Predicting water demand: a review of the methods employed and future possibilities

2019 ◽  
Vol 19 (8) ◽  
pp. 2179-2198 ◽  
Author(s):  
Gustavo de Souza Groppo ◽  
Marcelo Azevedo Costa ◽  
Marcelo Libânio
Keyword(s):  
Artificial Intelligence ◽  
Water Supply ◽  
Water Demand ◽  
Supply And Demand ◽  
Demand Forecasting ◽  
System Management ◽  
Water Demand Forecasting ◽  
Combination Of Methods ◽  
Applications Of Machine Learning

Abstract The balance between water supply and demand requires efficient water supply system management techniques. This balance is achieved through operational actions, many of which require the application of forecasting concepts and tools. In this article, recent research on urban water demand forecasting employing artificial intelligence is reviewed, aiming to present the ‘state of the art’ on the subject and provide some guidance regarding methods and models to research and professional sanitation companies. The review covers the models developed using standard statistical techniques, such as linear regression or time-series analysis, or techniques based on Soft Computing. This review shows that the studies are, mostly, focused on the management of the operating systems. There is, therefore, room for long-term forecasts. It is worth noting that there is no global model that surpasses all the methods for all cases, it being necessary to study each region separately, evaluating the strengths of each model or the combination of methods. The use of statistical applications of Machine Learning and Artificial Intelligence methodologies has grown considerably in recent years. However, there is still room for improvement with regard to water demand forecasting.

Optimisation of water demand forecasting by artificial intelligence with short data sets

2019 ◽  
Vol 177 ◽  
pp. 59-66 ◽  
Author(s):  
Rafael González Perea ◽  
Emilio Camacho Poyato ◽  
Pilar Montesinos ◽  
Juan Antonio Rodríguez Díaz
Keyword(s):  
Artificial Intelligence ◽  
Water Demand ◽  
Demand Forecasting ◽  
Data Sets ◽  
Water Demand Forecasting

scholarly journals Performance Assessment for Short-Term Water Demand Forecasting Models on Distinctive Water Uses in Korea

2021 ◽  
Vol 13 (11) ◽  
pp. 6056
Author(s):  
Kang-Min Koo ◽  
Kuk-Heon Han ◽  
Kyung-Soo Jun ◽  
Gyu-Min Lee ◽  
Jung-Sik Kim ◽  
...  
Keyword(s):  
Water Supply ◽  
Water Consumption ◽  
Water Demand ◽  
Demand Forecasting ◽  
Supply System ◽  
Water Supply System ◽  
Smart Meter ◽  
Short Term ◽  
Water Demand Forecasting ◽  
Smart Water

It is crucial to forecast the water demand accurately for supplying water efficiently and stably in a water supply system. In particular, accurately forecasting short-term water demand helps in saving energy and reducing operating costs. With the introduction of the Smart Water Grid (SWG) in a water supply system, the amount of water consumption is obtained in real-time through a smart meter, which can be used for forecasting the short-term water demand. The models widely used for water demand forecasting include Autoregressive Integrated Moving Average, Radial Basis Function-Artificial Neural Network, Quantitative Multi-Model Predictor Plus, and Long Short-Term Memory. However, there is a lack of research on assessing the performance of models and forecasting the short-term water demand in the SWG demonstration plant. Therefore, in this study, the short-term water demand was forecasted for each model using the data collected from a smart meter, and the performance of each model was assessed. The Smart Water Grid Research Group installed a smart meter in block 112 located in YeongJong Island, Incheon, and the actual data used for operating the SWG demonstration plant were adopted. The performance of the model was assessed by using the Residual, Root Mean Square Error, Normalized Root Mean Square Error, Nash–Sutcliffe Efficiency, and Pearson Correlation Coefficient as indices. As a result of water demand forecasting, it is difficult to forecast water demand only by time and water consumption. Therefore, as the short-term water demand forecasting models using only time and the amount of water consumption have limitations in reflecting the characteristics of consumers, a water supply system can be managed more precisely if other factors (weather, customer behavior, etc.) influencing the water demand are applied.

scholarly journals Performance Assessment for Short-Term Water Demand Forecasting Models at an End-Use Level in Korea

2021 ◽  
Author(s):  
Kang Min Koo ◽  
Kuk Heon Han ◽  
Kyung Soo Jun ◽  
Gyumin Lee ◽  
Jung Sik Kim ◽  
...  
Keyword(s):  
Water Supply ◽  
Water Consumption ◽  
Water Demand ◽  
Demand Forecasting ◽  
Supply System ◽  
Water Supply System ◽  
Short Term ◽  
Water Demand Forecasting ◽  
Smart Water ◽  
End Use

It is crucial to forecast the water demand accurately for supplying water efficiently and stably in a water supply system. In particular, accurately forecasting short-term water demand helps in saving energy and reducing operating costs. With the introduction of the Smart Water Grid (SWG) in a water supply system, the amount of water consumption is obtained in real time through an advanced metering infrastructure (AMI) sensor, which can be used for forecasting the short-term water demand. The models widely used for water demand forecasting include the autoregressive integrated moving average, radial basis function-artificial neural network, quantitative multi-model predictor plus, and long short-term memory. However, there is a lack of research on assessing the performance of models and forecasting the short-term water demand by applying the data on the amount of water consumption by purpose and the pipe diameter of an end-use level of the SWG demonstration plant in each demand forecasting model. Therefore, in this study, the short-term water demand was forecasted for each model using the data collected from the AMI, and the performance of each model was assessed. The Smart Water Grid Research Group installed ultrasonic-wave-type AMI sensors in the block 112 located in YeongJong Island, Incheon, and the actual data used for operating the SWG demonstration plant were adopted. The performance of the model was assessed by using the residual, root mean square error (RMSE), normalized root mean square error (NRMSE), Nash–Sutcliffe efficiency (NSE), and Pearson correlation coefficient (PCC) as indices. The water demand forecast was slightly underestimated in models that employed the assessment results based on the RMSE and NRMSE. Furthermore, the forecasting accuracy was low for the NSE due to a large number of negative values; the correlation between the observed and forecasted values of the PCC was not high, and it was difficult to forecast the peak amount of water consumption. Therefore, as the short-term water demand forecasting models using only time and the amount of water consumption have limitations in reflecting the characteristics of consumers, a water supply system can be managed more precisely if other factors (weather, customer behavior, etc.) influencing the water demand are applied.

Sign in / Sign up

Export Citation Format

Share Document