Abstract
This article proposes a new general approach in short-term water demand forecasting based on a two-stage learning process that couples time-series clustering with gene expression programming (GEP). The approach was tested on the real life water demand data of the city of Milan, in Italy. Moreover, multi-scale modeling using a series of head-time was deployed to investigate the optimum temporal resolution under study. Multi-scale modeling was performed based on rearranging hourly based patterns of water demand into 3, 6, 12, and 24 h lead times. Results showed that GEP should receive more attention among the emerging nonlinear modelling techniques if coupled with unsupervised learning algorithms in detailed spherical k-means.
Highlights
Water demand forecasting is a key predictive analytic among researchers in the field of water resource management
Short-term forecasts of water demand are usually based on the experience of the water distribution systems (WDS) operators in situations in which supervisory control and data acquisition (SCADA) systems are not yet deployed
The main outcome of this paper study is: Investigation of coupling time series clustering with gene expression programming (GEP) in short-term water demand forecasts to Evaluation of GEP
Summary
Water demand forecasting is a key predictive analytic among researchers in the field of water resource management. Operators in decision making for pumping schedules, storage, treatment, and distribution of water is an accurate and reliable forecast of short-term water demand [2]. Predictive models are becoming increasingly popular, since more data are available than in the past This popularity is highlighted in the field of water demand even more due to a lack of records on the consumption of water in the past. Short-term forecasts of water demand are usually based on the experience of the WDS operators in situations in which SCADA systems are not yet deployed.
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