Water demand time series generation for distribution network modeling and water demand forecasting

2018 ◽  
Vol 15 (2) ◽  
pp. 150-158 ◽  
Author(s):  
Bruno Melo Brentan ◽  
Gustavo Lima Meirelles ◽  
Daniel Manzi ◽  
Edevar Luvizotto
Keyword(s):  
Time Series ◽  
Water Demand ◽  
Network Modeling ◽  
Distribution Network ◽  
Demand Forecasting ◽  
Water Demand Forecasting

scholarly journals Municipal Water Demand Forecasting: Tools for Intervention Time Series

2011 ◽  
Vol 29 (6) ◽  
pp. 998-1007 ◽  
Author(s):  
M. Herrera ◽  
J. C. García-Díaz ◽  
J. Izquierdo ◽  
R. Pérez-García
Keyword(s):  
Time Series ◽  
Water Demand ◽  
Demand Forecasting ◽  
Water Demand Forecasting ◽  
Municipal Water

scholarly journals Hybrid Model for Short-Term Water Demand Forecasting Based on Error Correction Using Chaotic Time Series

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1683
Author(s):  
Shan Wu ◽  
Hongquan Han ◽  
Benwei Hou ◽  
Kegong Diao
Keyword(s):  
Time Series ◽  
Error Correction ◽  
Hybrid Model ◽  
Water Demand ◽  
Distribution Systems ◽  
Demand Forecasting ◽  
Chaotic Time Series ◽  
Forecasting Model ◽  
Short Term ◽  
Water Demand Forecasting

Short-term water demand forecasting plays an important role in smart management and real-time simulation of water distribution systems (WDSs). This paper proposes a hybrid model for the short-term forecasting in the horizon of one day with 15 min time steps, which improves the forecasting accuracy by adding an error correction module to the initial forecasting model. The initial forecasting model is firstly established based on the least square support vector machine (LSSVM), the errors time series obtained by comparing the observed values and the initial forecasted values is next transformed into chaotic time series, and then the error correction model is established by the LSSVM method to forecast errors at the next time step. The hybrid model is tested on three real-world district metering areas (DMAs) in Beijing, China, with different demand patterns. The results show that, with the help of the error correction module, the hybrid model reduced the mean absolute percentage error (MAPE) of forecasted demand from (5.64%, 4.06%, 5.84%) to (4.84%, 3.15%, 3.47%) for the three DMAs, compared with using LSSVM without error correction. Therefore, the proposed hybrid model provides a better solution for short-term water demand forecasting on the tested cases.

scholarly journals Variable selection methods for water demand forecasting in Ethiopia: Case study Gondar town

2018 ◽  
Vol 4 (1) ◽  
pp. 1537067 ◽  
Author(s):  
Mohammed Gedefaw ◽  
Wang Hao ◽  
Yan Denghua ◽  
Abel Girma ◽  
Mustafa Ibrahim Khamis
Keyword(s):  
Variable Selection ◽  
Water Demand ◽  
Demand Forecasting ◽  
Selection Methods ◽  
Water Demand Forecasting ◽  
Gondar Town
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