• Journal of Korean Society on Water Environment
• /
• v.20 no.6
• /
• pp.685-691
• /
• 2004

Water quality forecasting with long term flow is important for management and operation of river environment. However, it is difficult to set up and operate a physical model for water quality forecasting due to large uncertainty in the data required for model setting. Therefore, relatively simpler stochastic approaches are adopted for this problem. In this study we try several multivariate time series models such as ARMAX models for the possible substitute for water quality forecasting. Those models are applied to the BOD and COD levels at Noryangin station, Han river, and also evaluated the effect of release from Paldang dam on them. Monthly BOD and COD data from 1985 to 1991 (7 years) are used for model building and another two year data for model testing. As a result of the study, the effect of improvement on water quality is much more effective combining with the water quality improvement of dam release than considering only increment of dam release in the downstream Han river.

• International conference on construction engineering and project management
• /
• 2011.02a
• /
• pp.1-6
• /
• 2011

As large-scale building projects have recently increased for the residential, commercial and office facilities, construction costs for these projects have become a matter of great concern, due to their significant construction cost implications, as well as unpredictable market conditions and fluctuations in the rate of inflation during the projects' long-term construction periods. In particular, recent volatile fluctuations of construction material prices fueled such problems as cost forecasting. This research develops a time series model using the Box-Jenkins approach and material price time series data in Korea in order to forecast trends in the unit prices of required materials. Building information modeling (BIM) approaches are also used to analyze injection times of construction resources and to conduct quantity take-off so that total material prices can be forecast. To determine an optimal time series model for forecasting price trends, comparative analysis of predictability of tentative autoregressive integrated moving average (ARIMA) models is conducted. The proposed BIM-based time series forecasting model can help to deal with sudden changes in economic conditions by estimating material prices that correspond to resource injection times.

• Health Policy and Management
• /
• v.18 no.4
• /
• pp.85-110
• /
• 2008

In this study, we explore the long-term demand forecasting of high-price medical equipments based on logistic and Bass diffusion model. We analyze the specific pattern of each equipment's diffusion curve by interpreting the parameter estimates of Bass diffusion model. Our findings are as follows. First, ultrasonic imaging system, CT are in the stage of maturity and so, the future demands of them are not too large. Second, medical image processing unit is between growth stage and maturity stage and so, the demand is expected to increase considerably for two or three years. Third, MRI is in the stage of take-off and Mammmography X-ray system is in the stage of maturity but, estimates of the potential number of adopters based on logistic model is considerably different to that based on Bass diffusion model. It means that additional data for these two equipments should be collected and analyzed to obtain the reliable estimates of their demands. Fourth, medical image processing unit have the largest q value. It means that the word-of-mouth effect is important in the diffusion of this equipment. Fifth, for MRI and Ultrasonic system, q/p values have the relatively large value. It means that collective power has an important role in adopting these two equipments.

• Journal of Internet Computing and Services
• /
• v.21 no.5
• /
• pp.119-127
• /
• 2020

As the electric vehicle (EV) market in South Korea grows, it is required to expand charging facilities to respond to rapidly increasing EV charging demand. In order to conduct a comprehensive facility planning, it is necessary to forecast future demand for electricity and systematically analyze the impact on the load capacity of facilities based on this. In this paper, we design and develop a Long Short-Term Memory (LSTM) neural network model that predicts the daily peak electric load at each charging station using the EV charging data of KEPCO. First, we obtain refined data through data preprocessing and outlier removal. Next, our model is trained by extracting daily features per charging station and constructing a training set. Finally, our model is verified through performance analysis using a test set for each charging station type, and the limitations of our model are discussed.

• KIEAE Journal
• /
• v.11 no.3
• /
• pp.63-68
• /
• 2011

Building deterioration would be proceeded by various causes such as physical, social, economic degradation. The deterioration would be inevitably prevented or delayed to get the decent function and performance in various building part and components. The maintenance and management are continued to provide the decent living condition for the household. The maintenance means mainly a repair, including the on-time and longterm plan. The longterm repair would be conducted by the systemic preparation in management activity and a required cost. Therefore, the annual due for the longterm repair plan is important to prepare the repair cost in a required time. In this paper, it aimed at analyzing the longterm repair cost and modelling to forecast the required cost in total area, number of household and time elapse in apartment housing. The estimation model of a repair cost is used with a power function which has a good statistics. Results of this study are shown that the sample has a longterm repair due in a $2,032won/m^2{\cdot}yr$ averagely which is higher than $912won/m^2{\cdot}yr$ in domestic. Second, the longterm repair due is proportionally correlated with the time elapse in both a total area and the number of household. Third, the estimation model for the longterm repair amount is suitable for the power function which is most in any other estimation models. Fourth, the ration of the longterm plan repair due a year to the cumulated longterm amount is about 26%.

• KIPS Transactions on Computer and Communication Systems
• /
• v.6 no.7
• /
• pp.297-306
• /
• 2017

Citizens want more accurate forecast information using Bus Information System. However, most bus information systems that use an average based short-term prediction algorithm include many errors because they do not consider the effects of the traffic flow, signal period, and halting time. In this paper, we try to improve the precision of forecast information by analyzing the influencing factors of the error, thereby making the convenience of the citizens. We analyzed the influence factors of the error using BIS data. It is shown in the analyzed data that the effects of the time characteristics and geographical conditions are mixed, and that effects on halting time and passes speed is different. Therefore, the halt time is constructed using Generalized Additive Model with explanatory variable such as hour, GPS coordinate and number of routes, and we used Hidden Markov Model to construct a pattern considering the influence of traffic flow on the unit section. As a result of the pattern construction, accurate real-time forecasting and long-term prediction of route travel time were possible. Finally, it is shown that this model is suitable for travel time prediction through statistical test between observed data and predicted data. As a result of this paper, we can provide more precise forecast information to the citizens, and we think that long-term forecasting can play an important role in decision making such as route scheduling.

• KIPS Transactions on Software and Data Engineering
• /
• v.9 no.12
• /
• pp.419-430
• /
• 2020

This paper investigated methods to improve the forecasting accuracy of the electricity consumption prediction model. Currently, the demand for electricity has continuously been rising more than ever. Since the industrial sector uses more electricity than any other sectors, the importance of a more precise forecasting model for manufacturing sites has been highlighted to lower the excess energy production. We propose a double encoder-decoder model, which uses two separate encoders and one decoder, in order to adapt both long-term and short-term data for better forecasts. We evaluated our proposed model on our electricity power consumption dataset, which was collected in a manufacturing site of Sehong from January 1st, 2019 to June 30th, 2019 with 1 minute time interval. From the experiment, the double encoder-decoder model marked about 10% reduction in mean absolute error percentage compared to a conventional encoder-decoder model. This result indicates that the proposed model forecasts electricity consumption more accurately on manufacturing sites compared to an encoder-decoder model.

• The Journal of Bigdata
• /
• v.4 no.1
• /
• pp.63-72
• /
• 2019

For power energy, optimal generation and distribution plans based on accurate demand forecasts are necessary because it is not recoverable after they have been delivered to users through power generation and transmission processes. Failure to predict power demand can cause various social and economic problems, such as a massive power outage in September 2011. In previous studies on forecasting power demand, ARIMA, neural network models, and other methods were developed. However, limitations such as the use of the national average ambient air temperature and the application of uniform criteria to distinguish seasonality are causing distortion of data or performance degradation of the predictive model. In order to improve the performance of the power demand prediction model, we divided Korea into five major regions, and the power demand prediction model of the linear regression model and the neural network model were developed, reflecting seasonal characteristics through regional characteristics and migration period learning techniques. With the proposed approach, it seems possible to forecast the future demand in short term as well as in long term. Also, it is possible to consider various events and exceptional cases during a certain period.

• Korean Management Science Review
• /
• v.25 no.3
• /
• pp.27-43
• /
• 2008

This study analyzes the changes of Gyeongju local society because of setting up low and intermediate level radioactive waste disposal site by using urban dynamics model. Specifically, after examining 'Gyeongju Long-Term Development Plan' announced in 2007, I establish the number of industries, population, gross local product, residents' income, and the long term employment condition as essential change-causing factors in Gyeongju local society based on the Big3 government project, and forecast it by using 'Gyeongju long-Term Development Plan' and all sorts of statistical data. In this stage, I assume 3 scenarios(basic, optimistic, and pessimistic view) to estimate the changes of local society more exquisitely, and scenarios are composed through mediation about variables of a growth rate and an inflow or outflow rate. The result shows that Gyeonaju local society would have growing changes by 2020. The essential change-causing factors are as follows. The case of population is estimated that it starts going down at the level of approximately 270 thousand by 2009, starts going up continuously after 2009, the year of completion of low and intermediate level radioactive waste disposal site, and increases from the level of about 300 thousand as minimum to 340 thousand as maximum in 2020. The estimates of other cases are made that the number of Industries has about 10 thousand increases, gross local product has almost 6 trillion increases, nominal gross national income doubles, as well as residences have approximately 280 thousand increases, and also made that employment condition also improves continuously, and diffusion ratio of house starts going up but the amount of supplies is a little bit insufficient in the long view.

• Journal of Satellite, Information and Communications
• /
• v.12 no.3
• /
• pp.22-34
• /
• 2017

The K-DRUM(K-water hydrologic & hydraulic Distributed RUnoff Model), a distributed rainfall-runoff model of K-water, calculates predicted runoff and water surface level of a dam using precipitation data. In order to obtain long-term hydrometeorological information, K-DRUM requires long-term weather forecast. In this study, we built a system providing long-term hydrometeorological information using predicted rainfall ensemble of GloSea5(Global Seasonal Forecast System version 5), which is the seasonal meteorological forecasting system of KMA introduced in 2014. This system produces K-DRUM input data by automatic pre-processing and bias-correcting GloSea5 data, then derives long-term inflow predictions via K-DRUM. Web-based UI was developed for users to monitor the hydrometeorological information such as rainfall, runoff, and water surface level of dams. Through this UI, users can also test various dam management scenarios by adjusting discharge amount for decision-making.