A time series model of daily municipal water use is developed. The model is termed a conditional autoregressive process and can be interpreted as an autoregressive process with randomly varying mean. The randomly varying mean accounts for changes in water use that result from the complex interaction over time of “structural features” of the water use system. These features may include the price of water, total service area connections, plumbing code provisions, and customer income, among many others. The modeling approach is semiparametric. The model can be split into a component that is treated in a nonparametric framework and a component that is treated parametrically. The random mean process, which represents long‐term trend in water use, is treated in a nonparametric framework. Conditional on the random mean water use, the model reduces to a Gaussian autoregressive process with a modest number of parameters. The water use model is the core of a forecast system which is used to schedule releases from two water supply reservoirs which serve the Washington, D.C., Metropolitan Area. Model structure dictates that the key step in producing a water use forecast is an updating step in which a revised estimate of current mean water use is computed.
All Science Journal Classification (ASJC) codes
- Water Science and Technology