• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.5
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• pp.248-254
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• 2001

The traction power demand highly varies with time and train positions and the traction load is a large-capacity current at single phase converted from 3-phase power system. Subsequently, each phase current converted from 3-phase power system cannot be maintained in balance any longer and thus the traction load can bring about imbalance in three-phase voltage. Therefore, the exact assessment of voltage unbalance must be carried out preferentially as well as load forecast at stages of designing and planning for electric railway system. The evaluation of unbalance voltage in areas, such as electric railway depots should be a prerequisite with more accuracy. The conventional researches on voltage unbalance have dealt with connection schemes of the transformers used in ac AT-fed electric railroads system and induced formulas to briefly evaluate voltage unbalance in the system(3). These formulas are still being used widely due to their easy applicabilities on voltage unbalance evaluation. Meanwhile, they don't take into account detailed characteristics of ac AT-fed electric railroads system, being founded on some assumptions. Accordingly. accuracy still remains in question. This paper proposes a new method to more effectively estimate voltage unbalance index. In this method, numerous diverted circuits in electric railway depots are categorized in three components and each component is defined as a two-port network model. The equivalent circuit for the entire power supply system is also described into a two-port network model by making parallel and/or series connections of these components. Efficiency and accuracy in voltage unbalance calculation as well can be promoted by simplifying the circuits into two-port network models.

• Journal of Korean Society of Forest Science
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• v.84 no.2
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• pp.121-130
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• 1995

Forecasting accuracy is examined in the context of Michigan travel demand. Eight different annual models are used to forecast up to two years ahead, and nine different quarterly models up to four quarters. In the evaluation of annual models' performance, multiple regression performed better than the other methods in both the one year and two year forecasts. For quarterly models, Winters exponential smoothing and the Box-Jenkins method performed better than naive 1 s in the first quarter ahead, but these methods in the second, third, and fourth quarters ahead performed worse than naive 1 s. The sophisticated models did not outperform simpler models in producing quarterly forecasts. The best model, multiple regression, performed slightly better when fitted to quarterly rather than annual data: however, it is not possible to strongly recommend quarterly over annual models since the improvement in performance was slight in the case of multiple regression and inconsistent across the other models. As one would expect, accuracy declines as the forecasting time horizon is lengthened in the case of annual models, but the accuracy of quarterly models did not confirm this result.

• Journal of Korea Port Economic Association
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• v.39 no.2
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• pp.1-13
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• 2023

This study forecasted the manpower demand of eco-friendly smart shipbuilding, whose importance and weight are increasing according to the environmental regulations of the IMO and the spread of the 4th industrial revolution technology. It predicted the shipbuilding industry manpower by applying various models of trend analysis and time series analysis based on data from 2000 to 2020 of Statistics Korea. It was found that the prediction applying geometric mean had the smallest gap among the trend and time series analysis methods in comparing between forecast results and actual data for the past 5 years. Therefore, the demand for manpower in the shipbuilding industry was predicted by using the geometric mean method. In addition, the manpower demand of smart eco-friendly ships wast forecasted by using the 2018 and 2020 manpower survey results of the Ministry of Trade, Industry and Energy and reflecting the trend of manpower increase in the shipbuilding industry. The result of forecasting showed that 62,001 person in 2025 and 85,035 people in 2030. This study is expected to contribute to the adjustment of manpower supply and demand and the training professional manpower in the future by increasing the accuracy of forecasting for high value-added eco-friendly smart ships.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.1
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• pp.50-56
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• 2014

Accurate midterm load forecasting is essential to preventive maintenance programs and reliable demand supply programs. This paper describes a midterm load forecasting method using autoregressive integrated moving average (ARIMA) model which has been widely used in time series forecasting due to its accuracy and predictability. The various ARIMA models are examined in order to find the optimal model having minimum error of the midterm load forecasting. The proposed method is applied to forecast 104-week load pattern using the historical data in Korea. The effectiveness of the proposed method is evaluated by forecasting 104-week load from 2011 to 2012 by using historical data from 2002 to 2010.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.763-768
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• 2023

In this paper, the existing statistical method (ARIMA) and machine learning method (Informer) were developed and compared to predict the distribution volume of pharmaceuticals. It was found that a machine learning-based model is advantageous for daily data prediction, and it is effective to use ARIMA for monthly prediction and switch to Informer as the data increases. The prediction error rate (RMSE) was reduced by 26.6% compared to the previous method, and the prediction accuracy was improved by 13%, resulting in a result of 86.2%. Through this thesis, we find that there is an advantage of obtaining the best results by ensembleing statistical methods and machine learning methods. In addition, machine learning-based AI models can derive the best results through deep learning operations even in irregular situations, and after commercialization, performance is expected to improve as the amount of data increases.

• Journal of Internet of Things and Convergence
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• v.8 no.3
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• pp.55-61
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• 2022

In order to reduce greenhouse gases, the main culprit of global warming, the United Nations signed the Climate Change Convention in 1992. Korea is also pursuing a policy to expand the supply of renewable energy to reduce greenhouse gas emissions. The expansion of renewable energy development using solar power led to the expansion of wind power and solar power generation. The expansion of renewable energy development, which is greatly affected by weather conditions, is creating difficulties in managing the supply and demand of the power system. To solve this problem, the power brokerage market was introduced. Therefore, in order to participate in the power brokerage market, it is necessary to predict the amount of power generation. In this paper, the prediction system was used to analyze the Yonchuk solar power plant. As a result of applying solar insolation from on-site (Model 1) and the Korea Meteorological Administration (Model 2), it was confirmed that accuracy of Model 2 was 3% higher. As a result of comparative analysis of the DNN and RNN models, it was confirmed that the prediction accuracy of the DNN model improved by 1.72%.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.700-706
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• 2009

Bridges are vital components of any road network which demand crucial and timely decision-making for Maintenance, Repair and Rehabilitation (MR&R) activities. Bridge Management Systems (BMSs) as a decision support system (DSS), have been developed since the early 1990's to assist in the management of a large bridge network. Historical condition ratings obtained from biennial bridge inspections are major resources for predicting future bridge deteriorations via BMSs. Available historical condition ratings in most bridge agencies, however, are very limited, and thus posing a major barrier for obtaining reliable future structural performances. To alleviate this problem, the verified Backward Prediction Model (BPM) technique has been developed to help generate missing historical condition ratings. This is achieved through establishing the correlation between known condition ratings and such non-bridge factors as climate and environmental conditions, traffic volumes and population growth. Such correlations can then be used to obtain the bridge condition ratings of the missing years. With the help of these generated datasets, the currently available bridge deterioration model can be utilized to more reliably forecast future bridge conditions. In this paper, the prediction accuracy based on 4 and 9 BPM-generated historical condition ratings as input data are compared, using deterministic and stochastic bridge deterioration models. The comparison outcomes indicate that the prediction error decreases as more historical condition ratings obtained. This implies that the BPM can be utilised to generate unavailable historical data, which is crucial for bridge deterioration models to achieve more accurate prediction results. Nevertheless, there are considerable limitations in the existing bridge deterioration models. Thus, further research is essential to improve the prediction accuracy of bridge deterioration models.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.915-929
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• 2023

Improvements in people's quality of life, diversification of leisure activities, and changes in population structure have led to an increase in the demand for tourism and an expansion of the diversification of tourism activities. In particular, for coastal cities where land and marine tourism elements coexist, various factors influence their tourism demands. Tourism requires the construction of infrastructure and content development according to the demand at the tourist destination. This study aims to improve the prediction accuracy and explore influencing factors through time series analysis of tourism scale using agent-based data. Basic local governments in the Busan area were examined, and the data used were the number of tourists and the amount of tourism consumption on a monthly basis. The univariate time series analysis, which is a deterministic model, was used along with the SARIMAX analysis to identify the influencing factor. The tourism consumption propensity, focusing on the consumption amount according to business types and the amount of mentions on SNS, was set as the influencing factor. The difference in accuracy (RMSE standard) between the time series models that did and did not consider COVID-19 was found to be very wide, ranging from 1.8 times to 32.7 times by region. Additionally, considering the influencing factor, the tourism consumption business type and SNS trends were found to significantly impact the number of tourists and the amount of tourism consumption. Therefore, to predict future demand, external influences as well as the tourists' consumption tendencies and interests in terms of local tourism must be considered. This study aimed to predict future tourism demand in a coastal city such as Busan and identify factors affecting tourism scale, thereby contributing to policy decision-making to prepare tourism demand in consideration of government tourism policies and tourism trends.

• Journal of the Korea Society of Computer and Information
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• v.17 no.1
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• pp.211-218
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• 2012

Fossil fuel is used all over the world and it produces greenhouse gases due to fossil fuel use. Therefore, it cause global warming and is serious environmental pollution. In order to decrease the environmental pollution, we should use renewable energy which is clean energy. Among several renewable energy, wind energy is the most promising one. Wind power generation is does not produce environmental pollution and could not be exhausted. However, due to wind power generation has irregular power output, it is important to predict generated electrical energy accurately for smoothing wind energy supply. There, we consider use ramp characteristic to forecast accurate wind power output. The ramp increase and decrease rapidly wind power generation during in a short time. Therefore, it can cause problem of unbalanced power supply and demand and get damaged wind turbine. In this paper, we make prediction models using power ramp rate as well as wind speed and wind direction to increase prediction accuracy. Prediction model construction algorithm used multilayer neural network. We built four prediction models with PRR, wind speed, and wind direction and then evaluated performance of prediction models. The predicted values, which is prediction model with all of attribute, is nearly to the observed values. Therefore, if we use PRR attribute, we can increase prediction accuracy of wind power generation.

• Journal of Korean Society of Water and Wastewater
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• v.27 no.6
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• pp.761-770
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• 2013

This study was carried out to forecast the flow rate and water quality at the inlet of the Saemangeum bay in Korea using the SWMM(Storm Water Management Model) and the WASP(Water Analysis Simulation Program), and to analyze the impacts of pollutant loading from non-point source on the water quality of the bay. The calibration and validation of flow rate and water quality were performed using those from two monitoring points in the Mankyeong river administrated by Korean Ministry of Environment as part of the national water quality monitoring network. When the river flow rate was calibrated and validated using the rainfall intensities during 2011-2012, $R^2$ (i.e., coefficient of determination) was ranged from 0.91 to 0.96. For water qualities, it was shown that $R^2$ of BOD(Biochemical Oxygen Demand) was ranged from 0.56 to 0.86, and $R^2$ of T-N(Total Nitrogen) was from 0.64 to 0.75, and $R^2$ of T-P(Total Phosphorus) was from 0.67 to 0.89. The integrated modeling system showed significant advances in the accuracy to estimate the water quality. Finally, further simulations showed that annual average flow of the river running into the bay was estimated to be $1.439{\times}10^9m^3/year$. The discharged load of BOD, T-N, and T-P into the bay were anticipated to be 618.7 ton/year, 331.5 ton/year, and 40.4 ton/year, respectively.