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Rainwater harvesting has been used as a technique to promote water conservation in buildings, as it substitutes the potable water in activities where the use of potable water is not required. In spite of the surge in interest over recent years, some questions still remain regarding to these systems, mainly what involves the reservoir sizing. There are many methods for this purpose that use different inputs such as: rainwater demand, catchment area, roof material, rainy data (daily or monthly) and dry periods. Even in the Brazilian Standard (ABNT, 2007), there is no consensus as to which method should be used. Table 1 shows the main methods found in the literature and their respective inputs. Mainly in developing countries, actions that promote water conservation must be economically feasible so it can raise the interest in investments. Moreover, urban lots are progressively smaller and more expensive. These variables can restrict the size of the reservoirs used in a rainwater system and this should be considered in their design. This article proposes the use of an optimization technique to find the most adequate volume of rainwater reservoirs i.e. the optimal economical result measured by the Net Present Value (NPV): the Particle Swarm Optimization (PSO). PSO is a population-based technique of stochastic nonlinear functions. Its use was inspired by social behavior in flocking birds or school of fishes (Boeringer, Weiner, 2004). It was used for this optimization process because of its flexibility and because it allows the inclusion of other variables that might interfere with the NPV calculation in any given future. This aspect expands the capacity of data processing without loss of efficiency of the algorithm. In this study, PSO was used to size rainwater reservoirs in four case studies and the results obtained were compared with traditional methods that have been used for this purpose, verifying the improvement of the decision making process.

Determination of the Storage Volume in Rainwater Harvesting Building Systems: Incorporation of Economic Variable