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

Abstract

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.

Effects of Learning Rates and Optimization Algorithms on Forecasting Accuracy of Hourly Typhoon Rainfall: Experiments With Convolutional Neural Network

“…More importantly, optimization algorithms such as stochastic gradient descent (SGD), root mean square propagation (RMSprop), adaptive grad (AdaGrad), adaptive delta (AdaDelta), adaptive moment estimation (Adam), adaptive maximum (Adamax), and Nesterov‐accelerated adaptive moment estimation (Nadam) are absent in the typhoon rainfall forecasting model based on the DL model (Huang et al., 2018; Lin & Chen, 2005; Lin & Wu, 2009; Wei & Chou, 2020). These optimization algorithms have successfully been applied in rainfall forecasting (Barrera‐Animas et al., 2021; Fadilah et al., 2021; Manoj & Ananth, 2020; Prasetya & Djamal, 2019; Sari et al., 2020; Zhang et al., 2018), spatial prediction of landslides (Bui et al., 2019), wind speed and wind direction forecasting (Puspita Sari et al., 2020; Saputri et al., 2020), evapotranspiration forecasting (Walls et al., 2020), run‐off forecasting (Nath et al., 2021), air quality index prediction (H. He & Luo, 2020), river stage, flash flood susceptibility and streamflow forecasting (Hitokoto et al., 2017; Rahimzad et al., 2021; Tien Bui et al., 2020), water demand forecasting (Koo et al., 2021), temperature and global solar radiation prediction (Del & Starchenko, 2021; Ghimire et al., 2019).…”

Advanced Techniques for Monitoring and Management of Urban Water Infrastructures—An Overview

“…Koo et al [121] conduct extensive experiments on several widely used models for short-term water demand forecasting: ARIMA, radial basis function-artificial neural network (RBS-ANN), quantitative multi-model predictor plus (QMMP+), and LSTM. For the experiments, the authors use hourly water consumption data from smart water meters in an urban area with several types of water consumers (domestic, church, pre-school, restaurants, community center and shops).…”

Optimal Allocation of Resources for the Integrated Medical Care and Elderly Care Model under Dynamic Monitoring

“…Inventory demand forecasting can be forecasted by a number of different models. In the final selection of the most suitable model, this paper uses the principle of accuracy first as the model evaluation standard [15]. e principle of accuracy priority is that when the forecast model is used for inventory demand forecasting, the result of the forecast during the forecast period is smaller than the actual value, and the accuracy is higher.…”