Abstract
Short-term demand forecast is important for cost-effective operation of water distribution systems. It plays an essential role in optimizing pump operation strategy such that energy consumption cost can be consistently minimized on daily basis. In this paper, two genetic programming (GP) approaches including tree-based genetic programming (TGP) and gene expression programming (GEP) have been applied to the construction of explicit demand forecast models for a real water system. A number of demand forecast models have been induced by using the historical flow data from 2005 to 2008 for a district water system, a sub-system of a large demand monitoring zone (DMZ) in UK. In general, the results obtained show that both TGP and GEP are effective for constructing short-term demand forecast models. Among all the models tested in this study, an average forecast accuracy of greater than 90% was achieved for the forecast model with two previous day demands and average temperature.
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