• Journal of Korean Port Research
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• v.12 no.2
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• pp.313-322
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• 1998

The distributions of chemical oxygen demand (COD) and suspended solid (SS) in Ulsan Bay were simulated and reproduced by a numerical ecosystem model for the practical application to the management of marine water quality and the prediction of water quality change due to coastal developments or the constructions of breakwater and marine facilities. Comparing the computed with the observed data of COD and SS in Ulsan bay the results of simulation were found to be good enough to satisfy the practical applications.

• 한국태양에너지학회:학술대회논문집
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• 2009.04a
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• pp.184-189
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• 2009

In order to improve the operation of energy systems, it is necessary for the urban communities to have reliable optimization routines, both computerized and manual, implemented in their organizations. However, before a production plan for the energy system units can be constructed, a prediction of the energy systems first needs to be determined. So, several methodologies have been proposed for energy demand prediction, but due to uncertainties in urban community, many of them will fail in practice. The main topic of this paper has been the development of a method for energy demand prediction at urban community. Energy demand prediction is important input parameters to plan for the energy planing. This paper presents a energy demand prediction method which estimates heat and electricity for various building categories. The method has been based on artificial neural networks(ANN). The advantage of ANN with respect to the other method is their ability of modeling a multivariable problem given by the complex relationships between the variables. Also, the ANN can extract the relationships among these variables by means of learning with training data. In this paper, the ANN have been applied in oder to correlate weather conditions, calendar data, schedules, etc. Space heating, cooling, hot water and HVAC electricity can be predicted using this method. This method can produce 10% of errors hourly load profile from individual building to urban community.

• Journal of Korean Society of Water and Wastewater
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• v.23 no.1
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• pp.57-67
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• 2009

Under the supply-oriented policy, efficiency and rationale have not been fully considered in planning of water supply facilities in Korea. As a case, this study shows that large-size systems are suffering from overcapacity problem of water treatment plants, and thus discusses what options should be applied to deal with inefficiency. Water demand of large-size systems has suddenly decreased for the last 10 years while water demand has been often assumed to increase at a regular rate in planning of plants according to excess capacity hypothesis. This inconsistency led to a serious overcapacity. In 2006, total excess capacity of nine large-size systems was more than 1.2 times as large as maximum daily demand of total customers in Seoul. However, their options are expected to stay ex post facto. To prepare the risk of overcapacity, and draw large benefits out of the plants, the authors and other professionals in Korea should further discuss the more adaptive method for prediction of water demand, and systems integration between a large-size system and adjoining systems.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.15 no.2
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• pp.38-48
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• 2001

Kalman Filter model of demand for residental water and consumption pattern wore tested for their ability to explain the hourly residental demand for water in metro-politan distribution system. The daily residental demand can be obtained from Kalman Filter model which is optimized by statistical analysis of input variables. The hourly residental demand for water is calculated from the daily residental demand and consumption pattern. The consumption pattern which has 24 time rates is characterized by data granulization in accordance with season kind, weather and holiday. The proposed approach is applied to water distribution system of metropolitan areas in Korea and its effectiveness is checked.

• Journal of Korean Society of Water and Wastewater
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• v.22 no.1
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• pp.23-29
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• 2008

Throughout the analysis of field data from water distribution system, valid parameters were determined that can be included in the water service and design plan. This study investigates water consumption patterns to understand the variation of water-demand structures utilizing the pattern analysis of domestic purpose water. Water use data were collected by a public water resources management firm in Korea, Kwater, for 140 houses monitored during three years. Flow meters were installed at the faucet for drinking water, the shower booth, the laundry machine, bathroom sink, toilet, and garden faucet. Data was filtered using multiple physically meaningful criteria to improve analysis credibility. Mann Kendall and Spearman's Rho tests were used to carry out the analysis. Distinct factors of water consumption patterns can be determined for both increasing and decreasing trends of water use. Throughout the data analysis, the characterization of terms was classified and analyzed by the condition of the location of water-demand. Analysis of this data provide a physical basis for the parameter configuration of a reasonable design for a domestic water demand prediction model.

• Journal of Korea Water Resources Association
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• v.45 no.11
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• pp.1187-1199
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• 2012

A hybrid forecasting scheme based on wavelet decomposition coupled to a support vector machine model is presented for water demand series that exhibit nonlinear behavior. The use of wavelet transform followed by the SVM model of each leading component is explored as a model for water demand data. The proposed forecasting model yields better results than a traditional ARIMA time series forecasting model in terms of self-prediction problem as well as reproducing the properties of the observed water demand data by making use of the advantages of wavelet transform and SVM model. The proposed model can be used to substantially and significantly improve the water demand forecasting and utilized in a real operation.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.7
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• pp.489-497
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• 2003

A water management system is developed to reduce the unit cost of production in wide-area waterworks. Improving productivity in waterworks is to save power rate. We suggest a method to schedule the supply of water according to the time-varying power rate and pump control scheme. Water pipeline analysis package (SynerGEE Water) is utilized to obtain optimal pump control solution adaptation to water demand. Our evaluation results show that developed scheme is more efficient than the conventional.

• Journal of Korean Society on Water Environment
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• v.21 no.6
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• pp.617-623
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• 2005

Forestry and agricultural land uses constitute 85% of Korea and these land uses are typically mixed in many watersheds. Biological Oxygen Demand (BOD) concentration is a primary factor for managing water qualities of the water resources in Korea. BOD loadings from diffuse sources, however, not well monitored yet. This study aims to assess BOD loadings from diffuse sources and their affecting factors to conserve quality of water resources. Event Mean Concentration (EMC) of BOD was calculated based on the monitoring data of forty rainfall events at four agricultural-forestry watersheds. Exceedence cumulative probability of BOD EMCs were plotted to show agricultural activities in a watershed impacts on the magnitude of EMCs. Prediction equation for each rainfall event was proposed to estimate BOD EMCs: $EMC_{BOD}(mg/L)=EXP(0.413+0.0000001157{\times}$(discharged runoff volume in $m^3$)+0.018${\times}$(ratio of agricultural land use to total watershed area).

• Journal of Korea Water Resources Association
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• v.52 no.7
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• pp.505-514
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• 2019

Our country is making efforts to manage water resources efficiently. In the future, It is necessary to develop a plan after subdividing the basin considering regional problems and water use, topographical and climatic characteristics. This study constructed water supply and demand system based on the standard watershed unit for water shortage evaluation considering spatial resolution. In addition, water shortage were calculated and compared using the MODSIM model in the Han-river basin. As a result, the average water shortage occurring during the 49 years (1967-2015) was 129.98 million $m^3$ for the middle watershed unit and 222.24 million $m^3$ for the standard watershed unit, resulting in a difference of about 2.1 billion m3. However, the trends and distribution of water shortage occurrence were very similar. The reason for this is that, in the case of the Middle watershed unit analysis, water shortages are calculated for the demand for living, industrial, and agricultural water for the representative natural flow value, assuming that all the water can be used in basin. The standard basin unit analysis showed that the difference between the fractionated supply and demand resulted in a large water shortage due to the relatively small amount of available water, and that the main stream did not show water shortage due to the ripple effect of the return flow. If the actual water use system is considered in the model as well as the subdivision of the spatial unit, it will be possible to evaluate the water supply and demand reflecting the regional characteristics.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1508-1520
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• 2018

In this paper, we propose an improved model to provide users with a better long-term prediction of waterworks operation data. The existing prediction models have been studied in various types of models such as multiple linear regression model while considering time, days and seasonal characteristics. But the existing model shows the rate of prediction for demand fluctuation and long-term prediction is insufficient. Particularly in the deep running model, the long-short-term memory (LSTM) model has been applied to predict data of water purification plant because its time series prediction is highly reliable. However, it is necessary to reflect the correlation among various related factors, and a supplementary model is needed to improve the long-term predictability. In this paper, convolutional neural network (CNN) model is introduced to select various input variables that have a necessary correlation and to improve long term prediction rate, thus increasing the prediction rate through the LSTM predictive value and the combined structure. In addition, a multiple linear regression model is applied to compile the predicted data of CNN and LSTM, which then confirms the data as the final predicted outcome.