• 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 Navigation and Port Research
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• v.35 no.5
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• pp.387-392
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• 2011

The value of Photovoltaic as an independent power supply is small, but the city's carbon emissions reduction and for the reduction of fossil fuel use distributed power is the power source to a very high value. However, according to the weather conditions for solar power generation by power fluctuations because of the size distribution to be effective, the big swing for effectively controlling real-time monitoring should be made. But that depends on solar power generation solar radiation forecasts from the National Weather Service does not need to predict it, and this study, the diffuse sky radiation in the history of the solar radiation in the darkness of the clouds, thick and weather forecasts can be inferred from the atmospheric transmittance to announce this value is calculated to represent each weather forecast solar radiation and solar radiation predicted by substituting the expression And the measured solar radiation and CRM (Cloud Cover Radiation Model) technique with an expression of Kasten and Czeplak irradiation when compared to the calculated predictions were verified.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.8 no.2
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• pp.121-131
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• 2003

We have found that the east coast of Korea has had few sea fogs on January, February, November and December for the past 20 years by the analysis of monthly fog frequency and duration time. These phenomena appear to relate to the topographical characteristics of which the Taebaek Mountains descends toward the east to bar the radiation fog. On the other hand, the cause of occurring the spring and summer fog which has 90% of the whole frequency is divided into three cases. The first is the steam fog caused by the advection of the northeast cold air current on the East Sea due to the extension of Okhotsk High. The second is the advection fog caused by cooling and saturation of warm airmass advected on cold sea surface. And the last is the frontal fog caused by the supply of enough vapor due to the movement of low-pressure system and the advection of cold air behind a cold front. While, we simulate the sea fog for the period of the case studies by implementing fog prediction system(DUT-METRI) that makes it possible to forecast the fog in the vertical section of neighborhood of the East Sea and to predict the sea surface wind, relative humidity, ceiling height, visibility etc. Finally we verified this result by satellite image.

• Journal of the Society of Disaster Information
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• v.16 no.4
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• pp.842-848
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• 2020

Purpose: It is important to operate safely and economically in obsolescent power plant facilities. Economical operation is related in the balance of the supply and demand. Safety operation predicts the possible risks in the facilities and then, takes measures to the facilities. For the monitoring of the power plant facilities, we needs several kinds of the sensing system. From the sensors data, we can predict the possible risk. Method: We installed the acoustic, vibration, electric and smoke sensors in the power plant facilities. Using the data, we developed 3 kinds of prediction models, such as, demand prediction, plant engine abnormal prediction model, and risk prediction model. Results: Accuracy of the demand prediction model is over 90%. The other models make a stable operation of the system. Conclusion: For the sustainable operation of the obsolescent power plant, we developed 3 kinds of AI prediction models. The model apply to JB company's power plant facilities.

• Asia-Pacific Journal of Business
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• v.11 no.4
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• pp.189-203
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• 2020

Purpose - This study aims in analyzing the dynamic relationship between household loans and housing prices according to the characteristics of depository institutions after the financial crisis, identifying the recent trends between them, and making policy suggestions for stabilizing house prices. Design/methodology/approach - The monthly data used in this study are household loans, household loan interest rates, and housing prices ranging from January 2012 to May 2020, and came from ECOS of the Bank of Korea and Liiv-on of Kookmin Bank. This study used vector auto-regression, generalized impulse response function, and forecast error variance decomposition with the data so as to yield analysis results. Findings - The analysis of this study no more shows that the household loan interest rates in both deposit banks and non-bank deposit institutions had statistically significant effects on housing prices. Also, unlike the previous studies, there was statistically significant bi-directional causality between housing prices and household loans in neither deposit banks nor non-bank deposit institutions. Rather, it was found that there is a unidirectional causality from housing prices to household loans in deposit banks, which is considered that housing prices have one-sided effects on household loans due to the overheated housing market after the financial crisis. Research implications or Originality - As a result, Korea's housing market is closely related to deposit banks, and housing prices are acting as more dominant information variables than interest rates or loans under the long-term low interest rate trend. Therefore, in order to stabilize housing prices, the housing supply must be continuously made so that everyone can enjoy housing services equally. In addition, the expansion and reinforcement of the social security net should be realized systematically so as to stop households from being troubled with the housing price decline.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1005-1012
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• 2021

Recently, the use of various location-based services-based location information systems using maps on the web has been expanding, and there is a need for a monitoring system that can check power demand in real time as an alternative to energy saving. In this study, we developed a deep learning real-time virtual power demand prediction web system using open source-based mapping service to analyze and predict the characteristics of power demand data using deep learning. In particular, the proposed system uses the LSTM(Long Short-Term Memory) deep learning model to enable power demand and predictive analysis locally, and provides visualization of analyzed information. Future proposed systems will not only be utilized to identify and analyze the supply and demand and forecast status of energy by region, but also apply to other industrial energies.

• Journal of IKEEE
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• v.26 no.3
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• pp.355-363
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• 2022

Korea is pursuing a plan to switch and expand energy sources with a focus on renewable energy with the goal of becoming carbon neutral by 2050. As the instability of energy supply increases due to the intermittent nature of renewable energy, accurate prediction of the amount of renewable energy generation is becoming more important. Therefore, the government has opened a small-scale power brokerage market and is implementing a system that pays settlements according to the accuracy of renewable energy prediction. In this paper, a prediction model was implemented using a statistical model and an artificial intelligence model for the prediction of solar power generation. In addition, the results of prediction accuracy were compared and analyzed, and the revenue from the settlement amount of the renewable energy generation forecasting system was estimated.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.4
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• pp.11-20
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• 2022

Currently, the operation rule of agricultural reservoirs in case of drought events follows the drought forecast warning standard of agricultural water supply. However, it is difficult to preemptively manage drought in individual reservoirs because drought forecasting standards are set according to average reservoir storage ratio such as 70%, 60%, 50%, and 40%. The equal standards based on average water level across the country could not reflect the actual drought situation in the region. In this study, we proposed the improvement of drought operation rule for agricultural reservoirs based on the percentile approach using past water level of each reservoir. The percentile approach is applied to monitor drought conditions and determine drought criteria in the U.S. Drought Monitoring (USDM). We applied the drought operation rule to reservoir storage rate in extreme 2017 spring drought year, the one of the most climatologically driest spring seasons over the 1961-2021 period of record. We counted frequency of each drought criteria which are existing and developed operation rules to compare drought operation rule determining the actual drought conditions during 2016-2017. As a result of comparing the current standard and the percentile standard with SPI6, the percentile standard showed severe-level when SPI6 showed severe drought condition, but the current standard fell short of the results. Results can be used to improve the drought operation criteria of drought events that better reflects the actual drought conditions in agricultural reservoirs.

• New & Renewable Energy
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• v.18 no.3
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• pp.1-9
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• 2022

This study analyzed the anticipated supply-and-demand of forest biomass energy (through wood pellets) until 2050, in South Korea. Comparing the utilization rates of forest resources of five countries (United Kingdom, Germany, Finland, Japan, and S. Korea), it was found that S. Korea does not nearly utilize its forest resources for energy purposes. The total demand for wood pellets in S. Korea (based on a power generation efficiency of 38%) was predicted to be 3,629 and 4,371 thousand tons in 2034 and 2050, respectively. The anticipated total wood pellet power generation ratio to target power consumption is 1.13% (5,745 GWh), 1.17% (6,336 GWh), and 1.25% (7,631 GWh) in 2020, 2030, and 2050, respectively. Low value-added forest residues left unattended in forests are called "Unused Forest Biomass" in S. Korea. From the analysis, the total annual potential amount of raw material, sustainably collectible amount, and available amount of wood pellet in 2050 were estimated to be 6,877, 4,814, and 3,370 thousand tons, respectively. The rate of contribution to Nationally Determined Contributions was up to 0.64%. Through this study, the authors found that forest biomass energy will contribute to a carbon neutral society in the near future at the national level.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.