𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Forecasting water demand—A disaggregated approach

✍ Scribed by G. G. Archibald


Publisher
John Wiley and Sons
Year
1983
Tongue
English
Weight
725 KB
Volume
2
Category
Article
ISSN
0277-6693

No coin nor oath required. For personal study only.

✦ Synopsis


The paper outlines the background research into domestic and industrial water use that was conducted over a period of 3 years and the use that was subsequently made of the detailed information in establishing a revised 20 year forecast of the demand for potable water supplies in the Severn-Trent Water Authority area in England. The major difficulty in forecasting water demand is its multiplicity of uses, each with a different potential rate of growth in demand; a further complication is the growth in water recycling in industry.

The water industry is one of the most capital intensive industries in the UK and because of the large capital sums involved in reservoir development and the long lead times for construction, the reliability of forecasts is a sensitive area. The component method described in this paper replaces the traditional extrapolatory approach and is believed to produce more meaningful forecasts.

KEY WORDS Water use research Component forecast Consumer surveys Capital investment Long term forecasting Trend curves

The water industry must have an eye on the likely level of demands 15-20 years ahead in deciding on current capital projects. The traditional long term demand forecasting method in the UK has been trend extrapolation but during the 1970s there has been increasing concern about the risks of over-provision associated with this approach and alternative methods have been investigated.

Expenditure on new resources is 'lumpy'; the next major reservoir to be opened in the Severn-Trent Water Authority area (STWA) will cost over E30m and will increase capacity in the East Midlands area by 25 per cent t o meet projected demands sometime beyond the end of the 0277-6693/83/020 18 1-1 2$0 1. 20


📜 SIMILAR VOLUMES


Forecasting future canadian residential
✍ M. K. Berkowitz; G. H. Haines JR. 📂 Article 📅 1984 🏛 John Wiley and Sons 🌐 English ⚖ 741 KB

This paper examines the sensitivity of forecasts to the level of aggregation of the data. A relative shares regression model and a multinominal logit model are tested with both aggregate and disaggregate survey data from 21 09 respondents. The results indicate the appropriate model to use depends on

Forecasting tourism demand: a cubic poly
✍ Fong-Lin Chu 📂 Article 📅 2004 🏛 Elsevier Science 🌐 English ⚖ 259 KB

This paper examines the accuracy of a forecasting model in predicting international tourism arrivals, as represented by the number of worldwide visitors to Singapore. The cubic polynomial model is employed to forecast the volume of tourist arrivals from January 1989 to July 1990. The results are the

Short-term municipal water demand foreca
✍ John Bougadis; Kaz Adamowski; Roman Diduch 📂 Article 📅 2005 🏛 John Wiley and Sons 🌐 English ⚖ 140 KB

## Abstract Water demand forecasts are needed for the design, operation and management of urban water supply systems. In this study, the relative performance of regression, time series analysis and artificial neural network (ANN) models are investigated for short‐term peak water demand forecasting.

An integrated forecasting approach to ho
✍ Sedat Yüksel 📂 Article 📅 2007 🏛 Elsevier Science 🌐 English ⚖ 244 KB

We aimed to forecast demand fluctuations in the hotel business that lead to crises and create a systematic and dynamic process that could be re-used. We forecasted demand for a five star hotel in Ankara using 149 monthly series of data and compared the results with those from MA, Simple, Holt's, Win

Modelling and forecasting short-term loa
✍ Mohamed A. Abu-El-Magd; Naresh K. Sinha 📂 Article 📅 1982 🏛 Elsevier Science 🌐 English ⚖ 440 KB

## ~A multivariable time series model is proposed for short-term load demand forecasting. Unlike other approaches, the order of the model is determined without first finding the coefficients of the model. The Hankel matrix used for determining the order is also utilized for estimating the paramete