scholarly journals A Probabilistic Short-Term Water Demand Forecasting Model Based on the Markov Chain

Water ◽  
2017 ◽  
Vol 9 (7) ◽  
pp. 507 ◽  
Author(s):  
Francesca Gagliardi ◽  
Stefano Alvisi ◽  
Zoran Kapelan ◽  
Marco Franchini
Keyword(s):  
Markov Chain ◽  
Water Demand ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Short Term ◽  
Water Demand Forecasting ◽  
Model Based ◽  
Demand Forecasting Model

scholarly journals Short-term water demand forecasting using hybrid supervised and unsupervised machine learning model

Smart Water ◽  
2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Mo’tamad Bata ◽  
Rupp Carriveau ◽  
David S.-K. Ting
Keyword(s):  
Machine Learning ◽  
Water Demand ◽  
Demand Forecasting ◽  
Hybrid Models ◽  
Supervised Machine Learning ◽  
Forecasting Model ◽  
Short Term ◽  
Unsupervised Machine Learning ◽  
Water Demand Forecasting ◽  
Clustering Model

Abstract Regression Tree (RT) forecasting models are widely used in short-term demand forecasting. Likewise, Self-Organizing Maps (SOM) models are known for their ability to cluster and organize unlabeled big data. Herein, a combination of these two Machine Learning (ML) techniques is proposed and compared to a standalone RT and a Seasonal Autoregressive Integrated Moving Average (SARIMA) models, in forecasting the short-term water demand of a municipality. The inclusion of the Unsupervised Machine Learning clustering model has resulted in a significant improvement in the performance of the Supervised Machine Learning forecasting model. The results show that using the output of the SOM clustering model as an input for the RT forecasting model can, on average, double the accuracy of water demand forecasting. The Mean Absolute Percentage Error (MAPE) and the Normalized Root Mean Squared Error (NRMSE) were calculated for the proposed models forecasting 1 h, 8 h, 24 h, and 7 days ahead. The results show that the hybrid models outperformed the standalone RT model, and the broadly used SARIMA model. On average, hybrid models achieved double accuracy in all 4 forecast periodicities. The increase in forecasting accuracy afforded by this hybridized modeling approach is encouraging. In our application, it shows promises for more efficient energy and water management at the water utilities.

Research on Industrial Water Demand Forecasting Model and its Application in China

2014 ◽  
Vol 675-677 ◽  
pp. 976-981 ◽  
Author(s):  
Qing Rong Zou ◽  
Xiu Li Liu
Keyword(s):  
Water Demand ◽  
Demand Forecasting ◽  
Value Added ◽  
Forecasting Model ◽  
Water Supply Systems ◽  
Industrial Water ◽  
Correlation Relationship ◽  
Water Demand Forecasting ◽  
Demand Forecasting Model ◽  
Industrial Water Demand

Industrial water demand forecasting is needed for the effective planning and management of water supply systems. The paper first made impacting factor analysis of industrial water demand. Data analysis showed that there was a converse “U” type relationship between industrial water demand and industrial value added. There was a negative correlation relationship between industrial water demand and the recycling rate of it. With multiple regression method, industrial water demand forecasting model was established. In the supposed scenarios, we applied the model to predict Chinese industrial water demand in 2014 and 2015.The results were 141.04 billion m3 in 2014 and 137.79billion m3 in 2015.

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