• Journal of the Korean Solar Energy Society
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• v.33 no.4
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• pp.23-30
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• 2013

This paper presents the determination method of the hybrid energy storage capacity for compensating the output of wind power when disconnecting from the grid. In the wind power output compensation, a lot of charging and discharging time with lithium-ion battery will be deteriorated the life time. And also, this fluctuation will cause some problems of the power quality and power system stability. To solve these kind of problems, many researchers in the world have been studied with BESS(Battery Energy Storage System) in the wind farm. But, BESS has the limitation of its output during very short term period, this means that it is difficult to compensate the very short term output of wind farm. Using the EDLC (Electric Double Layer Capacitor), it is possible to solve the problem. Installing the battery system in the wind farm, it will be possible to decrease the total capacity of BESS consisting of HESS (Hybrid Energy Storage System). This paper shows simulation results when not only BESS is connected to wind farm but also to HESS. To verify the proposed system, results of computer simulation using PSCAD/EMTDC program with actual output data of wind farms of Jeju Island will be presented.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.19-28
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• 2022

Recently, cryptocurrency has attracted much attention, and price prediction studies of cryptocurrency have been actively conducted. Especially, efforts to improve the prediction performance by applying the deep learning model are continuing. LSTM (Long Short-Term Memory) model, which shows high performance in time series data among deep learning models, is applied in various views. However, it shows low performance in cryptocurrency price data with high volatility. Although, to solve this problem, new input features were found and study was conducted using them, there is a lack of study on input features that drop predictive performance. Thus, in this paper, we collect the recent trends of six cryptocurrencies including Bitcoin and Ethereum and analyze effects of input features on the cryptocurrency price predictive performance through statistics and deep learning. The results of the experiment showed that cryptocurrency price predictive performance the best when open price, high price, low price, volume and price were combined except for rate of closing price fluctuation.

• Journal of Korea Trade
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• v.25 no.4
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• pp.17-36
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• 2021

Purpose - This study provides useful information to stakeholders by forecasting the tramp shipping market, which is a completely competitive market and has a huge fluctuation in freight rates due to low barriers to entry. Moreover, this study provides the most effective parameters for Baltic Dry Index (BDI) prediction and an optimal model by analyzing and comparing deep learning models such as the artificial neural network (ANN), recurrent neural network (RNN), and long short-term memory (LSTM). Design/methodology - This study uses various data models based on big data. The deep learning models considered are specialized for time series models. This study includes three perspectives to verify useful models in time series data by comparing prediction accuracy according to the selection of external variables and comparison between models. Findings - The BDI research reflecting the latest trends since 2015, using weekly data from 1995 to 2019 (25 years), is employed in this study. Additionally, we tried finding the best combination of BDI forecasts through the input of external factors such as supply, demand, raw materials, and economic aspects. Moreover, the combination of various unpredictable external variables and the fundamentals of supply and demand have sought to increase BDI prediction accuracy. Originality/value - Unlike previous studies, BDI forecasts reflect the latest stabilizing trends since 2015. Additionally, we look at the variation of the model's predictive accuracy according to the input of statistically validated variables. Moreover, we want to find the optimal model that minimizes the error value according to the parameter adjustment in the ANN model. Thus, this study helps future shipping stakeholders make decisions through BDI forecasts.

• Journal of Korea Water Resources Association
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• v.55 no.8
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• pp.565-575
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• 2022

Recently, in the field of water resource engineering, interest in predicting time series water levels and flow rates using deep learning technology that has rapidly developed along with the Fourth Industrial Revolution is increasing. In addition, although water-level and flow-rate prediction have been performed using the Long Short-Term Memory (LSTM) model and Gated Recurrent Unit (GRU) model that can predict time-series data, the accuracy of flow-rate prediction in rivers with rapid temporal fluctuations was predicted to be very low compared to that of water-level prediction. In this study, the Paldang Bridge Station of the Han River, which has a large flow-rate fluctuation and little influence from tidal waves in the estuary, was selected. In addition, time-series data with large flow fluctuations were selected to collect water-level and flow-rate data for 2 years and 7 months, which are relatively short in data length, to be used as training and prediction data for the LSTM and GRU models. When learning time-series water levels with very high time fluctuation in two models, the predicted water-level results in both models secured appropriate accuracy compared to observation water levels, but when training rapidly temporal fluctuation flow rates directly in two models, the predicted flow rates deteriorated significantly. Therefore, in this study, in order to accurately predict the rapidly changing flow rate, the water-level data predicted by the two models could be used as input data for the rating curve to significantly improve the prediction accuracy of the flow rates. Finally, the results of this study are expected to be sufficiently used as the data of flood warning system in urban rivers where the observation length of hydrological data is not relatively long and the flow-rate changes rapidly.

• Korean Journal of Optics and Photonics
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• v.35 no.4
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• pp.143-149
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• 2024

We analyze the effect of atmospheric fluctuation on laser beam propagation, using a single-phase screen model and a multi-phase screen model. When a laser beam (wavelength 1064 nm, radius 10 mm, collimated by 25.4 mm optics) propagates 3 km, atmospheric fluctuation with structure constant C_n² in the range of 10^-17 to 10^-14 is generated by the single- and multi-phase screen models. The results of short-term and long-term exposures are analyzed in terms of the beam profile, power in the bucket, and beam radius at the receiver plane. The power in the bucket and beam radius increase as the structure constant increases. When the structure constant is less than 2×10^-15, the results of the single- and multi-phase screen models are similar, within a difference of 1.5 %. However, when the structure constant is greater than 2×10^-15, the difference between the two models increases, and the multi-phase screen model is appropriate under this condition.

• KDI Journal of Economic Policy
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• v.14 no.1
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• pp.121-145
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• 1992

This paper examines characteristics of time series data related to the construction investment(stationarity and time series components such as secular trend, cyclical fluctuation, seasonal variation, and random change) and surveys predictibility, fitness, and explicability of independent variables of various models to build a short-term construction investment forecasting model suitable for current economic circumstances. Unit root test, autocorrelation coefficient and spectral density function analysis show that related time series data do not have unit roots, fluctuate cyclically, and are largely explicated by lagged variables. Moreover it is very important for the short-term construction investment forecasting to grasp time lag relation between construction investment series and leading indicators such as building construction permits and value of construction orders received. In chapter 3, we explicate 7 forecasting models; Univariate time series model (ARIMA and multiplicative linear trend model), multivariate time series model using leading indicators (1st order autoregressive model, vector autoregressive model and error correction model) and multivariate time series model using National Accounts data (simple reduced form model disconnected from simultaneous macroeconomic model and VAR model). These models are examined by 4 statistical tools that are average absolute error, root mean square error, adjusted coefficient of determination, and Durbin-Watson statistic. This analysis proves two facts. First, multivariate models are more suitable than univariate models in the point that forecasting error of multivariate models tend to decrease in contrast to the case of latter. Second, VAR model is superior than any other multivariate models; average absolute prediction error and root mean square error of VAR model are quitely low and adjusted coefficient of determination is higher. This conclusion is reasonable when we consider current construction investment has sustained overheating growth more than secular trend.

• Korean Journal of Construction Engineering and Management
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• v.5 no.6 s.22
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• pp.203-211
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• 2004

This study is the results to survey on the problems and improvable Policies for current escalation system in construction contracts, through a Delphi survey to experts. From the survey results, it is desirable to decide the fluctuation rate of construction cost, which is the requirement of escalation clause, on the basis of inflation rate or construction cost index. The desirable price fluctuation rate is proposed as a $3\%$ level. However, it is difficult for construction companies to cope with the sudden increase of material price in advance, arising from short-term shock factors such as exchange rate and international raw material's price. Accordingly escalation system for specified materials, as an exceptional mode, should be introduced. As a method to calculate the fluctuation rate, ARCA(adjustment rate for the categories of articles) is more desirable than ARI(adjustment rate for an index), because the ARCA can be more reflected the characteristics of each construction work.To rationalize the ARI method, it is needed to announce the wage index, material index and machinery expense index via detailed classification by construction types. Also, it is desirable to prescribe the bidding date as a starting date of the price change, rather than contact signing date. considering the price change can happen since the biddiilg stage.

• International Commerce and Information Review
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• v.7 no.3
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• pp.269-286
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• 2005

The exchange rate volatility has been increased since the time when the floating exchange rate system was introduced in Korea. As a result, the increase of the exchange rate volatility raised the risk in international trades in Korea. The purpose of this study in to study the feature of exchange rate volatility and the main sources of its increase and to confirm whether the exchange rate volatility influence export volume and price of Korea. In the first place, I measured exchange rate volatility with two methods. The one is descriptive statistic method such as the width of daily exchange rate fluctuation and the rate of exchange rate devaluation. The other is the time varying conditional variance of exchange rate. Then, I studied the sources of exchange rate volatility. In the second place, I defined the exchange rate volatility as the time varying conditional variance and estimated it by using elastic a approach model which shows exchange rate is affected by itself and its conditional variance, I estimated its effects on export volumes and prices of electric home appliances, information & communication equal and semi-conductor. The result of this study is as follows. With presumed result EU and Korea because is not the goods which is to substantial competition relationship, The effect where the relative value change of presumed result expression anger and the dollar of import and export function goes mad to the import and export of Korea the income compared to is to export and it is appearing a lot. The EU goods is sold more expensively the Korean goods than from about length being caused by American market of the dollar and the balance of trade of Korea is visible like being visible the improvement of single breadth. Because the relationship of competition is weak but substantially there is to a short term and expression - the effect where the dollar rate fluctuation is big in Korean trade there is a possibility of saying that widely known it is not.

• Journal of Navigation and Port Research
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• v.35 no.9
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• pp.765-776
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• 2011

For container shipping company, fuel oil prise is a considerable expense. Since 2008, fuel oil prises have risen dramatically. An increasing fuel oil price in container shipping, in the short term, is only partially compensated through surcharges and may affect earnings negatively. This study discusses the impact of an increasing fuel oil price and capital costs for vessels on the Asia-Europe trade of 'H' Shipping Company. According to the result of 'H' carrier's operation in 2008, there were no cost differences between 8 and 9 vessels operations in case of fuel oil price with USD 169/tons while adopting USD 31,818 as a fixed cost. We can expect that the fuel oil price will not go lower than USD 200/Ton on the basis of current high oil price phenomenon. When the fuel oil price is over USD 200/ton, 9 vessel operation is more economic than 8 vessel operation even if the fixed cost is over USD 35,000.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.83-84
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• 2008

Voltage fluctuation, also known as flicker, is a power quality problem caused by nonlinear loads like electric arc furnace. Since it is interpreted as a variation of the supplied electrical energy, it causes the residential customers to feel much annoyed visually through the lamps. Due to the statistical nature of IEC (International Electrotechnical Commission) short-term flicker severity index, Pst, it is not feasible to pre-evaluate the flicker level using the transient power system simulators such as Sim Power System in Matlab/Simulink. So this paper presents not only how to design the Matlab/Simulink IEC flickermeter to yield the Pst value, which considering electric distribution environments of South Korea, but also how to mitigate the voltage flicker at the Point of Common Coupling (PCC). In order to achieve this, the flicker mitigation efficiencies of various compensating devices, such as Static Var Compensator (SVC), STATCOM will be applied and compared. The simulated result demonstrates which compensating equipment is the most efficient method to mitigate the flicker phenomenon.