Spatial-temporal Statistical Modeling of Treated Drinking Water Usage
Author | : Ernesto Arandia |
Publisher | : |
Total Pages | : 172 |
Release | : 2013 |
ISBN-10 | : OCLC:896729797 |
ISBN-13 | : |
Rating | : 4/5 (97 Downloads) |
Book excerpt: In essence, modern drinking water distribution systems (DWDSs) exist to continuously satisfy the demand of their users while complying with water quality regulations. It stands to reason that the tasks of quantifying, estimating, and forecasting water consumption are critical components of resource management, planning and operation in the urban water industry. Yet, due to the complex stochastic nature of water demands, such important tasks are typically performed in an oversimplified deterministic manner which at best produces conservative results. Of critical inter- est, therefore, is the adoption of quantitative methods and technologies for accurately estimating and forecasting water consumption. The concomitant benefits may include the reduction of energy costs, residence times, pressure, and leakage in the DWDS through the optimal operation of pumps, reservoirs, and supply.Computational models of DWDSs have widely been developed by water utilities and researchers and applied mainly in design and offline analyses. It is clear that the industry and research com- munity recognize the usefulness of hydraulic models as tools to analyze the complex interaction among the generally massive number of system's components. Significant efforts are sometimes devoted to the refinement of the models to ensure their parameters reflect reality as close as possi- ble. Curiously, however, the parameters that most greatly influence the model's behavior, i.e., the water demands, are normally overlooked. It is not uncommon to assume a single arbitrary daily pattern for the totality of the nodes in a network model. This research considers that a more valid approach is to combine a reliable hydraulic model of a DWDS with realistic stochastic models of water use developed from fine-resolution consumption data. The intent is to abandon the time-pattern paradigm and take benefit of the opportunity to ac- cess large volumes of automatic meter reading (AMR) data at the level of the individual consumer which are expected to increasingly become available in the water industry. Methodologies that may be applied in offline and online estimation of water consumption are presented. On the one hand, a stochastic model structure for synthetic generation of demands is studied which may be used in producing individual consumer as well as nodal demands in a hydraulic network model. The method comprises a parameterization that intends to represent the native characteristics of the data. On the other hand, methodologies to estimate and forecast nodal demands are proposed consistent with a real-time approach. One of the contributions of this work is the development of an AMR database which is made available to the public. The data provides access to water consumption measurements from an unprecedented number of users of different categories over an extended period of time. Data of such type is expected to increase in the near future, therefore, the database may be a useful test bed for algorithms that incorporate highly disaggregated measurements of water use in DWDSs. Another contribution is the adoption of models for synthetic generation of demands that vary peri- odically and resemble real AMR demands. In addition, this research examines different models to forecast water demand aggregated at useful scales. Finally, it applies the findings to estimate and forecast water consumption in a network zone and in real time. The method proposed is extensively assessed and compared against a conventional approach to forecast demands.