Drinking water demand forecasting using artificial neural network in Tunisia

2020 ◽  
Vol 176 ◽  
pp. 296-299
Author(s):  
Issam Nouiri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Drinking Water ◽  
Water Demand ◽  
Demand Forecasting ◽  
Water Demand Forecasting ◽  
Artificial Neural

scholarly journals Weekly urban water demand forecasting using a hybrid wavelet–bootstrap–artificial neural network approach

2014 ◽  
Vol 46 (3) ◽  
pp. 197-204
Author(s):  
Kaz Adamowski ◽  
Jan F. Adamowski ◽  
Ousmane Seidou ◽  
Bogdan Ozga-Zieliński
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Demand ◽  
Demand Forecasting ◽  
Urban Water ◽  
Neural Network Approach ◽  
Water Demand Forecasting ◽  
Urban Water Demand ◽  
Artificial Neural ◽  
Artificial Neural Network Approach

Abstract Weekly urban water demand forecasting using a hybrid wavelet-bootstrap-artificial neural network approach. This study developed a hybrid wavelet-bootstrap-artificial neural network (WBANN) model for weekly (one week) urban water demand forecasting in situations with limited data availability. The proposed WBANN method is aimed at improving the accuracy and reliability of water demand forecasting. Daily maximum temperature, total precipitation and water demand data for almost three years were used in this study. It was concluded that the hybrid WBANN model was more accurate compared to the ANN, BANN and WANN methods, and can be applied successfully for operational water demand forecasting. The WBANN model simulated peak water demand very effectively. The better performance of the WBANN model indicated that wavelet analysis significantly improved the model’s performance, whereas the bootstrap technique improved the reliability of forecasts by producing ensemble forecasts. The WBANN model was also found to be effective in assessing the uncertainty associated with water demand forecasts in terms of confidence bands; this can be helpful in operational water demand forecasting.

scholarly journals Forecasting water demand under climate change using artificial neural network: a case study of Kathmandu Valley, Nepal

2020 ◽  
Vol 20 (5) ◽  
pp. 1823-1833
Author(s):  
Manish Shrestha ◽  
Sujal Manandhar ◽  
Sangam Shrestha
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Supply ◽  
Phase I ◽  
Water Demand ◽  
Demand Forecasting ◽  
Phase Iii ◽  
Annual Rainfall ◽  
Kathmandu Valley ◽  
Artificial Neural

Abstract With a water demand of 370 MLD, Kathmandu Valley is currently facing a water shortage of 260 MLD. The Melamchi Water Supply Project (MWSP) is an interbasin project aimed at diverting 510 MLD of water in three phases (170 MLD in each phase). Phase I of the project was expected to complete by 2018. Water demand forecasting is the first and important activity in managing water supply. Using the socio-economic factors of number of connections, water tariff and ratio of population to number of university students and climatic factor of annual rainfall, artificial neural network (ANN) was used to predict the water demand of Kathmandu Valley until the year 2040. The analysis suggests that, even after the completion of Phase I of MWSP, the water scarcity in the valley will be 160 MLD in 2020. Therefore, Phase II of MWSP should be completed within 2025 and Phase III should be completed by 2040. The result of this study aids KUKL for better management of the water system. In addition, this research can help in decision making to construct the second and third phase for MWSP, the construction date of which still has not been decided.

scholarly journals Data Preprocessing for Load Forecasting using Artificial Neural Network

2020 ◽  
Author(s):  
Rafael S. F. Ferraz ◽  
Renato S. F. Ferraz ◽  
Lucas F. S. Azeredo ◽  
Benemar A. de Souza
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power System ◽  
Demand Forecasting ◽  
Energy Resource ◽  
Data Preprocessing ◽  
Electric Power System ◽  
Percentage Error ◽  
Gradual Change ◽  
Artificial Neural

An accurate demand forecasting is essential for planning the electric dispatch in power system, contributing financially to electricity companies and helping in the security and continuity of electricity supply. In addition, it is evident that the distributed energy resource integration in the electric power system has been increasing recently, mostly from the photovoltaic generation, resulting in a gradual change of the load curve profile. Therefore, the 24 hours ahead prediction of the electrical demand of Campina Grande, Brazil, was realized from artificial neural network with a focus on the data preprocessing. Thus, the time series variations, such as hourly, diary and seasonal, were reduced in order to obtain a better demand prediction. Finally, it was compared the results between the forecasting with the preprocessing application and the prediction without the  preprocessing stage. Based on the results, the first methodology presented lower mean absolute percentage error with 7.95% against 10.33% of the second one.

scholarly journals Artificial Neural Networks for Urban Water Demand Forecasting: A Case Study

2019 ◽  
Vol 1284 ◽  
pp. 012004 ◽  
Author(s):  
Leandro L Lorente-Leyva ◽  
Jairo F Pavón-Valencia ◽  
Yakcleem Montero-Santos ◽  
Israel D Herrera-Granda ◽  
Erick P Herrera-Granda ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Demand ◽  
Demand Forecasting ◽  
Urban Water ◽  
Water Demand Forecasting ◽  
Urban Water Demand ◽  
Artificial Neural
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