Short-term water demand forecasts can provide valuable information to distribution system operators for controlling the production, storage and delivery of drinking water. Our current research is focused on developing an integrated Time Series Forecasting Framework (TSFF) to statistically predict hourly/quarter-hourly demands in real-world, real-time scenarios. The first version of TSFF has been prototyped within Matlab. Two forecasting models, a fixed seasonal auto-regressive (FSAR) model and an adaptive seasonal auto-regressive (ASAR) model have been included within the framework for evaluation. The ASAR model self-updates model parameters at run-time using maximum likelihood estimates (MLE). The framework has been applied to a real-world case study of a system-wide water demand time series. With an underlying auto-regressive model, AR(3), the ASAR provides, on average, 5.3% absolute relative prediction error (ARE) for lead-1 forecasts, 10.2% for lead-2 forecasts, and 14.2% for lead-3 forecasts. Computationally, the speed of the algorithm is such as to easily accommodate real-time activities. The framework will be applied to additional demand time series in order to evaluate the performance of the forecasting algorithm and compare the water consumption characteristics of different distribution systems.
|Original language||English (US)|
|Number of pages||4|
|State||Published - 2014|
|Event||12th International Conference on Computing and Control for the Water Industry, CCWI 2013 - Perugia, Italy|
Duration: Sep 2 2013 → Sep 4 2013
- Seasonal auto-regressive model
ASJC Scopus subject areas