.3 3.2.4 3.2.5 3.2.6 3.2.7 3.2.9 3.2.10

4

Page Number

Occupancy Age of occupants Affluence Socio-demographic customer segmentation Property type Cultural and life-style change Practices of water use Behaviours Weather Climate change Metering Technology Regulation Summary

18 19 19 19 20 20 21 22 23 23 24 24 25 26

Methods for estimating future household water consumption

27

6.1 6.2 6.3

27 28 28

Introduction Use existing study data Per-capita methods


6.4 6.5 6.6 6.7 6.8 6.9 6.10 6.11 6.12 6.13 6.14

7

Trend based models Econometric Regression models Micro-component or end-use models Aggregated micro (or macro) components Variable flow methods Micro-simulation Agent-based modeling Backcasting Hybrid approaches Novel approaches

References

Appendix 1 Questions used in practitioner interviews

29 29 30 31 32 33 34 35 37 37 38

39 43


Glossary This glossary provides explanation of some of the terms used in this report. Definitions for further demand forecasting and water resources planning terms are recorded in the UKWIR “WR27” report “Water Resources Planning Tools: Definitions” (UKWIR, 2012). Term / Acronym

Definition

ACORN

A socio-economic classification developed by CACI Limited that is widely used in the Water Industry, stands for ‘A Classification Of Residential Neighbourhoods’.

CC

Climate Change

Correlation

The description of the strength of the linear relationship between different data sets.

DMA

District metered area. A defined area of the distribution system that can be isolated by valves and for which the quantities of water entering and leaving can be metered.

Dry year annual A year in which rainfall is low but demand is unrestricted (see below). average (DYAA) This is a scenario used in water resource management plans. Dry year annual The level of water demand, which is just equal to the maximum annual unrestricted average, which can be met at any time during the year without the daily demand introduction of demand restrictions. This should be based on a continuation of current demand management policies. The dry year demand should be expressed as the total demand in the year divided by the number of days in the year. Dry year critical The period during which water resource zone supply-demand balances period (DYCP) are at their lowest. Critical period does not necessarily occur in the period of peak demand. Final planning The scenario of total water available for use and final planning demand scenario forecast, which constitute the company’s best estimate for planning purposes, and which is consistent with the information provided to the regulators for Periodic Reviews. Hindcasting

The use of a model to predict what happened during past episodes.

Household consumption

The water delivered that is assumed to be within the control of the customer i.e. excluding supply pipe losses. Household consumption = use + plumbing losses.

Household demand

Household consumption multiplied by population (for PCC) or properties (for PHC).


Macrocomponent

A method of consumption forecasting based on the grouping of microcomponents or end-uses into similar categories according to the main factor that influences consumption.

Microcomponent

A sub-component of household water consumption, for example water used by toilet flushing, showers, baths, washing machines, dishwashers, or external water use (including garden watering).

MOSAIC

A socio-economic classification developed by Experian that is widely used in the Water Industry.

Multiple linear A linear regression derived for more than one explanatory variable. See regression also simple linear regression. MTP

Market Transformation Programme, an initiative developed by Defra for a wide range of products, including water-using products. Contains forecasts of how the market for water-using products will develop under certain future policy scenarios. The last MTP update was in 2011, so these forecasts should now be treated with caution.

Normal year A scenario considering a year with normal or average weather patterns, annual average divided by the number of days in the year. Normal year The total water demand in a year with normal or average weather annual average patterns, divided by the number of days in the year. demand (NYAA) PCC

Per capita consumption, usually expressed as litres per head per day (l/hd/d).

PHC

Per household consumption, usually expressed as litres per household per day (l/hhd/d).

Sensitivity testing

A process of evaluating the impact of choice of assumptions on outcomes. For example, sensitivity testing of a model would involve evaluating the impact of varying model parameters or model structure on model outputs.

Simple linear A linear regression is a model with a single explanatory variable. regression Stochastic process

A process incorporating an element of randomness, the evolution of which can only be predicted within a range of values of the uncertain variables.

WR27

UKWIR project reports “Water Resources Planning Tools” Ref 12/WR/27/6 (2012).


WRMP

Water Resources Management Plan, which is a comprehensive plan of future actions to maintain adequate water supply reliability. All water companies have to produce a WRMP every 5 years.

WRPG

Guidance document “Water Resources Planning Guideline” produced by Environment Agency, Ofwat, Defra and the Welsh Government. It was last published in November 2012, and will be updated for WRMP19. The latest version will provide the context for the manual. It provides guidance on how water companies should prepare their Water Resources Management Plans.

WRZ

Water Resource Zone defined as “the largest possible zone in which all resources, including external transfers, can be shared and hence the zone in which all customers experience the same risk of supply failure from a resource shortfall”.



1

Introduction

1.1 Background Understanding the household consumption of water is fundamental to ensuring the resilience of future water resources systems. Guidance for the most recent round of water resources management plans (WRMP14) states that: “The use of micro-components should be used to justify a water company‘s current and forecast household consumption. Micro-components can be used to build bottom up forecasts as well as to check on a top down forecast.”1 This approach has been effective over the last 10-15 years, although it is regarded by some as too data intensive. WRMP19 provides an opportunity to reconsider household consumption forecasting methods to see if an approach (or approaches) exist which provide results with the appropriate level of confidence for all companies and stakeholders and is/are costeffective. This project has been commissioned to: 1. Review the household demand forecasting methods currently in use and to examine the pros and cons of each. 2. To identify methods not currently in use which could potentially provide a new approach to demand forecasting. 3. To assess whether the additional resources required by more in-depth forecasting methodologies result in better forecasts. 4. To recommend an appropriate forecasting methodology for WRMP 2019, and include a guide to producing such a forecast that can be followed by companies in the production of future forecasts. 5. To provide answers to a number of specific technical questions:

1



What level of uncertainty is present in the demand forecasting methods used in WRMP14, and how should uncertainty in forecasts be treated in the future?



Could improvements to the existing methodology be made to improve accuracy and cost?



What demand forecasting methods are appropriate for use in both WRMP19 and subsequent plans?



How do companies undertake the appropriate forecasting methods for inclusion in WRMPs?

Environment Agency et al (2012) Water Resources Planning Guideline – June 2012 pp80-81

1


1.2 Purpose and structure of this report This Supporting Guidance has been prepared following practitioner interviews with water company demand forecasters and members of the Environment Agency demand team. Section 2 describes how these interviews were carried out and summarises their findings in the context of the guidance and methods for household consumption forecasting used in WRMP14. The consultation undertaken during the interviews with practitioners has been useful in setting the context for the rest of this project and has prompted further consideration of a range of issues, including:

2



The requirements for household consumption forecasts, as presented in section 3;



The factors which could influence household consumption trends (section 4); and



The potential methods for forecasting household consumption (section 5).

Understanding household consumption forecasting

2.1 What is a ‘good’ forecast? A simple definition of a forecast is “a statement of what is expected to happen in the future”; and forecasting usually requires the “study and analysis of available pertinent data”. Forecasting in general is a challenging endeavour. This is true of household consumption forecasting for the reasons outlined in this section; set out in later sections of this supporting guidance; and presented in the accompanying manual. There is no single ‘best’ way of forecasting household demand – this will depend on your specific situation, the data you have available, and the time and money available to invest in the process. This should be proportionate. Within this context, proportionality is critical, as described by the UKWIR (1995) report “Demand Forecasting Methodology”: “There is no absolute level of accuracy that is appropriate in all demand forecasting circumstances. Instead, the level of forecast detail should be sensitive to its purpose. The cost of improving the demand forecast should always be balanced against the additional benefits derived from the greater accuracy.” “The most appropriate method for forecasting [unmeasured] household consumption should be determined by the motivation behind the forecast.” This is still valid: there should be no “one size fits all’ method for forecasting household consumption. The answer to the question “what is a good forecast?” is therefore “one that is proportionate”.

2


2.2 What is household consumption? Household consumption is defined as customer use plus plumbing losses (UKWIR, 1995), analysed at a ‘per capita’ or ‘per household’ level. It is typically measured as litres per person per day (l/hd/d) or litres per household per day (l/hhd/d). The term “household demand” is used in this report to refer to the quantity of water consumed by a group of households: it is calculated as the product of consumption and population or properties, and is typically measured in megalitres per day (Ml/d).

2.3 Why do we forecast household consumption? Water companies have a statutory duty to produce WRMPs. These plans require 25-year forecasts of future supply and demand to be made, so that companies can maintain reliable, sustainable and affordable water supplies. Forecasting household consumption is a particular challenge because the total water use by household customers depends on the behaviour, attitudes and practices of many individuals. This in turn is driven by: 

Social norms and expectations;



Meter status and water charging tariff;



Perceptions and beliefs about water (based on upbringing, religion, environmental awareness and family habits, amongst other things);



Habitual daily routines, or ‘practices’ (and how these change – e.g. in the short-term due to holiday periods, and over the long-term due to changing household composition – from single people to growing families, teenagers, retired people etc.)



Water using technology and household infrastructure (from plumbing and water heating systems to water using devices);



The weather; and



Interactions within and between households.

These factors have driven major change in household water use – such as the increased frequency of personal washing, from infrequent (e.g. weekly) baths to daily showers over the last thirty years or so. Research by Pullinger et al (2013) shows that 70% of the population now have a full body wash at least daily, mostly by showering – and over 50% no longer take a bath. Changes like this are possible over the period covered by a water resources plan, and the forecasts need to recognise this. Water companies are required to predict how these drivers will affect household water consumption. This is clearly a challenge that needs careful consideration and good guidance.

3


Step 1 of the manual (separate report) provides guidance on selecting the forecasting method(s) that is most appropriate for a particular water resources situation, applying the ‘proportionality’ principle.

2.4 What happens if your forecast is wrong? The UKWIR Demand Forecasting Methodology (1995) states that: “Whatever demand forecasting methodologies are used, it is highly unlikely that actual demand will turn out exactly as predicted. The performance of historic demand forecasts should therefore be systematically monitored against actual out-turn figures for the years concerned.” Forecasts will rarely predict the actual outcome accurately. They are a planning tool that enables organisations to make decisions on future courses of action. This should be dealt with in two ways:

3



Represent uncertainty in your forecast. This can be done in several ways as described in the Step 8 of the manual.



Regularly review and update your forecast using recent actual water use data. This should be done every five years as part of the WRMP.

Review of methods used in WRMP14

3.1 The basis of the current micro-component approach Current practice in forecasting household consumption is based on the micro-component approach. It is useful to review the basis for this approach. Herrington (1996) was the first widely-read UK publication to develop the concept of using the micro-components of demand to forecast water consumption. This report identified three fundamental approaches to demand forecasting: extrapolation; analysis combined with judgement; and survey methods. Herrington (1996) suggested that only the last two methods are appropriate for household demand forecasting, and quickly dismisses extrapolation, primarily on the basis that “there is no good reason to assume that past trends will continue into the future (especially in the medium to long-term future)”. The majority of the sections on ‘analysis’ in Herrington’s report explore the approaches to forecasting using micro-components. The report states that “A detailed picture of past, present and future domestic use may be constructed with the aid of knowledge and assumptions concerning ownership of appliances, frequency of use of appliances or habits, volumes of water used and household occupancy rates”, and indicates that “this sort of exercise make[s] for more rational and more thoughtful demand forecasting”. Later (p33) Herrington (1996) states that the forecasts derived are based on “copious helpings of judgement”, informed by numerous literature sources, and laid out in transparent detail. The implication from Herrington seems to be that: 

Micro-component analysis will require the use of assumptions and judgement;

4




This is acceptable as long as these assumptions are based on evidence (e.g. from literature) and are transparent; and



This approach enables forecasters to ‘show their workings’ and present the basis for their demand forecasts to others (e.g. regulators).

As it is now nearly 20 years on from this first piece of work on micro-components, it is interesting to review how Herrington’s forecasts compare to the actual data. Figure 1 illustrates Herrington’s actual and predicted demand, split by micro-component in the bars for the years 1976-77, 1991-92, 2001-02, 2010-11, and 2019-20; Herrington (1996). Average PCC for all customers, unmeasured customers and measured customers are shown in black, orange and green respectively. These data are sourced from: 1961-92 Herrington (1996), 1992-99 Ofwat (1999), 2000-2010 Ofwat Annual level of service and leakage annual reports, 2011 Environment Agency. Figure 1 also shows actual micro-component usage from published IdentiFlow data for unmeasured households for 2002-03 and 2011-12 from WRc (2005 & 2012). Figure 1 illustrates that Herrington’s forecast was accurate for unmeasured and total average PCC (both as reported, and based on IdentiFlow data) for 2001-02 – i.e. around ten years out from the original work conducted in the early 1990s. Herrington’s 2010-11 forecast also compares reasonably well with reported unmeasured PCC – over-estimating by only 5-6 litres per head per day, or around four percent. However this forecast is high when compared to actual total average and measured PCC for 2010-11, and the IdentiFlow data for 2011-12. One potential reason for this could be that Herrington did not account sufficiently for the effect of increased meter penetration on total average PCC. Figure 1 Comparison of Herrington (1996) forecast and actual England and Wales per capita consumption data

5


The later section in Herrington on micro-component modelling under climate change scenarios focuses on how a changing climate may affect demand in the ‘six summer months’, focusing on showering, lawn sprinkling and other garden use only. Herrington (1996) is regarded in the industry as a seminal work, and certainly is academically sound. It is useful to re-read it to understand the basis upon which it promotes microcomponents and recognise its limitations and requirements – including transparent supporting evidence for the numbers used, which he provides. The review presented here provides the context for the review of current practice, which is described in the following sections.

3.2

A review of current UK practices in forecasting household demand

3.2.1 Methodology A review of water industry practice was undertaken principally via semi-structured telephone interviews with demand forecasting practitioners, using a pro-forma which is included in Appendix 1. Four face to face interviews were also completed with organisations which the project team considered would yield additional insights. These were also conducted using the pro-forma as a guide to the interview. Details of the interviews are presented in Table 1. Table 1 Practioner Interview Details Organisation Affinity Water Anglian Water

Bristol Water Cambridge Water Dee Valley Water Dŵr Cymru - Welsh Water Essex and Suffolk Water Northern Ireland Water Northumbrian Water Portsmouth Water Scottish Water Sembcorp Bournemouth Water Severn Trent Water

Interviewees Interviewer Sarah Clark & Katherine Rob Lawson Ward Sarah Castelvecchi, Karen Richard Critchley McDougal, Steve Moncaster & Doug Spencer Martin Berry Rob Lawson Steve Colella (views are as expressed for South Staffs Water) Chris Smith & Kate Powell Richard Critchley Andy Blackhall & Lee Rob Lawson Brown Liz Wright Ania Bujnowicz

Date / Method 18/12/2014 Telephone

Alan Crilly

Richard Critchley

09/01/2015 Telephone

Liz Wright

Ania Bujnowicz


Paul Sansby Elaine Hutchinson & Simon Fuller Greg Pienaar

Rob Lawson Rob Lawson

07/01/2015 Telephone 13/01/2015 Telephone

Richard Critchley


Ismail Mulla

Rob Lawson


6

09/01/2015 Telephone 28/01/2015 Meeting

23/01/2015 Telephone 07/01/2015 (Nil return) 08/01/2015 Telephone 19/01/2015 Telephone 07/01/2015 Telephone


South East Water

South Staffs Water South West Water Southern Water Sutton and East Surrey Water Thames Water United Utilities Wessex Water Yorkshire Water Environment Agency

Andy Ball, Helen Chapman & Gemma Frisby Steve Colella Paul Merchant Faisal Butt Lester Sonden

Rob Lawson

26/01/2015 Meeting

Rob Lawson Rob Lawson Richard Critchley Richard Critchley

16/12/2014 Telephone 06/01/2015 Telephone 15/01/2015 Telephone 16/01/2015 Telephone

Ross Henderson & Brad Howe John Birkhead Julie Morton Clare Dunlop Angela Wallis

Rob Lawson

28/01/2015 Meeting

Richard Critchley Rob Lawson Richard Critchley Rob Lawson and Richard Critchley

08/01/2015 Telephone 23/02/2015 Meeting 13/01/2015 Telephone 15/01/2015 Meeting

The following sections summarise the findings from the discussion held with practitioners about their household consumption forecasting methods. Selected anonymous quotes are presented at the end of the section to provide a balanced summary of the discussions.

3.2.2 Estimating base year micro-components Many companies begin by using measured and unmeasured PCC figures as per Annual Return (AR) for a recent complete year (e.g. 2010/11 or 2011/12 for WRMP14) as the base year. All but one company used customer survey data to estimate the ownership and frequency of use of water using devices. Companies also used these surveys to collect occupancy data. Companies aimed to generate a representative sample of households (which reflected the total population of their household customers), based on a range of factors including meter status, socio-economic status (e.g. ACORN or MOSAIC), property type and property rateable value. Several companies were concerned about the representativeness of the survey samples, because respondents may have been self-selecting or biased to certain cohorts of the population (such as older age groups and lower occupancy households). Nearly all commissioned specific surveys from market research companies, whilst one water only company effectively ‘bought’ a number of questions on wider market survey questionnaires. Cost is an issue. Several companies recognised that surveys needed to be repeated regularly and consistently. Some were concerned that the results of these surveys – which provide important baseline information – will vary because of the different survey methods, questions and answer options employed. Survey data is one source of base year micro-component information. Different companies take different approaches to this. Companies also used different approaches to reconcile the PCC derived from base year microcomponent estimates with the reported base year PCC. The approach to apportioning the

7


residual from this calculation (e.g. residual goes to miscellaneous) also varied from company to company. Data on volume per use was generally sourced from the Defra Market Transformation Programme (MTP) project (for example Defra, 2011), or from Identiflow data (WRc 2005) to populate base year micro-component model. Companies recognised that both these data sources are now quite old and likely to be out of date. Other companies assessed some base year micro-components using expert judgement, and again recognised the potential for significant variation in consumption that could result from this approach. Companies often collected data for slightly different micro-components than those listed in the WRPG (EA et al, 2012), but all companies used the Environment Agency standard microcomponents for reporting. This is another source of potential variability between companies.

3.2.3 Micro-component forecasting The WRPG (Environment Agency et al, 2012) recommended the use of micro-components, therefore all companies used micro-components for demand forecasting in their 2014 WRMPs. However, there was a clear difference in the approaches used by companies with water resource constraints (who adopted relatively advanced approaches) and those with surplus (who used relatively basic approaches). This is appropriate and follows the proportionate approach set out in the current WRPG guidance. All companies reported that the main challenge for micro-component forecasting was the lack of data or information on which they could base their predictions. The MTP reports contain forecasts of appliance ownership, split by volume categories (e.g. toilets with different cistern sizes/flush methods, shower types and flow rates) and so was used extensively by water companies, especially to derive volume per use and lifespans. However it is recognised that this data is now becoming outdated. All companies agreed that a lot of judgement was required in the forecasting process. Practitioners had to determine whether the forecast “looks right”. This required consideration of influencing factors, such as occupancy, lifestyle, time spent at home, weather (peak summer) and the effect of behaviour on discretionary use. Companies all reported that micro-component forecasts were easier for relatively predictable devices and non-discretionary activities such as toilet flushing, but much harder for personal washing components, where there is more discretionary use and behaviour is a much larger factor. This led to several companies suggesting forecasts for toilets and other relatively predictable components could be aggregated, avoiding the need to explicitly model ownership, frequency of use and volume per use. The factors that influence consumption for these ‘aggregated components’ is discussed in section 4. There was variable application of new homes PCC guidance. Some companies assumed that PCC in new homes would be 125 l/h/d, based on current Building Regulations, whilst others adjusted this figure based on data collected for specific surveys.

8


A handful of companies compared micro-component predictions against actual demand in previous years. One company also developed a trend-based forecast alongside their microcomponent forecast to provide some reassurance that their modelling was reasonable. All companies segmented their properties/populations by meter status, with most splitting metered customers down into sub-groups such as optants, new properties, and properties who will be metered by compulsory or change or occupier metering programmes. There was some segmentation by property type and/or ACORN/MOSAIC, but most companies relied on the survey sample composition used to derive base year values to provide this representative coverage. Metered and unmeasured population and property numbers changed over the planning period to reflect company metering forecasts, whilst other segments mostly remained static. Judgements about how micro-components will vary over time have to be made for each segment of the population that is modelled. Some companies chose not to model each detailed segment: Most companies’ forecasts of household consumption show average PCC falling over the planning period. Most companies stated that this downward trend in PCC resulted from genuine factors such as increased metering, more water-efficient new houses and increased uptake of water efficient technology. This is in-line with the expectations set out in the Guiding Principles of the Water Resources Planning Guidelines.

3.2.4 Estimating and forecasting occupancy forecast by segment Estimating occupancy is recognised as a major challenge by practitioners. It is not measured routinely, so companies estimate occupancy for the households in their areas based on survey data. It is a dynamic variable in time and can only be estimated based on separate population and property forecasts. These forecasts are outside the scope of this study – but can be challenging in themselves, particularly when separate forecasts are required for customer segments such as new properties, meter optants and unmeasured customers. Companies use a mixture of (often limited) data to determine base year occupancy, and then judgement and assumptions to estimate how occupancy will change over time for each segment they model. Many companies highlighted the challenges of estimating and forecasting household occupancy, and suggested it would be simpler to use per household consumption (PHC) instead of PCC as the forecasting unit. This is definitely worth considering further.

3.2.5 Final planning forecasts Nearly all companies accounted for water efficiency schemes in their final plans via a volume adjustment, rather than by modelling their effects at the micro-component level. Companies also recognised that there were a lot of assumptions relating to the level of baseline water efficiency and what impact this would have going forward. 9


3.2.6 Normal year / dry year / critical period uplift A wide range of different approaches were used to calculate normal year, dry year and critical period uplifts. The methods used include: weather-demand models; comparison of summer and winter consumption; trend analysis of consumption; or a variety of simpler methods. The choice of method tends to be determined by the available data and the vulnerability of the WRZ to supply-demand deficit. Companies recognise that the lack of strong guidance in this area is an issue and leads to uncertainty from the start of the analysis. 1995 and 2003 were common peak demand years. But a key issue now is the difference in the behaviour of customers 20 years on, especially with respect to metering. Question is how large volumes of metered customers respond to peak periods – almost certainly different to 1995. It was noted that there is limited guidance on good practice for calculating normal year and dry year uplift factors. This provides companies with the flexibility to choose an approach that best suits their data and conditions.

3.2.7 Uncertainty assessment approach Uncertainty assessment is key to maintaining resilience, drought planning, and mitigating investment and wider business risks. Uncertainty is currently taken account of in different aspects of water resources and drought planning, such as headroom. There was broad agreement that using a single figure for the longer range forecast of consumption was unrealistic, especially given the level of judgement required to establish this single number. It was also widely stated that it is hard to forecast the ownership, frequency of use and volume per use of micro-components far into the future. Interestingly though, several companies highlighted that population uncertainty risk is generally greater than PCC uncertainty (though the relative size of these risks vary over the planning period). Most companies prefer the specifics of deterministic forecasts rather than stochastic, but recognise that there is a challenge of defending the single-line values and historically based nature of deterministic forecasts. Some companies like the idea of stochastic approach but are cautious because at present it has not been tested or applied in a water resources plan. Nearly all companies used the UKWIR (2002) numerical approach to determine a target headroom value which includes an allowance for demand forecast uncertainty. This uncertainty was generally a plus or minus percentage on total household demand (in megalitres per day) which also accounted for the uncertainty in population forecasts. Some companies used micro-components to estimate the uncertainty but the distributions of the demand forecast uncertainty were largely judgement based. Only one company in England and Wales used the old target headroom method (UKWIR, 1998). Companies recognised that uncertainty needs to be accounted for more explicitly in the household consumption forecast. Options for quantifying the extent of uncertainty include: 

Stochastic (e.g. using probability density functions) 10




Deterministic ranges



Scenarios

3.2.8 Example water company quotes Topic area

Relevant quotes

Why was MCA chosen?

“Chose this method because it was the method that the EA instructed in the WRPG” “Chose MCA because this was the guidance” “They did not consider any alternative to MCA because EA required use of MCA”

MCA And data/judgement

“Main challenge was lack of data.” “MCA was too data intensive for our needs” “MCA involves too much judgement” “Main challenge was the lack of data to inform forecasts” “Data challenges could be addressed through research” “Lot[s] of judgement needed” “Key challenges in microcomponent modelling was data” “The main challenges were associated with data” “MCA is reasonable if data is there” “Very data intensive” “Too much judgement involved” “Much of the base data for micro-components is now old” “Too many judgements – there are a variety of data sources to choose between and it is difficult to know which is most reliable” “MCA may have a useful role in higher risk WRZs but needs better and more consistent data.” “Concerned that current data (e.g. MTP) is too old for future plans.”

Dealing with uncertainty

“Alternative methods could go back to a trend based forecast with uncertainty analysis – [but] then which one of those spreading lines would you choose?” “Once you start forecasting pcc out 25 years it’s going to be a very uncertain business. Even starting with what is a normal year.” “[You need to] balance big picture uncertainties against the need to predict toilet flushes in 25 years’ time.” “Everything about m/comps is uncertain – how will OVF vary according to lifestyle – e.g. will more people join gyms and shower there instead of at home?” “The problems with MCA include…very uncertain data for future O, F and V values particularly F..”

11


“[We] are very concerned about the large uncertainties/inaccuracies in MCA forecasts.” “All methods need to incorporate assumptions and have uncertainties. They are transparent in MCA.” “The uncertainty in MCA needs to be strongly recognised.” In support of MCA

“But there is nothing underpinning [trend-based forecasts] – which is where the comfort blanket of m/comps comes in.” “Nothing fundamentally wrong with m/comps. Would be better if there was some flexibility on its application.” “Haven’t looked at alternatives. Whatever is used needs to provide clarity – m/comps does this.” “MCA advantages are that it takes account of behavioural and technological changes and enables flexibility in judgement choice of assumptions.” “MCA is good at evaluating change going forward. For comparing between now and the future it is as good as any method.” “Any new method would not have a track record to prove it and [I] would need a lot of persuading that it was a better approach.” “[We] expect to stick with MCA in higher risk WRZs as unlikely that anything better will be available. However, are interested in using alternatives to test the forecasts.”

Forecasting microcomponents

“Fairly comfortable with m/comps but can’t go on slicing it down to smaller and smaller pieces. Do we need to talk about plumbing losses and other factors to the nth degree?” “M/comps useful for analysing benefits of water efficiency, but over designed for forecasting – too complex – e.g. how many times someone is going to flush the loo in 40 years time!” “Forecasting using m/comps is not appropriate.” “Forecasting detailed variables such as showering volumes is the big challenge.” “Specification of m/comp shower volumes to a single number in 25 years is totally unrealistic. A range would be better.” “Quite like m/comps – it is logical and it gives you confidence in the forecast.” “It is a lot of effort, and forecasts are based on national averages (e.g. MTP).” “[We] like MCA as a method as it is good for showing the change over time and the assumptions can be adjusted or tested to ensure forecast is reasonable.” “[There is a] wide range of MCA forecasts at micro-component level between water companies.”

What would you have done differently

“A much simpler trend-based approach would have been adequate.” “Alternative methods could go back to a trend based forecast.”

12


“Given the uncertainties and the surpluses, use of a simple trend-based PCC forecast would be adequate (with testing of assumptions and checking robustness of supply-demand balances).” “Would consider going back to previous trend methods – forecasts will always be wrong, it’s about the level of risk you’re willing to accept.” “Is it worth all that effort, when trend based model gave a reasonable long term forecast?” “Alternative approach: engineering judgement? But this leaves you open to challenge.” Alternative MCA approaches and data improvements

“M/comps should be used to model specific things like CC or behaviour change, can be used to compare details.” “Maybe use m/comps for next 5-10 years and trend based after that.” “Maybe use m/comps for short term only.” “Don’t want a black box. If something different was chosen it would be good to compare it to a parallel m/comp forecast to check differences in results are down to method.” “Perhaps a reduced m/comps. Macro-components – e.g. without OFV – assume minor macro-comps remain constant e.g. cooking, drinking, focus on toilets, showers, outdoor use.” “Would it be possible to rule out some of the non-varying variables?” “More standardisation and collaboration to collect data on pcc in socioeconomic categories or UKWIR behavioural groups. Then take account of local issues on top of that.” “An industry wide microcomponent model would be good, with a range of values for O, V, F to choose from.” “Have talked to [a neighbouring company] about joining up demand forecasts – need to be consistent on methods. And also same questions on surveys.” “Would be good to have a range of data sources and be clear on sources and assumptions.” “If industry keeps MCA, it needs better data and more consistency (and working together) across water companies in the component forecasts.”

3.2.9 A summary of practice Figure 2 summarises the methods that most companies used to forecast household consumption in WRMP14. A number of companies also took a ‘top down’ approach, fitting micro-components to the observed base year consumption.

13


Figure 2 Flow chart outlining WRMP14 practice

3.2.10 Observations from the stakeholder review The main observations from the stakeholder review are: i.

Many companies would like the option to use a method that requires less forecasting – e.g. of ownership, frequency of use and volume per use of micro-components, particularly for water resource zones that have minimal risk of a supply demand deficit.

ii.

There were comments that the level of micro-component breakdown (into ownership, frequency of use and volume per use variables) results in a complicated process for 14


forecasting. It was suggested that aggregated micro-components or ‘macrocomponents’ might be a solution for relatively predictable types of consumption (such as toilet use). iii.

However, some companies would like to use a more complex method, especially for high-risk areas.

iv.

Many companies said that they would like to see flexibility in the choice of method, and that selection of the most appropriate method should be based on a set of forecasting requirements or criteria.

v.

Stakeholders would like to see greater confidence in the survey and measured data under-pinning the forecasts.

vi.

Many companies commented that there is a large amount of uncertainty in the forecasting data that is not accounted for explicitly in current approaches.

vii.

Micro-component analysis requires a lot of judgement calls in estimating forecast data.

viii.

The current micro-component forecasting method implies a level of certainty in the forecast of numerous micro-component variables, which is not always justified.

ix.

However, there were many comments that the micro-component approach takes explicit account of assumed changes in behaviour, is very transparent and all assumptions can be clearly stated and audited.

x.

There is a need to understand and improve the most appropriate ways to estimate the uncertainty in the forecasts.

xi.

A number of companies suggested that more could be done to work together and collaborate on collecting and analysing survey and measurement data on water use in households.

xii.

Occupancy was widely recognised as a key factor in household consumption, but is often difficult to estimate or forecast accurately. It was suggested that forecasts might be better done at the property level.

xiii.

Of all the factors associated with household consumption forecasts ‘change in water use behaviours’ was suggested as the factor about which least is known and that causes the most uncertainty.

xiv.

There is currently a lack of guidance on good practice methods for estimating normal year and dry year adjustments.

4

Requirements of a forecast

The Project Steering discussed requirements of household consumption forecasts and concluded that:

15




Micro-component data (collected from customer surveys or as actual consumption data) is important for understanding household consumption now and in the future.



Companies should continue to collect and analyse micro-component data.



Companies should be able to explain how they have arrived at their consumption forecasts (expert judgement/clear assumptions for future use is acceptable), and should be able to justify the forecast by ‘drilling down’ below household consumption.



Evidence to support companies’ forecasts should be based on the key drivers of change in consumption. These drivers may change over time.



Companies should use proportionate methods for forecasting household consumption, and the extent of justification needed to support their forecast should also be proportionate.



It would be useful to be able to present micro component consumption forecasts in a consistent way between companies, though this will be a challenge if companies adopt a wide range of forecasting methods.

4.1 Assessment criteria There is a need for a set of requirements (or criteria) against which various forecasting methods can be assessed and selected. The following are a set of draft requirements for estimating future water household demand. They are based on the ‘Criteria for assessing methods’ contained in the UKWIR/EA Forecasting water demand component – Best practice manual from 1997 (shown in the left hand column of Table 2). In the right hand column is a proposed set of requirements based on the 1997 list, but modified by the project team in light of the stakeholder review, and agreed by the Project Steering Group. Table 2 Criteria for assessing forecasting methods Criteria from the 1997 report

Proposed forecasting requirements

Consistency and transparency of method

Transparency and clarity

Logical/theoretical appeal

Logical/theoretical appeal

The forecast needs to be understood and should be able to be replicated by others.

The forecast should command confidence to practitioners and decision makers; it should address those factors that people believe drive water demand, and it should be relevant to historical trends.

16


Criteria from the 1997 report

Proposed forecasting requirements

Incorporates/explains historical trends

Not included in current criteria.

Explicit treatment of factors that the industry wishes to see identified separately

Explicit treatment of factors and segments of water users

Empirical validation

Empirical validation

It is not considered necessary to incorporate historical trends in the forecasting method. However learning from them is important and is necessary to understand the factors driving demand (see next criteria). Explaining historical trends is one approach to the empirical validation of a method (see next but one criteria).

There are different factors which drive household demand, and different segments of consumers with respect to household water use, and these should be explicitly addressed in the forecast.

A key element of good forecasting practice is to compare outturns with past projections, and seek to learn from the comparison by understanding the differences. Testing the proposed method on past data can be done to validate the method. Two considerations should be tested: How well does the method predict demand? If explanatory factors are used in the forecast; how well have these explanatory factors been predicted? Acceptance by the regulator/industry

Acceptance by the regulator, industry and external stakeholders The forecast should stand up to scrutiny from the regulators, and other external stakeholders, including customers.

Cost/feasibility

Replaced by: Appropriate to the level of risk being addressed The forecast should be appropriate in terms of cost, data requirements, and frequency of updates, for the planning problem being addressed; i.e. the degree of risk of a supply demand deficit. And include the balance between uncertainty and cost/difficulty.

17


Criteria from the 1997 report

Proposed forecasting requirements Explicitly address uncertainty The forecast should recognise that there will be uncertainty around the forecast, and should quantify the level of uncertainty. Be underpinned by valid data The forecast should be based on data that is valid for the water resources situation for the company or zone area under consideration.

5

Factors affecting household consumption models and forecasts

The initial stage of this project identified 13 factors which affect water demand models and forecasts. Some of these factors are well defined and data is available on the relationships, some are well defined but data are sparse, some are poorly defined and data are very sparse. The factors listed below are those that are mentioned in the literature or that have been raised during the industry consultation. Step 3 of the accompanying manual refers to five factors, which are a rationalisation or reorganisation of the original 13 factors presented below. The following sections describe how the factors presented here relate to the five factors presented in Step 3 of the manual.

5.1 Occupancy It has been established (Edwards and Martin, 1995) that occupancy is a key factor in explaining either PHC or PCC, see example box plots below showing data from a company’s consumption monitor. Figure 3 PHC and PCC variation with occupancy

PHC by occupancy PCC by occupancy The challenges in using occupancy as a main factor in a water demand model are that occupancy data at the household level are difficult to acquire with confidence, occupancy itself is dynamic over time, and average occupancy masks differences in water use by

18


individual households and occupants (for example adults, teenagers, and infants; or occupants that are home during the day and those that spend significant parts of the day away from the home). Water companies tend to use average occupancy (for each meter status segment being analysed) as a starting point for water demand forecasts, which are currently built around PCC estimates. Average occupancy is based on population and property projection figures. There is a clear relationship between occupancy and consumption (either PHC or PCC), therefore this is one of the five factors referred to in the consumption forecasting manual.

5.2 Age of occupants The age of household occupants has been shown (Russac et al, 1991) to explain some aspects of water demand, for example between retired single occupants and working single adult occupants. More recently (Pullinger et al, 2013) has shown that the age range of the occupants may be significant in explaining different showering/bathing practices (as illustrated in in Figure 4). Age of occupant data is not readily available and would be expensive to collect. Similar, related data are available from published socio-demographic datasets such as ACORN or MOSAIC. Therefore this is not one of the factors referred to in the consumption forecasting manual.

5.3 Affluence Affluence is often cited as an influencing factor for water consumption, and has been clearly demonstrated in developing countries (Butler and Memon, 2006). The correlation between affluence and water conservation in the UK is less clear. Data on affluence is not readily available and can be difficult/expensive to collect. Published socio-demographic datasets such as ACORN or MOSAIC contain related data. Therefore this is not one of the factors referred to in the consumption forecasting manual.

5.4 Socio-demographic customer segmentation In addition to segmenting consumers into metered and unmetered segments (see section 4.11), some companies’ segment customers by socio-demographic classifications such as ACORN. This is nearly always applied at the coarsest level (e.g. ACORN Category). The purpose of segmentation is to split consumers into segments within which they have similar water use, and between which the consumption is different. Previous studies (Russac et al,1991) have shown that similar demand in different ACORN Categories and that different demands occurred the in the same ACORN Categories. Recent work has investigated the variability of annual average water demand for billed properties by looking at the finest level of ACORN (ACORN Types, of which in the latest classification there are 59 Types for domestic properties). This is possible because of the large sample size (>500,000 properties) with each property defined by ACORN Type. Hierarchical cluster analysis was then carried out to place the properties into five clusters; each with similar 19


water consumption within the cluster, and each cluster statistically different from the others. The results are summarised in Table 3, which identifies how many ACORN Types in each ACORN Category fall in each Water Using Group. The table demonstrates that the coarsest level of ACORN (Categories) is not correlated well to the different segments of water use at the property level (Water Using Groups). Table 3 Water using groups based on Acorn Types

Number of ACORN Types in each Category

Water using groups and level of PHC Alpha

Beta

Gamma

Delta

Epsilon

ACORN Categories

Hi

Hi/med

Med

Lo/med

Lo

1. Affluent achievers

5

3

2

2

1

2. Rising prosperity

1

0

3

1

1

3. Comfortable communities

3

5

2

2

1

4. Financially stretched

2

4

5

3

2

5. Urban adversity

1

4

1

2

3

This analysis demonstrates that household water consumption is influenced sociodemographics. It should be noted that care is required to ensure that if socio-demographic segments are applied, the segments really represent water use well, and water use within segments is similar. Therefore socio-demographics is one of the five factors referred to in the consumption forecasting manual.

5.5 Property type Property type has been demonstrated (Russac et al, 1991) to explain differences in water consumption, for example with higher consumption in detached properties compared with flats. The reasons for the relationship are likely to be a combination of other factors (occupancy, more space for appliances, outside water use, affluence, tenure, etc.). House type is used as an explanatory factor by a number of companies in their existing microcomponent models. Typically property types include: detached, semi-detached, bungalows, terraced, flats (sometimes sub-divided into purpose built and converted flats). Therefore property type is one of the five factors referred to in the consumption forecasting manual.

5.6 Cultural and life-style change Cultural and life-style change in water use plays a large part in long term water trends (ESRC, 2006) and can clearly be seen in the context of bathing habits and the move from one bath a week to a growing portion of the population showering at least once a day. Other examples include: the transition from top-loading washing machines to front-loading washing machines; and the increasing prevalence of automatic dishwashers. Both of these latter examples have

20


probably been driven by convenience, increasing prosperity and less space in the kitchen; but they have led in most cases to a reduction in water use. The change in bathing habits however is generally considered to have been responsible for an increase in water use for personal washing purposes. Most water companies point to this factor being an area of considerable uncertainty in predicting future household water use. There are similarities between culture and lifestyle change factors (this section), practices of water use (section 5.7) and behaviours (section 5.8). For simplicity, a single factor: ‘behaviour’ has been identified for use in the manual. Therefore culture and lifestyle change is not one of the factors referred to in the consumption forecasting manual.

5.7 Practices of water use Recent research (Pullinger et al, 2013) investigated the water related practices of households in southern England and their influence on water consumption, and this has revealed some interesting insights on water consumption. The research takes ‘practices’ as the unit of analysis when exploring water use (rather than attitudes, behaviours or simply ‘litres used’) and highlights how this changed unit of analysis allows for a deeper understanding of the routines and habits of everyday life that lead to domestic water consumption. This is based on understanding what people do, how people do it, and what technologies and infrastructures they use while consuming water; the routines and habits of everyday life that lead to domestic water consumption – washing and personal hygiene, doing the laundry, gardening, cooking etc. This research explores whether ‘average’ water users are similar or dissimilar to one another when looking at how and why they use water in the home, which has implications for the validity of models which assume households with similar PCC will all change in similar ways in future or respond similarly to interventions. There are some interesting findings in the research, which might help in forecasting some of the more complex water uses, like personal washing (bathing and showering); for example, identifying six clusters of washing practices which might be related to age profile of consumers. The research covers all water using practices in the home, and outside. Taking showering/bathing (personal hygiene) as an example; the research identifies that 70% of the population have a full body wash at least daily, mostly by showering – over 50% never have a bath. It is reported that the variant of practice a person follows is only weakly predicted by their socio-demographic characteristics and environmental values, although there is a substantial variation by age, with frequency of showering and bathing being higher on average among younger age groups (indicated in Figure 4). Figure 4 shows some interesting relationships between age and personal washing practices, which might be used to help predict how quantity of water used in the home for personal washing will change in the future.

21


Patterns of Water report

variants of practice at different periods of their lives), or whether it represents generational change, with new variants of washing emerging (e.g. Simple Daily Showering, Out and About Washing, and Attentive Cleaning) and other variants now simply being traces of disappearing practice (e.g. Low Frequency Showering and Bathing).

Figure 4 Variation in percentages showering/bathing cluster membership by age Figure 11 Variation in percentages ofof cluster membership by age n = 1725, weighted by respondent

There are similarities between culture and lifestyle change factors (section 5.6), practices of 5.1.3 Detailed cluster results, and proxies of practice water use (this section) and behaviours (section 5.8). For simplicity, a single factor: six clusters are described in more detail below, drawing on the data from Figure 10 ‘behaviour’ hasThe been identified for use in the manual. above and more detailed analyses available in the Technical Appendix. The descriptions cover the aspects of washing practice which define the clusters (i.e. the respondents’ scores on fouruse cluster dimensions frequency, technology and consumption Therefore practices of the water is not one of–the factorsdiversity, referred to in the outsourcing), other aspects of washing and personal care practice not included in the forecasting manual. cluster definition (such as shaving and tooth brushing practices), and sociodemographic characteristics of the members of the different clusters. We have included some quotes from the interviews to illustrate in more detail possible formations of how personal hygiene practices appear in each cluster.

5.8 Behaviours

A recent UKWIR report (UKWIR, 2014) on understanding customer behaviour for water Simple Daily Showering demand forecasting, a water segmentation model based on five clusters: Practiced by:proposes 39% of population (of SE England) (n = 674, of 1747, weighted) Disengaged, Theory not practice, Contemporary lifestyles, Settled residents and Conscious A summary of the cluster: consumers. By far the largest group, this form of washing is somewhat more homogenous than those of the other washing clusters. This variant involves washing frequently, nearly always at least daily, and mostly without any variation in shower duration or how full the bath is

The report recommends using five customer typologies that are defined by customer attitudes and perceptions, and then linked to behaviours. The behaviour typologies are linked to 42 inferred water consumption and changes to micro-components (based on interviews). It is then recommended that these inferred changes are applied to existing micro-component data held by companies. It is suggested that external or water company policies can result in changes in behaviour, and that repeat surveys are carried out to track trends in these typologies. Further investigation of the validity of such behavioural typologies is currently being undertaken (UKWIR 2016, in preparation). Given the similarities between culture and lifestyle change factors (section 5.6), practices of water use (section 5.7) and behaviours (this section), a single factor: ‘behaviour’ has been identified for use in the manual. Therefore behaviour is one of the five factors referred to in the consumption forecasting manual.

22


5.9 Weather Household consumption has been observed to be dependent on weather (up to a certain point), most notably during the summer period (Herrington, 1996). A number of microcomponent studies (Kowalski and Marshallsay, 2006; UKWIR, 2013; Affinity Water, 2014) have demonstrated that the increase during peak summer periods is mostly contained in the outside use micro-component. Figure 5 (taken from Kowalski and Marshallsay, 2006) shows a two-week period during the summer of 2001 where demand for water correlates well with daily maximum temperature, and it is clear that the demand peak may be largely attributed to increased external consumption. Figure 5 Micro-component variation during peak summer demand

Guidance and methods developed over the past 20 years use specific weather scenarios (e.g. dry year annual average, critical period) to account for the effect of weather on consumption. Future methods may incorporate a full distribution of weather variables (rather than scenarios), and the effect of weather on consumption could be modelled more implicitly (e.g. by varying the micro-components influenced by weather, such as outside use). However, it is likely that the majority of companies will continue to use weather scenarios. Therefore weather is not one of the factors referred to in the consumption forecasting manual.

5.10 Climate change The original study by Herrington (1996) and Downing et al (2003) both investigated the long term impact of climate change on household consumption. Climate change will result in changes in the micro-components which are weather related, in particular external use and personal washing. The 2003 work is still used by most water companies to estimate the effect of climate change on baseline demand. The report provides regional factors over a 25 year period as an adjustment for dry year annual average demand. Further work was done (UKWIR 2013) to provide methods for water companies to estimate the effects of climate change on household consumption, micro-components and non-household demand. This report provides a summary of typical forecast percentage impacts on household demand for Water Framework Directive river basins, national averages (England, Wales, Scotland and Northern Ireland) and a UK average. 23


It is likely that the effects of climate change on household consumption will continue to be analysed using methods similar to those applied in recent water resources plans. Therefore climate change is not one of the factors referred to in the consumption forecasting manual.

5.11 Metering Consumers who pay for water on a metered bill often have lower average per-household consumption than those who pay for water on a bill based on rateable value. The international evidence for the impact on demand from all types of water metering indicates that the demand reduction following metering falls in a range of 5 to 22% (UKWIR, 2008). In the UK, the range of demand savings from metering is generally reported to be in the range of 5% to 15%. More recent studies indicate that the average reduction could be as much as 16% (Southern Water/Southampton University, 2015, unpublished). It is also likely that forecasts of consumption for customers charged on a metered basis will be different from those for unmetered customers. For example, replacement rates for water using devices may be higher in metered households. Water companies are required to forecast metered and unmetered household consumption separately. However, drawing conclusions from the differences between the two segments is difficult due to the nature of the policies which drive metering; free meter options, new properties since 1990, and compulsory metering. For example, it is thought that meter optants (who can make up a significant part of the population) start with a lower consumption before they opt for a meter. It is expected that companies will continue to use meter status as the primary basis for segmenting household customers. Therefore metering is not one of the factors referred to in the consumption forecasting manual.

5.12 Technology Changes in technology over time have a direct impact on water use in the home. Examples include: front loading washing machines, which have reduced the amount of water per load by 76% since 1970 (Waterwise, 2008); dishwashers, which have followed a similar pattern with a 30% reduction in water use per load since 1990; and power showers, which have been attributed to an increase in showering volumes. Technology is also responsible for the development of water saving technologies, such as variable flush devices, spray taps, water efficient showers, etc. There is also the potential for innovative and disruptive technologies to have a significant impact on water use in the home; examples include ultra-low flush WCs and recycling showers. These are very dependent on the receptiveness of the market for their uptake making it difficult to predict their impact on future water use.

24


Technology can also result in increased water consumption. Examples include jet washers2 and semi-rigid paddling/swimming pools. There is a relationship between technology change and long term trends in culture and lifestyle – e.g. increased showering rates has been enabled by plumbing systems that provide a plentiful supply of hot water. Water companies have used forecasts from the Defra market transformation programme dataset3 to predict how changes in technology will influence household water consumption. Market transformation can play an important part in future changes in household water consumption, and it is largely outside of the control of the water company. Market transformation is concerned with changing the buying and selling trends of goods, in order to effect a beneficial change, often following government policy and innovation interventions. It is linked to technological change. In water consumption terms this has seen the adoption by retailers of more water efficient goods, and the introduction of water labelling schemes to encourage consumers to buy more water efficient goods. Therefore behaviour is one of the five factors referred to in the consumption forecasting manual.

5.13 Regulation Two areas of regulation have had a significant impact on water consumption in the home. Firstly, the changes through water bylaws to toilet flush volumes (shown in Figure 6), which limits the maximum flush volume for installed toilets. They also allow the installation of dual flush toilets, and whilst the maximum flush volume for new installations is currently 6 litres per flush; dual flush systems are available from 6 & 4 litre flushes to 4.5 & 2 litre flushes. Figure 6 Change in toilet flush volume regulations over time

Secondly, the Water Industry Act places a responsibility on water companies to promote water efficiency and minimise wastage. During 2010 to 2015, this was augmented by water efficiency targets for companies to reduce water consumption by up to 1 litre per property

2

Jet washer flow rates may be less than that of a hosepipe but their application (e.g. for jet washing a patio) may result in increased water consumption compared to other patio cleaning methods). 3

e.g. http://efficient-products.ghkint.eu/spm/download/document/id/954.pdf

25


per day per year over the five year period. Many companies have maintained this aspiration in after 2015, through their WRMPs. However, regulation tends to drive technology change, and so only has an indirect influence on consumption. Therefore regulation is not one of the factors referred to in the consumption forecasting manual.

5.14 Summary Table 4 summarises key issues for further consideration from the preceding sections. Table 4 Summary of factors that influence household consumption Factor

Summary

Potential links

Data availability

Included in the Step 3 of the manual?

Occupancy

Consumption is proportional to occupancy

Age of occupants, sociodemographics, property type

Customer surveys, population divided by property numbers

Yes

Age of occupants

Age influences water-using habits

Occupancy, sociodemographics, property type

Customer surveys

No

Affluence

Likely to be a lesser factor in the UK

Sociodemographic, property type

Customer survey

No

Sociodemographics

Use of commercially available georeferenced data to identify customers by sociodemographic group

Occupancy, age of occupants, property type

CACI, Experian

Yes

Property type

Property type linked to consumption

Occupancy, sociodemographics

Customer survey, digital mapping, billing database (?)

Yes

Practices of water use, behaviours

Historic trends from customer surveys

No

Cultural and lifestyle change, behaviours

Data not directly available – would require re-analysis

No

Cultural life-style change

Practices water use

and Factor responsible for long-term changes in waterusing habits (e.g. weekly bathing to daily showering) of Analysis to explore the diversity and common variants of water using practice,

26


Factor

Summary

Potential links

in particular, personal cleaning, laundry, and garden watering.

Data availability

Included in the Step 3 of the manual?

of existing survey data

Behaviours

Behaviour is likely to influence discretionary water use

Culture and lifestyle change, practices of water use

Data would need to be collected in new customer surveys

Yes

Weather

Weather affects external use and potentially other discretionary uses

Climate change

Water company consumption data and commercial weather data

No

Climate change

Climate change will influence water consumption over long term

Weather

Standard factors exist (e.g. Downing et al, 2003 and UKWIR, 2012)

No

Metering

Meter status affects total consumption

Company billing database, research (e.g. Southampton University, 2015)

No

Technology

The nature of water using devices will determine water use volumes and/or flow rates

Market transformation, regulation

Customer survey, MTP

Yes

Regulation

Controls on the water using products that can be sold will influence what is used in the home

Technology, market transformation

Government guidance and legislation

No

6

Methods for estimating future household water consumption

6.1 Introduction Limitations on available data, less than perfect knowledge of underlying relationships, and inherent uncertainty about the future make estimating future water demand a challenge. This section discusses a range of methods and approaches that have been used or suggested for estimating the future demand for water. Most of the techniques discussed are forecasting 27


approaches based on a defined formula relating demand to a function of one or more factors, for example: D = f(x), where the demand (D) is a function of a factor (x). Future values of demand (D) are conditional on future or forecast values of (x), as defined by the function ‘f(x)’. In reality the picture is more complex; there are frequently a range of factors, which are often difficult to define and the true nature of the dependencies on the factors is can be difficult to determine with any certainty. Then for the forecasts the future values of each of the factors need to be known with some confidence. Backcasting is also discussed as a method for exploring how future states can be achieved, for example meeting an aspiration set by policy. These techniques are described at a high level to describe the range of methods available, with example references of where they have been applied. This was the first step in exploring which alternative methods could be applied to household consumption forecasting in the UK. A fuller assessment of selected methods is presented in Step 5 of the manual. The pros and cons of the various approaches are also assessed against criteria in the manual. This will guide the selection of appropriate methods for use by individual companies. Further detail on how to apply the selected methods to meet the forecasting challenge are also included in Step 5 of the manual.

6.2 Use existing study data This is probably the most simplistic approach and relies on using the results of another study to find the change in predicted PCC and then applying this to the starting level of PCC. This has been used in the past by taking the results of another company’s rate of change in PCC and applying it to a different company’s PCC. This technique may also be used as a ‘rule of thumb’ to assess whether an alternative forecast is plausible.

6.3 Per-capita methods Per capita demand forecasting requires data on current (base-year) PCC and forecasts future demand by applying the current PCC to a population forecast. Hence the change in demand is proportional to a change in population. This method does not consider changes in any other of the factors that influence demand. PCC factors may be disaggregated into smaller segments of customers to increase the resolution of the forecast. This then requires a population forecast for each customer segment, as well as past usage data for each segment. This is often the starting point for many water demand forecasts (see section 6), and provides an initial picture of the future assuming that nothing else changes except for change in the population. In areas where the supply demand risk is low it is still a common approach for forecasting (e.g. CH2MHill, 2012 and Palmer et al, 2006). The method can be built on to form hybrid approaches, by including additional detail on explanatory factors or microcomponents.

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6.4 Trend based models Trend or time-series analysis is a method that forecasts water demand by fitting a trend line to historic data and extrapolating this trend into the future. The models predict future water demand based on assignment of trend parameters or statistical (autoregressive) relationships that link past values and systematic repeating cycles of demand (such as seasonal trends) to future values of demand. They tend to be used to predict demands over relatively short timeframes when longer-term influences may not be as significant. In their simplest form, trend models can take the basic form of a simple linear regression. This again can be used as a simple approach for comparing a new forecast to historical trends, and applying knowledge and logic to determine whether the new forecast is plausible. In more complex forms, trend models are built up using a range of more complex statistical modelling techniques such as ARIMA (Autoregressive integrated moving average) models. Whilst trend models can be built solely on a time series set of demand data, in the more complex trend modelling approaches it is also possible to include additional explanatory factors in the model (and this is a basis for some econometric modelling approaches, see below). One of the key advantages of robust trend analysis in water demand forecasting is the ability to take high level data on demand and produce a reasonably robust short term forecast (2-3 years). It therefore may have applications where an alternative forecast technique is needed to check forecasts in the very short term. It is unlikely to have reliable application in the medium to long term for household consumption forecasts.

6.5 Econometric Econometric models establish a statistical relationship between water demand and changes in defined explanatory variables ‘x’, for example price, income, or employment. Historic data for all the determinants, as well as past water usage, is required. Independently generated forecasts for any of the determinants are incorporated to reflect how the changes in determinants will alter demand. “Water demand estimation is usually formed as a generic model of the form: C = f(P,Z), which relates water consumption C to some price measure P and other factors Z.” (Arbues et al, 2003). An OECD report (OECD 2008) discusses modelling for a wide range of household behaviours, including water. It provides a comprehensive introduction to traditional econometric techniques and forecasting models. In particular, large parts of the water chapter are given over to defining the price elasticity of water under a range of different pricing and tariff regimes. It does also consider the effect of ‘demand side measures’ (i.e. water efficiency), service interruptions and incentives for water conservation in this econometric context. It concludes (contradictorily) that whilst water prices are relatively inelastic (i.e. consumption

29


does not tend to respond much to changes in unit prices), price remains one of the primary levers for influencing water consumption. In essence this apparent paradox cuts to the core of the UK water sector’s problem: demand is relatively price inelastic; less than 50% pay proportionately for the amount of water they consume (as they are unmetered); and yet price is one of the few levers the sector is able to manipulate. This does not imply that manipulating it will make much difference to demand. On the contrary it implies that other ways of influencing demand based on knowing what people actually do with water is crucial.

6.6 Regression models Probably the most detailed study on regression methods for water demand was carried out by the AWWA (AWWA Research Foundation, 1999) This was a large study based on the collection of primary data on the end-uses (i.e. micro-components) of water consumed in 1,188 single-family households surveyed across 12 US cities over a two-year period. The study carried out statistical regression analysis using a range of factors or influencing parameters. Memon and Butler (2006) report how the AWWARF study developed a range of complex statistical relationships based on the data collected. The daily water consumption for each micro-component was expressed in terms of several demand influencing parameters such as household size, income, floor area, degree of water conservation appliances and the marginal price of water. Examples of the equations for two of the micro-components (toilet use and shower/bath use) are included below. Toilet Water use model (US gallons per household per day)

qTOILET  14.483( MPW )0.225( HS )0.509( HSQFT )0.117 .e 0.091( PRE 60s )  ( 0.164( POST 80s )  0.076(ULTRATIO )  0.539(ULTONLY ) Where: MPW

Marginal price of water

HS

Household size (average number of persons)

HSQFT

Home square footage (average)

PRE60s

fraction of houses built before 1960

POST80s

fraction of houses built after 1980

ULTRATIO

fraction of all toilets that are ultra-low flush (ULF)

ULTONLY

fraction of customers that are completely retrofitted with ULF toilets

Shower/Bath Water Use Model (US gallons per household per day) qSHOWER  3.251(MPW )0.514( HS )0.885( INC )0.171e0.349( RENT ) 0.16(ULSRATIO )

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Where: INC

household income ($, average)

RENT

fraction of customers that rent

ULSRATIO

fraction of all showerheads that are low flow

These equations can be used to predict water use for each micro-component, given assumptions about the demographic make-up of a particular service area, and how these might change over time. In addition, the toilet and shower models include a component that can model the introduction of more water efficient fittings. The models were tested against actual consumption data for the locations studied, and demonstrated good performance for both internal and external water use, once the external use models were modified to include weather-related factors. One of the key observations from this study was that the models were able to accurately predict water use for the principal internal components (including toilets, showers, dishwashers and washing machines) regardless of location – i.e. the data collected and models developed were spatially transferable.

6.7 Micro-component or end-use models Micro-component models are a more detailed approach to water demand forecasting. They quantify the water used for specific activities (e.g. showering, bathing, toilet flushing, dishwashing, garden watering, etc.) by combining values for ownership, volume per use and frequency of use. For example, where per-capita consumption (PCC) is defined (e.g. UKWIR 1997) as: PCC = ∑i(Oi x Vi x Fi) + pcr Where ‘O’ is the proportion of household customers using the appliance or activity for microcomponent ‘i’ ‘V’ is the volume per use for ‘i’ ‘F’ is the frequency per use for ‘i’ pcr is per capita residual demand (miscellaneous use) By applying this together with the population data, a water demand forecast is formed. The forecast changes in each of the variables needs to be defined, which means that microcomponent models require practitioners to make estimates of detailed changes in numerous variables, to reflect future changes in technology, policy, regulation, and behaviour. The work of Herrington (1996) has been discussed earlier (see section 2.2). Micro-component modelling was then used by the Environment Agency to develop scenarios of possible future demand in EA (2001). The scenarios considered in detail how socio-economic change, based 31


on UK government ‘Foresight Scenarios’ could influence the micro-components of household demand in 2010 and 2025. In the last 12 years, micro-component analysis has been the main basis for household water consumption forecasting in England and Wales.

6.8 Aggregated micro (or macro) components This approach was proposed by several companies during the practitioner interviews, so merits further consideration. This approach maintains the principles of micro-component modelling, whilst reducing the number of variables that need to be forecast. One way of doing this would be to group the micro-components into the following: 

Micro-components which are influenced by factors that are difficult to predict



Micro-components which are influenced by factors that are more predictable



External use which can be dealt with using dry year annual average and critical period uplifts.

It is suggested that micro-components can be aggregated into the following three groups, as shown in Table 5: A. Reasonable predictability (internal use) B. Challenging predictability (internal use) C. External use, which contains an element of normal use and use which is uplifted using the dry year annual average and critical period assessment process. Future consumption of the more predictable Group A micro-components (e.g. WC flushing) could be estimated with greater confidence (less uncertainty) than Group B components. It may be possible to agree industry-standard consumption rates for Group A components.

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Table 5 Suggested grouping of micro-components EA WRPG microcomponents WC flushing

Clothes washing

Personal washing

Dishwashing Miscellaneous (internal) use

External use

Devices and activities

Predictability

Toilets

Reasonable predictability based on frequency of use and change in volume per use. Hand washing using internal Reasonable predictability, tap based on O, V and F. Washing machines Washer dryers Showers Challenging predictability, Power showers large component of Baths behaviour and Internal taps practices/habits that are not well understood. Hand washing Reasonable predictability, Dishwashing machine based on O, V and F. Internal tap (drinking, Reasonable predictability, cooking, cleaning) based on O, V and F. Wastage (leaks, plumbing losses, taps running, leaking WCs, etc.) Garden hose, garden Normal use requires sprinkler, pressure washer, definition, peak demand watering can. period uplifted in line with DY and CP use.

Proposed grouping A

A

B

A A

C

6.9 Variable flow methods The variable flow method uses consumption per household as the main water use factor (like the per –capita method with segmentation), but modifies the water use factors over time to account for changes in price and water conservation measures. The method starts with determining the PHC for each segment of the population, then normalises this for weather. The factors are then adjusted for price and income effects over time, then integrated with water conservation planning data, and multiplied by demographic projections to create a water demand forecast. This approach could be adapted to take account of the other factors that influence consumption (e.g. listed in section 4), where there are data which practitioners could use to determine the value of the factor(s). Palmer et al (2006) presents a review of the Variable Flow forecasting approach used by Seattle Public Utility’s water demand forecasts. ‘Variable Flow’ considers factors such as income elasticity, projected household income, household size, passive savings (i.e. through technological development and new regulations) and water conservation interventions; and 33


how changes to these factors affect the demand for water. The term ‘variable flow’ refers to how fixed future assumptions on ‘flows’ of water into supply are modified by the factors listed above. The approach is relatively simple in principle, predicting gross total future domestic demand (i.e. not split down to households or individuals) using a combination of econometric factors and the forecast impacts of regulation, market transformation and proactive demand side measures. The various papers recognise that these may not be the only factors influencing future water demand. Importantly, the approach controls for weather and climate influences, and confirms that that the method depends on identifying the correct explanatory variables. The most innovative, unusual and potentially useful aspect of this work is the use of Monte Carlo simulation to reflect the uncertainty around the effect of the explanatory variables on future water demand. Monte Carlo simulation could be applied to a range of the possible demand forecasting approaches being considered in this project.

6.10 Micro-simulation Micro-simulation is an approach for modelling the behaviour and interactions of micro units. In the case of water demand modelling this could be at an area, household or person level. A micro-simulation model is a set of rules operating on a representative sample of micro units. Models of this nature can be used to analyse the impact of policy or other external influences on the distribution of target variables, as opposed to the mean. So, a unit might be a household. The household would have a number of attributes; a number of people of known age and gender, a number of water using devices/activities, for example. A set of rules are then applied to these units, leading to simulated changes in state and behaviour (either deterministic or stochastic): for example how devices are used, chance of garden watering, chance of moving, opting for a meter, the chance of becoming more or less water efficient, etc., within a given time period. The model then estimates the outcomes of applying the rules to produce an overall aggregate change and the way the change is distributed across the population being modelled. Micro-simulation has been mainstreamed in the transport, health and especially tax, benefit and pension modelling contexts (Mitton, Sutherland, & Weeks, 2000; Tanton et al, 2009; Zaidi, Harding, & Williamson, 2009), especially where a fixed set of rules can be applied to a large heterogeneous population to understand the distributional effects of potential change. Micro-simulation models function by taking a large scale dataset of the units of interest – such as a household water demand population sample survey – and uses a range of methods to a) project the sample population forward in time and b) to model potential changes to any and all attributes that are thought to affect the outcome of interest – such as factors affecting household water demand. It is crucial to understand that in this approach each household is modelled as a distinct entity so that full heterogeneity to be found ‘in the real world’ can be maintained. Average consumption values, with appropriate estimates of variation and/or uncertainty for different household types for a range of scenarios therefore become a way to represent results and to analyse (or reveal) distributional effects (or change) rather than being core components of the

34


models themselves. Further, the combination of such models with a number of ‘spatialisation’ techniques has recently provided the means to construct models of local area demand which can be used as inputs to localized infrastructure investment cost-benefit analyses (Birkin & Clarke, 2011). Only a handful of studies have explicitly applied the micro-simulation approach to the projection of water demand starting with the work of Paul Williamson and colleagues (Clarke et al, 1997; Williamson et al., 2002). This work used a customer survey/monitor dataset supplied by Yorkshire Water to estimate the demand for water at the household level using micro-econometric approaches based on a range of compositional and appliance ownership characteristics. These models were then applied to Census data to develop a synthetic ‘water census’ for the Yorkshire Water area and thus overall (regional) demand estimates. By applying the models to small area census data they were also able to produce local (small area) demand estimates of use in local infrastructure planning. By rolling forwards both the micro-econometric demand model (under different scenarios, including those found in Herrington (1996)) and the Census data (using a population reweighing approach) the models were able to produce both small area and regional demand forecasts out to 2025. More recently, work has been reported that uses micro-simulation to assess the distributional and revenue impact of different water tariff schemes under the assumption of little or no behavioural response (Tanton, Keegan, & Vu, 2011). The models used bespoke household survey data collecting a range of socio-demographic characteristics as well as linked address level consumption data from the household’s service provider. Similar work has also been reported for Belgium (Vanhille, 2013) which used expenditure data captured in a large scale national income survey (EU-SILC 2009) to assess the relative impact of higher volumetric water consumption charges related to policies to encourage water demand reduction. These results clearly showed how cross-population price rises had a proportionately higher effect on low income households as water expenditure formed a higher proportion of household income and water consumption varies little by income. Whilst this work was intended to explore scenarios for less regressive charging mechanisms, it is also clear that the approach has potential value in the projection of demand especially if mechanisms to incorporate behavioural response and the evolution of water using practices can be incorporated.

6.11 Agent-based modeling Agent-based modelling is a type of micro-simulation that has been applied to develop forecasts of household water consumption by Downing et al (2003). This study found that socio-economic, political, regulatory drivers are likely to dominate climatic effects in driving future demand. Agent-based modelling is able to incorporate important social effects, therefore presents particular benefits, given the findings of the project. The report presents evidence that demonstrates the frequency distributions of real and simulated water consumption data have no defined variance and possibly no defined mean, therefore it is not legitimate to apply conventional statistical modelling procedures to these data. Similarly the use of models that assume a normal or any other finite-variance distribution are inappropriate. The agent-based models used in this project focused upon household behaviour, and how nearby households imitate each other’s behavioural patterns and affect the aggregate 35


demand for water. Individual household use is affected by temperature and precipitation, exhortations to use less water during droughts and the behaviour of neighbouring households. The extent to which individual households are influenced by their neighbours’ behaviour or by water company calls for restraint were set arbitrarily by the modellers. The model did include functionality for the replacement of washing machines and power showers, but oddly, not other water-using devices. Also, the model did not represent micro-component usage. The results of the modelling illustrate a wide range of different predicted demands. In general, the greater the proportion of households that was biased towards imitating their neighbours, the more stable was predicted demands. Demands were reduced during droughts, and in some runs, this reduced demand persisted. The report states that simulated behaviours are imitated from household to household, so can become entrenched through mutual reinforcement. The modelling indicates that social effects within clusters of households, including how these interactions influence the adoption of new water-using devices, may be a significant factor in determining demand. Athanasiadis et al (2005) presents an excellent summary of what agent-based modelling involves. It describes the development of the ‘DAWN’ model: a hybrid agent-based social model for the consumer with conventional econometric models, for evaluating different scenarios for policy making. It demonstrates how such an approach was effective in producing credible consumption estimates, compared to actual demand, and simulated a range of scenarios depending on different approaches to communication and education strategies. This paper also makes reference to the following papers, which focused on the Thames region. The EU funded FIRMA (Freshwater Integrated Resource Management with Agents) programme funded two related projects that present the same results for Agent Based Social Simulation (ABSS) of demand forecasts for the Thames region (defined as Oxford, London and the Southeast in Moss et al, 2000). Both papers aim to simulate how individual households collectively respond to exhortations to conserve water during drought periods. The first model that was developed aimed to capture the influence of the weather system, the hydrological system, the behaviour of households and the behaviour of policy agencies on total demand. These ‘prototype’ models were then extended to include micro-components of domestic consumption. These microcomponents were incorporated into the household interaction element of the model, so that specific patterns of ownership, frequency of use and volume per use could be propagated between households which could ‘see’ each other in the model. Work undertaken by ISD Analytics for several water service providers in the Australian state of Victoria uses an agent-based micro-simulation – SimulAIt – to model how individual households will react and behave in response to new strategies, policies, products, prices and competitive strategies. They argue that the strength of this method lies in the fact that it does not rely on previous data, but that historic consumption data can be used independently of the model to validate its performance. The modelling framework developed in this study appears to incorporate what we would expect to see in a household water demand forecasting micro-simulation model. However there is insufficient data available from online sources to understand model inputs and the specification of behavioural rules that will drive the model. Similarly, there are no details available online regarding model performance, measures of variation and confidence in the model results. 36


6.12 Backcasting Backcasting studies involve describing a desired future end-point and then working back from that point, to determine the feasibility (cost and practicalities) and what would be required to reach that goal. Backcasting is often quoted as being helpful when: problems are complex, there is need for major change, or dominant trends are causing problems in creating a robust forecast. It is essentially an approach for exploring means by which specified future states might be attained. Backcasting is a method that has been applied to water planning problems in Australia (White, Milne and Reidy, 2003), and in energy policy planning (Robinson, 1982). Robinson quotes that backcasting is concerned with: “not what futures are likely to happen, but with how desirable futures can be obtained. It …(involves) working backwards from a particular desired future end-point to the present in order to determine the physical feasibility of that future and what policy measures would be required to reach that point.” In their paper exploring the application of least cost planning for the evaluation of water demand measures, Fane et al (2007) investigate the combination end-use (micro-component) analysis and backcasting techniques. Importantly, this allowed them to challenge assumptions about future forecasts and explore alternative scenarios for meeting future goals. Backcasting approaches could be employed when a specific policy target (e.g. for household PCC) has been established. This would be the “desired future end-point” and from this, it would be possible to determine “what policy measures would be required to reach that point”.

6.13 Hybrid approaches There are various examples where the methods and approaches described above can be combined into hybrid models, in addition to the common approach of applying the methods to specific customer segments. For example, unit use coefficients may be scaled according to information and parameters obtained from literature, separate regression models, or microcomponent model outputs. One example of this approach is the variable flow method, which has been applied to some success in North America. The common applications of backcasting are also often a hybrid approach as the starting point for backcasting is often a detailed forecast model to determine the underlying trends driving water demand (for example demographic, technology and behaviour change). This identifies which factors represent the underlying trend and which factors are mutable to future action, i.e. which factors can be changed to achieve the desired end point. A hybrid approach can be attractive for dealing with the challenges of the estimating future water demand in the UK over the short, medium and long term. In the UK, the approach to estimating future demand starts with a baseline projection over 25 years (how demands are expected to change assuming existing management and policies continue). Then, if a company predicts a deficit in the supply-demand balance a final planning projection for demand is produced, taking into account the options (interventions and policies) developed in the water resources plan to resolve the deficit. Therefore forecasting techniques may be

37


suited to estimating the baseline projections of water demand, with backcasting methods being applied to investigate how to achieve desired future water demand.

6.14 Novel approaches Pullinger et al (2013) recently published a paper that builds on a rather different approach to the concept of demand to explore the value in considering the wider ‘ecology’ of household consumption to assess what drives water demand, now and potentially in the future. The approach focuses on the services that water provides to the individual which might include cleanliness, comfort, leisure convenience, health and psychological wellbeing and are analysed via the ‘traces’ they leave in, for example, household expenditure data. The paper used the UK Government’s Living Cost and Food Survey and an econometric approach to estimate the correlations between expenditure on the services that may be ‘implicated’ in water consumption (e.g. shampoo, soaps, washing detergents, etc. as proxies for actual behaviour) and expenditure on water by metered households (as a proxy for actual consumption). The authors suggest that such an approach could form the basis for a more nuanced projection of future demand through the consideration of wider influences on consumption and through the potential incorporation of societal trends such as changes in norms of showering, bathing, laundering, cooking and gardening. The paper then proposes a second approach based on the analysis of time use by individuals. It presents an analysis of within-day personal behaviour, derived from the ONS Multinational Time Use Survey, to illustrate the potential to estimate overall and temporal water consumption from traces of (or proxies for) daily activities. This is interesting, but as the paper points out, it really needs a longitudinal view of this for it to be of value in demand forecasting. Importantly, the paper highlights that there is too much focus in the UK water industry on average consumption, and this is unhelpful, as this doesn’t capture the full range of water using behaviours. In summary, this paper presents a ‘proxies of consumption’ approach. It is a quasieconometric modelling approach that could reveal much more about individual behaviours, but has the same challenges as more traditional econometric models in forecasting water demand.

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7

References

Arbués, F; Garcı ́a-Valiñas, M; Martı ́nez-Espiñeira, R, Estimation of residential water demand: a state-of-the-art review. The Journal of Socio-Economics Volume 32, Issue 1, March 2003, (2003) Pages 81–102 Athanasiadis, I.N., Mentes, A.K., Mitkas, P.A., Mylopoulos, Y.A. A Hybrid Agent-Based Model for Estimating Residential Water Demand. In Simulation: Transactions of The Society for Modelling and Simulation International, (2005) 81 (3): 175-187 AWWA Research Foundation, Residential End Uses of Water (1999) Birkin, M., & Clarke, M. Spatial Microsimulation Models: A Review and a Glimpse into the Future. (2011) In J. Stillwell & M. Clarke (Eds.), Population Dynamics and Projection Methods. London: Springer. CH2MHill, Long Range Water Resource Plan. Probabilistic Water Demand Forecast – Technique and Input Variable Summary. (2012) Memorandum 28 December 2012. Clarke, G. P., Kashti, A., McDonald, A., & Williamson, P. Estimating small area demand for water: a new methodology. (1997) Water and Environment Journal, 11(3), 186–192. Defra, BNWAT01 WCs: market projections and product details (2011), accessed at http://efficient-products.ghkint.eu/cms/product-strategies/subsector/domestic-waterusing-products.html#viewlist Environment Agency, A scenario Approach to Water Demand Forecasting (2001) Environment Agency, Ofwat, Defra and the Welsh Government, Water Resources Planning Guideline: The guiding principles for developing a water resources management plan, (November 2012) Environment Agency, Ofwat, Defra and the Welsh Government, Water Resources Planning Guideline: The technical methods and instructions, (November 2012) Downing T.E, Butterfield R.E., Edmonds B., Knox J.W., Moss S., Piper B.S. and Weatherhead E.K. (and the CCDeW project team). Climate Change and the Demand for Water, Research Report, Stockholm Environment Institute Oxford Office, Oxford. (2003) Herrington, Paul, Climate Change and the Demand for Water. HMSO, London. (1996) Memon F.A, Butler D. Water consumption trends and demand forecasting techniques. In: Butler D, Memon FA (eds) Water demand management. (2006) IWA Publishing, London Mitton, L., Sutherland, H., & Weeks, M. J. Microsimulation modelling for policy analysis : challenges and innovations (2000) pp. xxi, 331. Cambridge: Cambridge University Press. Moss, S, Downing, T, and Rouchier, J. Demonstrating the role of stakeholder participation. An agent-based social simulation model of water demand policy and response. (2000) CPM

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Report 00-76. Centre for Policy Modelling, The Business School, Manchester Metropolitan University, Manchester, UK OECD, Household Behaviour and the Environment: Reviewing the Evidence (2008) Ofwat, Patterns of Demand for Water in England and Wales: 1989-1999 (1999) Palmer, R; Polebitski, A; Traynham, L; King, K; Enfield, B. (2006) Review of Seattle’s New Water Demand Model. For King County Department of Natural Resources and Parks. (2006) Pullinger, M., Browne, A., Anderson, B., & Medd, W. (2013). Patterns of water: The water related practices of households in southern England, and their influence on water consumption and demand management. Lancaster University: Lancaster UK. Tanton, R., Vidyattama, Y., McNamara, J., Vu, Q. N., & Harding, A. Old, Single and Poor: Using Microsimulation and Microdata to Analyse Poverty and the Impact of Policy Change among Older Australians. (2009). Economic Papers: A Journal of Applied Economics and Policy, 28(2), 102–120. doi:10.1111/j.1759-3441.2009.00022.x Tanton, R., Keegan, M., & Vu, Q. N. A Microsimulation Model to Identify the Effects of Regulatory and Concessional Pricing. (2011) UKWIR/NRA, Demand Forecasting Methodology Main Report, UKWIR Report Ref No. 95/WR/01/1 (1995) UKWIR/EA, Forecasting Water Demand Components - Best Practice Manual, UKWIR Report Ref No. 97/WR/07/1 (1997) UKWIR, A Practical Method for Converting Uncertainty into Headroom, Ref 98/WR/13/1 (1998) UKWIR, Best Practice for Unmeasured Per Capita Consumption Monitors, Ref 99/WM/08/25, (1999) UKWIR, An Improved Methodology for Assessing Headroom, Ref 02/WR/13/2 (2002) UKWIR, Economics of Balancing Supply and Demand (EBSD), Ref 02/WR/27/3 (2002) UKWIR, Peak Water Demand Forecasting Methodology, Ref 06/WR/01/07 (2006) UKWIR, Customer Behaviour and Water Use: A good practice manual and roadmap for household consumption forecasting, Ref 12/CU/02/11 (2012) UKWIR, Water Resources Planning Tools 2012: EBSD report, Ref 12/WR/27/6 (2012) UKWIR, Water Resources Planning Tools 2012: Definitions, Ref 12/WR/27/6 (2012) UKWIR, Impact of Climate Change on Water Demand, Ref 13/CL/04/12 (2013) UKWIR, Understanding customer behaviour for water demand forecasting. Ref 14/WR/01/14 (2014) 40


UKWIR, WRMP19 Methods – Population, Household Properties and Occupancy Forecasting (2015, in preparation) UKWIR, WRMP19 Methods – Decision Making Process (2016, in preparation) UKWIR, WRMP19 Methods – Risk-Based Planning (2016, in preparation) UKWIR, Integration of Behavioural Change into Demand Forecasting and Water Efficiency Practices (2016, in preparation) Vanhille, J. Simulating water bill reform scenarios in Flanders, Belgium - an ex-ante evaluation of distributional effects of water pricing schemes for Flemish households. (2013). In 4th General Conference of the International Microsimulation Association. Canberra. Williamson, P., Mitchell, G., & McDonald, A. T. Domestic water demand forecasting: a static microsimulation approach. Water and Environment Journal, (2002) 16(4), 243–248. WRc. CP187: Domestic Microcomponent Water Use Data (WRc report P6832). (2005) WRc. Compendium of Micro-component Statistics. (2012) Zaidi, A., Harding, A., & Williamson, P. New Frontiers in Microsimulation Modelling. Public Policy and Social Welfare (2009) Vol. 36. Aldershot: Ashgate.

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42


Appendix 1 Questions used in practitioner interviews This Appendix presents the questions (with part 1 in the format of a matrix of questions) that were asked of demand forecasting practitioners. The findings from the survey are described in Section 2 of the report.

1

Thinking about the demand forecasts for WRMP 2014:

Question

What method did you use? For example (non-exhaustive list)

a)

Base year PCC/PHC – ask separately for measured and unmeasured customers

Micro-component Analysis? If so, what data was used for O, V, F?

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• Micro-component measurements (new data or historic data) • MTP data • EST data • Questionnaire data (new or historic) • Industry averages • Other published data • Other sources Did you use the EA definitions of microcomponents or others? Did you translate from the model to EA definitions Did you ‘calibrate’ base year microcomponents to reported PCC? Other methods?

b)

Forecast PCC/PHC

Did you use a micro-component forecast model? Did you use any other forecast method to check/validate the micro-component model? What data did you use to forecast the segmentation variables?


What data did you use to forecast forward the O, V, F data? • MTP projections • Logistic growth curves • Time series forecast models • Behavioural predictions • Technological predictions • Other Did you quantify uncertainties in the projections?

Why was it chosen?

What alternatives did you consider?

What were the challenges?

What would you do differently?

c)

d)

Occupancy forecast by segment Customer segmentation

How did you forecast occupancy for each segment?

Area – WRZ or other Socio-demographics – Property type, metered/unmetered, ACORN, occupancy, Other, or combinations of the above (state)

e)

Final planning forecasts

How did you incorporate water efficiency into the forecasts? Did you incorporate other demand management interventions? (e.g. alternative metering / tariff interventions) To what extent did Government guidance influence the final demand forecast figures?

f)

44

g)

Normal year / Dry year / critical period uplift Uncertainty assessment approach

How did you create normal, DYAA and critical period adjustments?

Did you quantify uncertainty associated with base data? If so, how Did you quantify uncertainties in your demand forecasts? If so, how Deterministic or stochastic? If so, how Did you consider the confidence in your uncertainty estimates?

2

Thinking about the demand forecasts for WRMP 2019:


a) b) c) d) e) f) g)

What would you change and why? Do you have examples of alternative approaches to demand forecasting that you have used or investigated? What do you think the main challenges for demand forecasting will be for WRMP 2014 (e.g. WRMP & DP links, resilience)? Is there a need to move away from micro-component analysis, if so to what? Any thoughts on how to include behaviour into the forecasts? What are the main data challenges? How do you suggest addressing the data challenges?