• Advances in Energy Research
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• v.6 no.2
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• pp.131-143
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• 2019

Global Solar Radiation (GSR) is the key element for performance estimation of any Solar Power Plant (SPP). Its forecasting may help in estimation of power production from a SPP well in advance, and may also render help in optimal use of this power. Seasonal Auto-Regressive Moving Average (SARMA) and Artificial Neural Network (ANN) models are combined in order to develop a hybrid model (SARMA-ANN) conceiving the characteristics of both linear and non-linear prediction models. This developed model has been used for prediction of GSR at Gorakhpur, situated in the northern region of India. The proposed model is beneficial for the univariate forecasting. Along with this model, we have also used Auto-Regressive Moving Average (ARMA), SARMA, ANN based models for 1 - 6 day-ahead forecasting of GSR on hourly basis. It has been found that the proposed model presents least RMSE (Root Mean Square Error) and produces best forecasting results among all the models considered in the present study. As an application, the comparison between the forecasted one and the energy produced by the grid connected PV plant installed on the parking stands of the University shows the superiority of the proposed model.

• KIEE International Transactions on Power Engineering
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• v.4A no.4
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• pp.262-267
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• 2004

With the advent of electric power systems moving from a vertically integrated structure to a deregulated environment, calculating reactive power service charges has become a new and challenging theme for market operators. This paper examines various methods for reactive power management adopted throughout various deregulated foreign and domestic markets and then proposes an innovative method to calculate reactive power service charges using a reactive power market in a wholesale electricity market. The reactive power market is operated based on bids from the generating sources and it settles on uniform prices by running the reactive OPF programs of the day-ahead electricity market. The proposed method takes into account recovering not only the costs of installed capacity but also the lost opportunity costs incurred by reducing active power output to increase reactive power production. Based on the result of the reactive OPF program, the generators that produce reactive power within the obligatory range do not make payments whereas the generators producing reactive power beyond the obligatory range receive compensation by the price determined in the market. A numerical sample study is carried out to illustrate the processes and appropriateness of the proposed method.

• Journal of Korean Medicine Rehabilitation
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• v.18 no.2
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• pp.169-191
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• 2008

Objectives : This study was performed to report the present situation of the cupping treatment to make standardization of cupping treatment in Korea. Methods : We searched relevant case reports, surveys, and review articles using a databases of online bibliography. And we had research to oriental medical doctor with questionnaire about the cupping treatment. Results : 1. Cupping treatment is used for diagnoisis, protection and treatment for many kinds of diseases such as musculoskeletal diseases, internal diseases, sequela of cerebral attacks and so on in Korea. 2. Adequate cupping area is the area of lesion. 3. Cupping time and pressure are various. 4. Adequate amount of venesection is 10cc. 5. Adequate dry cupping term is 1 time/day and adequate wet cupping term is 1 time/2~3days. 6. Cognition of adverse reaction of cupping treatment is different among the doctors. 7. Method of disinfection of cup is different among the doctors. Conclusions : The result of this study will help to make the a guideline of cupping treatment. And we have to go ahead studying to make standardization of cupping treatment.

• Nuclear Engineering and Technology
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• v.50 no.3
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• pp.488-493
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• 2018

The Firebrick Resistance-Heated Energy Storage (FIRES) concept developed by the Massachusetts Institute of Technology aims to enhance profitability of the nuclear power industry in the next decades. Studies carried out at Massachusetts Institute of Technology already provide estimates of the potential revenue from FIRES system when it is applied to industrial heat supply, the likely first application. Here, we investigate the possibility of operating a power plant (PP) with a fluoride-salt-cooled high-temperature reactor and a closed Brayton cycle. This variant offers features such as enhanced nuclear safety as well as flexibility in design of the PP but also radically changes the way of operating the PP. This exploratory study provides estimates of the revenue generated by FIRES in addition to the nominal revenue of the stand-alone fluoride-salt-cooled high-temperature reactor, which are useful for defining an initial design. The electricity price data is based on the day-ahead markets of Germany/Austria and the United States (Iowa). The proposed method derives from the equation of revenue introduced in this study and involves simple computations using MatLab to compute the estimates. Results show variable economic potential depending on the host grid but stress a high profitability in both regions.

• Clean Technology
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• v.28 no.2
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• pp.138-146
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• 2022

Electricity has become a factor that dramatically affects the market economy. The day-ahead system marginal price determines electricity prices, and system marginal price forecasting is critical in maintaining energy management systems. There have been several studies using mathematics and machine learning models to forecast the system marginal price, but few studies have been conducted to develop, compare, and analyze various machine learning and deep learning models based on a data-driven framework. Therefore, in this study, different machine learning algorithms (i.e., autoregressive-based models such as the autoregressive integrated moving average model) and deep learning networks (i.e., recurrent neural network-based models such as the long short-term memory and gated recurrent unit model) are considered and integrated evaluation metrics including a forecasting test and information criteria are proposed to discern the optimal forecasting model. A case study of South Korea using long-term time-series system marginal price data from 2016 to 2021 was applied to the developed framework. The results of the study indicate that the autoregressive integrated moving average model (R-squared score: 0.97) and the gated recurrent unit model (R-squared score: 0.94) are appropriate for system marginal price forecasting. This study is expected to contribute significantly to energy management systems and the suggested framework can be explicitly applied for renewable energy networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2157-2177
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• 2024

Demand response (DR) refers to the customers' active reaction with respect to the changes of market pricing or incentive policies. DR plays an important role in improving network reliability, minimizing operational cost and increasing end users' benefits. Hence, the integration of DR in the microgrid (MG) management is gaining increasing popularity nowadays. This paper proposes a day-ahead MG scheduling framework in conjunction with DR and investigates the impact of DR in optimizing load profile and reducing overall power generation costs. A linear responsive model considering time of use (TOU) price and incentive is developed to model the active reaction of customers' consumption behaviors. Thereafter, a novel multi-swarm sine cosine algorithm (MSCA) is proposed to optimize the total power generation costs in the framework. In the proposed MSCA, several sub-swarms search for better solutions simultaneously which is beneficial for improving the population diversity. A cooperative learning scheme is developed to realize knowledge dissemination in the population and a competitive substitution strategy is proposed to prevent local optima stagnation. The simulation results obtained by the proposed MSCA are compared with other meta-heuristic algorithms to show its effectiveness in reducing overall generation costs. The outcomes with and without DR suggest that the DR program can effectively reduce the total generation costs and improve the stability of the MG network.

• Journal of Korean Tunnelling and Underground Space Association
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• v.16 no.5
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• pp.445-458
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• 2014

The displacement data monitored during tunnel construction play a crucial role in predicting the behaviour of ground around and ahead of excavation face. However, the management criteria for monitoring data are not well established especially for the reliable analysis on varying aspect of displacement data along with chainage. In this study, we evaluated the applicability of x-MR control chart method, which is kind of applied statistical management method, for the analysis of displacement monitoring data in terms of prediction of possible collapse or induced cracks. As a result, a possible abnormal behaviour could be predicted beforehand at 5 ~ 13 m ahead or on at least one day before it occurred by using x-MR control chart method. In addition, it is noted that the moving range for the x-MR control chart should be set to 5~10 for this purpose.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.87-95
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• 2016

This study examined the characteristics of the hourly demand of city gas in Korea and proposed multiple regression models to obtain precise estimates of the hourly demand of city gas. Forecasting the hourly demand of city gas with accuracy is essential in terms of safety and cost. If underestimated, the pipeline pressure needs to be increased sharply to meet the demand, when safety matters. In the opposite case, unnecessary inventory and operation costs are incurred. Data analysis showed that the hourly demand of city gas has a very high autocorrelation and that the 24-hour demand pattern of a day follows the previous 24-hour demand pattern of the same day. That is, there is a weekly cycle pattern. In addition, some conditions that temperature affects the hourly demand level were found. That is, the absolute value of the correlation coefficient between the hourly demand and temperature is about 0.853 on average, while the absolute value of the correlation coefficient on a specific day improves to 0.861 at worst and 0.965 at best. Based on this analysis, this paper proposes a multiple regression model incorporating the hourly demand ahead of 24 hours and the hourly demand ahead of 168 hours, and another multiple regression model with temperature as an additional independent variable. To show the performance of the proposed models, computational experiments were carried out using real data of the domestic city gas demand from 2009 to 2013. The test results showed that the first regression model exhibits a forecasting accuracy of MAPE (Mean Absolute Percentage Error) around 4.5% over the past five years from 2009 to 2013, while the second regression model exhibits 5.13% of MAPE for the same period.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.188-198
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• 2016

In soybeans, responses of high temperature according to shift of sowing dates during the growing season was explored using the crop model, CROPGRO-soybean. In addition, it analyzed impact on change of sowing dates affects yield potential of soybean under future climate scenario (2041-2070). In Jeonju and Miryang during 1981-2010, if sowing at 15 or ten days ahead from 10 June, namely in shorten of the sowing day (i.e. when sown on 25 or 30 May), the yield potential reduced. However, the yield potential increased when sown 5 June. In the case of delay of sowing day (i.e. when sown on 15 or 20 June), reduction of yield potential in the average -5% was higher than increase in the average +2%. In particular, the relative changes for shorten of the sowing day or delay of the sowing day do not be shown in normal years which high temperatures did not abnormally occur during the growing season from 2003 to 2010 except when sown on 25 May. In abnormal years which high temperatures occurred during the critical period, especially R5 to R7, shorten of the sowing day affected to the increase of yield potential in Miryang, while the yield potential decreased in Jeonju except when sown on 5 June. However, delay of the sowing day influenced on the reduction of yield potential both in two sites. In future climate scenario of Representative Concentration Pathway (RCP) 8.5 during from 2041 to 2070, the increase and decrease of yield potential for shorten of the sowing day were +10/-9% for RCP 8.5 of Jeonju, and +14/-9% for RCP 8.5 of Miryang, respectively. Additionally, it showed +10/-17% for RCP 8.5 in Jeonju, and +10/-29% for RCP 8.5 in Miryang, respectively in the increase and decrease of yield potential for delay of the sowing day.

• Journal of the Korea Convergence Society
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• v.9 no.6
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• pp.39-44
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• 2018

The application of deep neural networks to finance has received a great deal of attention from researchers because no assumption about a suitable mathematical model has to be made prior to forecasting and they are capable of extracting useful information from large sets of data, which is required to describe nonlinear input-output relations of financial time series. The paper presents a new deep neural network model where single layered autoencoder and 4 layered neural network are serially coupled for stock price forecasting. The autoencoder extracts deep features, which are fed into multi-layer neural networks to predict the next day's stock closing prices. The proposed deep neural network is progressively learned layer by layer ahead of the final learning of the total network. The proposed model to predict daily close prices of KOrea composite Stock Price Index (KOSPI) is built, and its performance is demonstrated.