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As part of the POWADIMA research project, this paper describes the technique used to predict the consequences of different control settings on the performance of the water-distribution network, in the context of real-time, near-optimal control. Since the use of a complex hydraulic simulation model is somewhat impractical for real-time operations as a result of the computational burden it imposes, the approach adopted has been to capture its domain knowledge in a far more efficient form by means of an artificial neural network (ANN). The way this is achieved is to run the hydraulic simulation model off-line, with a large number of different combinations of initial tank-storage levels, demands, pump and valve settings, to predict future tank-storage water levels, hydrostatic pressures and flow rates at critical points throughout the network. These input/output data sets are used to train an ANN, which is then verified using testing sets. Thereafter, the ANN is employed in preference to the hydraulic simulation model within the optimization process. For experimental purposes, this technique was initially applied to a small, hypothetical water-distribution network, using EPANET as the hydraulic simulation package. The application to two real networks is described in subsequent papers of this series.

analysis problems, where the hydraulic simulation has to be repeated many times. Among 7 the methods used for hydraulic solvers, the most prominent nowadays is the global gradi-8 ent algorithm (GGA), based on a hybrid node-loop formulation and used by the software 9 package Epanet. Earlier, another method based just on loop flow equations was proposed, 10 which presents the advantage that it leads to a system matrix which is in most cases much 11 smaller than in the GGA method, but has also some disadvantages, mainly a less sparse 12 system matrix, and the fact that introducing some types of valves requires the redefinition 13 of the set of network loops initially defined. 14The contribution of this paper is to present solutions for overcoming the mentioned 15 disadvantages of the method based on loop flow equations. In particular, efficient procedures 16 are shown for selecting the network loops so as to achieve a highly sparse matrix, and methods 17 are presented to incorporate check valves and automatic control valves, while avoiding the 18 need to redefine the loops initially selected.

This paper introduces Elastic Cloud Computing Cluster (EC3), a tool that creates elastic virtual clusters on top of Infrastructure as a Service (IaaS) Clouds. The clusters are self-managed entities that scale out to a larger number of nodes on demand, up to a maximum size specified by the user. Whenever idle resources are detected, the clusters automatically scale in, according to some predefined policies, in order to cut down the costs in the case of using a public Cloud provider. This creates the illusion of a real cluster without requiring an investment beyond the actual usage. Two different case studies are presented to assess the effectiveness of an elastic virtual cluster. The results show that the usage of self-managed elastic clusters represents an important economic saving when compared both to physical clusters and to static virtual clusters deployed on an IaaS Cloud, with a reduced penalty in the elasticity management.