A Tool for Daily Demand Pattern Generation

A correct water demand characterization is at the base of a reliable water distribution system simulation. The stochastic nature of water demand is well established and thus has to be addressed. In the present work a methodology to generate synthetic demand patterns interpolating known points by means of piecewise interpolation has been implemented in Python. Subsequently a stochastic approach has been applied to the interpolated demand patterns, which is based on a mixed probability distribution. Such approach considers the dual nature of water demand as continuous and discrete random variable, in order to contemplate both the event of it being null and not null. The needed parameters are obtainable through simple equations depending solely on the number of served users.