The short-term, demand-forecasting model described in this paper forms the third constituent part of the POWADIMA research project which, taken together, address the issue of real-time, near-optimal control of water-distribution networks. Since the intention is to treat water distribution as a feed-forward control system, operational decisions have to be based on the expected future demands for water, rather than just the present known requirements.Accordingly, it was necessary to develop a short-term, demand-forecasting procedure. To that end, monitoring facilities were installed to measure short-term fluctuations in demands for a small experimental network, which enabled a thorough investigation of trends and periodicities that can usually be found in this type of time-series. On the basis of these data, a short-term, demand-forecasting model was formulated. The model reproduces the periodic patterns observed at annual, weekly and daily levels prior to fine-tuning the estimated values of future demands through the inclusion of persistence effects. Having validated the model, the demand forecasts were subjected to an analysis of the sensitivity to possible errors in the various components of the model. Its application to much larger case studies is described in the following two papers.

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In this paper a method for optimal placement of isolation valves in water distribution systems is presented. These valves serve to isolate parts of the network (segments) containing one or more pipes on which maintenance work can be performed without disrupting service in the entire network or in large portions of it. The segments formed after the installation and closure of isolation valves are identified and characterised using an algorithm which is based on the use of topological matrixes associated with the structure of the original network and the one modified to take account of the presence of (closed) valves. A multi-objective genetic algorithm is used instead to search for the optimal position of the valves. In the application of the method different objective functions were used and compared to solve the problem as to the optimal placement of the valves. The results showed that the most appropriate ones are the total cost of the valves (to be minimised) and the weighted average "water demand shortfall" (likewise to be minimised); in particular, the weighted average shortfall is calculated considering the shortfalls associated with the various segments of the network (shortfall is the unsupplied demand after isolating a segment) and the likelihood of failures tied to mechanical factors occurring in the segments. The methodology was applied to a case study focusing on a simplified layout of the water distribution system of the city of Ferrara (Italy). 4318 E. Creaco et al.

This paper presents a procedure for the automatic creation of district metered areas (DMAs) in a water distribution system. The procedure uses techniques derived from graph theory (Breadth First Search and algorithm for finding the shortest paths in a graph) and demand-driven hydraulic simulations of the network in order to (a) divide the nodes among an assigned number of DMAs, (b) identify the “open” links between districts where flow meters will be placed and (c) identify the “closed” links between districts where isolation valves will be placed. The application of the proposed procedure to the case of a real water distribution system revealed it to be robust and effective. In particular, the results obtained show that the procedure makes it possible to identify very good solutions in terms of resilience and minimum pressures when reference to the peak demand and fire-flow conditions is made. The resulting performance indicators were better than those obtainable by applying a similar procedure previously proposed in the scientific literature

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Calibration is a process of comparing model results with field data and making the appropriate adjustments so that both results agree. Calibration methods can involve formal optimization methods or manual methods in which the modeler informally examines alternative model parameters. The development of a calibration framework typically involves the following: (1) definition of the model variables, coefficients, and equations; (2) selection of an objective function to measure the quality of the calibration; (3) selection of the set of data to be used for the calibration process; and (4) selection of an optimization/manual scheme for altering the coefficient values in the direction of reducing the objective function. Hydraulic calibration usually involves the modification of system demands, fine-tuning the roughness values of pipes, altering pump operation characteristics, and adjusting other model attributes that affect simulation results, in particular those that have significant uncertainty associated with their values. From the previous steps, it is clear that model calibration is neither unique nor a straightforward technical task. The success of a calibration process depends on the modeler's experience and intuition, as well as on the mathematical model and procedures adopted for the calibration process. This paper provides a summary of the Battle of the Water Calibration Networks (BWCN), the goal of which was to objectively compare the solutions of different approaches to the calibration of water distribution systems through application to a real water distribution system. Fourteen teams from academia, water utilities, and private consultants participated. The BWCN outcomes were presented and assessed at the 12th Water Distribution Systems Analysis conference in Tucson, Arizona, in September 2010. This manuscript summarizes the BWCN exercise and suggests future research directions for the calibration of water distribution systems.

Haifa-A is the first of two case studies relating to the POWADIMA research project. It comprises about 20% of the city's water-distribution network and serves a population of some 60,000 from two sources. The hydraulic simulation model of the network has 126 pipes, 112 nodes, 9 storage tanks, 1 operating valve and 17 pumps in 5 discrete pumping stations. The complex energy tariff structure changes with hours of the day and days of the year. For a dynamically rolling operational horizon of 24 h ahead, the real-time, near-optimal control strategy is calculated by a software package that combines a genetic algorithm (GA) optimizer with an artificial neural network (ANN) predictor, the latter having replaced a conventional hydraulic simulation model to achieve the computational efficiency required for real-time use. This paper describes the Haifa-A hydraulic network, the ANN predictor, the GA optimizer and the demand-forecasting model that were used. Thereafter, it presents and analyses the results obtained for a full (simulated) year of operation in which an energy cost saving of some 25% was achieved in comparison to the corresponding cost of current practice. Conclusions are drawn regarding the achievement of aims and future prospects.

The second of the two case studies in the POWADIMA research project, the Valencia waterdistribution network, serves a population of approximately 1.2 million and is supplied by surface water via two treatment plants which have significantly different production costs. The only storage available is located at the treatment plants, each of which has its own pumping station.The management of the network is a complex operation involving 4 pressure zones and 49 operating valves, 10 of which are routinely adjusted. The electricity tariff structure varies with the hour of the day and month of the year. The EPANET hydraulic simulation model of the network has 725 nodes, 10 operating valves, 2 storage tanks and 17 pumps grouped at the two pumping stations. The control system that has been implemented comprises an artificial neural network predictor in place of the EPANET model and a dynamic genetic algorithm to optimize the control settings of pumps and valves up to a 24 h rolling operating horizon, in response to a highly variable demand. The results indicate a potential operational-cost saving of 17.6% over a complete (simulated) year relative to current practice, which easily justifies the cost of implementing the control system developed.