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.

In South Korea, in line with the increasing need for a reliable water supply following the continuous increase in water demand, the Smart Water Grid Research Group (SWGRG) was officially launched in 2012. With the vision of providing water welfare at a national level, SWGRG incorporated Information and Communications Technology in its water resource management, and built a living lab for the demonstrative operation of the Smart Water Grid (SWG). The living lab was built in Block 112 of YeongJong Island, Incheon, South Korea (area of 17.4 km², population of 8000), where Incheon International Airport, a hub for Northeast Asia, is located. In this location, water is supplied through a single submarine pipeline, making the location optimal for responses to water crises and the construction of a water supply system during emergencies. From 2017 to 2019, ultrasonic wave type smart water meters and IEEE 802.15.4 Advanced Metering Infrastructure (AMI) networks were installed at 527 sites of 958 consumer areas in the living lab. Therefore, this study introduces the development of SWG core element technologies (Intelligent water source management and distribution system, Smart water distribution network planning/control/operation strategy establishment, AMI network and device development, Integrated management of bi-directional smart water information), and operation solutions (Smart water statistics information, Real-time demand-supply analysis, Decision support system, Real-time hydraulic pipeline network analysis, Smart DB management, and Water information mobile application) through a field operation and testing in the living lab.

This study evaluated possible application of a seawater dilution process (SDP) to a seawater reverse osmosis (SWRO) filtration system for low-energy desalination. The main experiments of this study are to find the effects of the SDP in an SWRO filtration process to reduce seawater salinity. The measured total dissolved solids values were 29,640 mg/L for seawater, 8,720 mg/L for brackish water, and 2,896 mg/L for treated sewage water. In the experiment, two multiple water were blended with seawater to lower salinity. First experiment was conducted seawater with brackish water and found an increase in the permeate flux at 9.6% in the SWRO process. Second experiment was conducted seawater with treated sewage water and delivered an increase in the permeate flux at 12.9%. During the ultrafiltration (UF) operation, the pressure applied in diluted water increased rapidly comparing to seawater because the high concentrations of particulate and organic matter were detected in pretreatment of UF and main treatment of SWRO due to high silt density index and total organic carbon value in supplemental water. Therefore, to optimize application of the SDP, fouling effects should be considered for a longtime operation in membrane processes.