Water demand forecasting of Beijing using the Time Series Forecasting Method

2012 ◽  
Vol 22 (5) ◽  
pp. 919-932 ◽  
Author(s):  
Yuanzheng Zhai ◽  
Jinsheng Wang ◽  
Yanguo Teng ◽  
Rui Zuo
Keyword(s):  
Time Series ◽  
Water Demand ◽  
Demand Forecasting ◽  
Time Series Forecasting ◽  
Water Demand Forecasting ◽  
Forecasting Method

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

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 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

scholarly journals Demand forecasting in Small and Medium Enterprises (SMEs) ED Aluminium Yogyakarta using causal, time series, and combined causal-time series approaches

2018 ◽  
Vol 204 ◽  
pp. 01004 ◽  
Author(s):  
Wildanul Isnaini ◽  
Andi Sudiarso
Keyword(s):  
Time Series ◽  
Small And Medium Enterprises ◽  
Demand Forecasting ◽  
Actual Condition ◽  
Inventory Data ◽  
Inventory Cost ◽  
Forecasting Method ◽  
Medium Enterprises ◽  
Better Than

ED Aluminium is the biggest Small and Medium Enterprises (SMEs) in Daerah Istimewa Yogyakarta (DIY) with 90 number of workers and 1,5 ton ingot capacity for production (Isnaini, 2014). Inventory data in December 2015 indicates that some products are overstocked (9%) and stockout (83%). This condition can happend because that SMEs still using intuition to predict the number of demand. Inventory fluctuation causes the inventory cost increases while overstock happend and lost the opportunity cost during stockout. To avoid overstock and stockout, the determination of demand with exact method is needed and one of them can be solved by forecasting method. This study aims to find the best forecasting methods of demand in 2015 using causal, time series, and combined causal-time series approces that better than the actual condition. The results of this research is the best forecasting method used to predict the number of sales in January-November 2015, that are SARIMA (3,1,1)(0,1,1)12 for WB, SARIMA (1,1,1)(1,0,1)6 for WSD, SARIMA (1,1,1)(1,1,0)6 for DE, SARIMA (2,1,1)(1,1,0)6 for PE, and SARIMA (2,1,3)(0,1,0)12 for PT.

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