• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 A
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• pp.417-419
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• 2005

This paper presents the effects and the regional power distribution of an increase or a decrease of a power reserve by load flow calculations under seasonal load patterns of each country for the future power shortages faced by the metropolitan areas or by the southeastern area of the South Korea in North-East Asia. In these connections, the types of a power transmission for interconnection consist of the 765kV HVAC and the HVDC. In this paper, the various cases of the power system interconnections in Far-East Asia are presented, and the resulting interconnected power systems are simulated by means of a power flow analysis performed with the PSS/E 28 version tool. The power flow map is drawn from data simulated and the comparative study is done. In this future, a power flow analysis will be considered to reflect the effects of seasonal power exchanges And the plan of assumed scenarios will be considered with maximum or minimum power exchanges during summer or winter in North-East Asia countries.

• Journal of Electrical Engineering and Technology
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• 제2권1호
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• pp.1-9
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• 2007

This paper presents the effects of an increase or a decrease of a power reserve by load flow calculations under the seasonal load patterns of each country for the future power shortages faced by the metropolitan areas or by the southeastern area of South Korea in North-East Asia. In this paper, the various cases of the power system interconnections in Far-East Asia are presented, and the resulting interconnected power systems are simulated by means of a power flow analysis performed with the PSS/E 28 version tool. Data for simulation were obtained from the 2-th long term plan of electricity supply and demand in KEPCO. The power flow map is drawn from simulated data and the comparative study is done. In the future, a power flow analysis will be considered to reflect the effects of seasonal power exchanges. And the plan of assumed scenarios will be considered with maximum or minimum power exchanges during summer or winter in North-East Asian countries.

• 산업경영시스템학회지
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• 제39권1호
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• pp.25-30
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• 2016

It is critical to forecast the maximum daily and monthly demand for power with as little error as possible for our industry and national economy. In general, long-term forecasting of power demand has been studied from both the consumer's perspective and an econometrics model in the form of a generalized linear model with predictors. Time series techniques are used for short-term forecasting with no predictors as predictors must be predicted prior to forecasting response variables and containing estimation errors during this process is inevitable. In previous researches, seasonal exponential smoothing method, SARMA (Seasonal Auto Regressive Moving Average) with consideration to weekly pattern Neuron-Fuzzy model, SVR (Support Vector Regression) model with predictors explored through machine learning, and K-means clustering technique in the various approaches have been applied to short-term power supply forecasting. In this paper, SARMA and intervention model are fitted to forecast the maximum power load daily, weekly, and monthly by using the empirical data from 2011 through 2013. $ARMA(2,\;1,\;2)(1,\;1,\;1)_7$ and $ARMA(0,\;1,\;1)(1,\;1,\;0)_{12}$ are fitted respectively to the daily and monthly power demand, but the weekly power demand is not fitted by AREA because of unit root series. In our fitted intervention model, the factors of long holidays, summer and winter are significant in the form of indicator function. The SARMA with MAPE (Mean Absolute Percentage Error) of 2.45% and intervention model with MAPE of 2.44% are more efficient than the present seasonal exponential smoothing with MAPE of about 4%. Although the dynamic repression model with the predictors of humidity, temperature, and seasonal dummies was applied to foretaste the daily power demand, it lead to a high MAPE of 3.5% even though it has estimation error of predictors.

• 전기학회논문지
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• 제59권11호
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• pp.1956-1963
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• 2010

Studies on the development of various energy management programs and real-time bidirectional information infrastructures have been actively conducted to promote the reduction of power demands and CO2 emissions effectively. In the conventional energy management programs, the demand response program that can transition or transfer the power use spontaneously for power prices and other signals has been largely used throughout the inside and outside of the country. For measuring the effect of such demand response program, it is necessary to exactly estimate short-term loads. In this study, the power consumption patterns in both individual and group consumers were analyzed to estimate the exact short-term loads, and the relationship between the actual power consumption and seasonal factors was also analyzed.

• 한국인공지능학회지
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• 제12권1호
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• pp.17-24
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• 2024

This paper introduces a novel approach to time-series estimation for energy load forecasting within Virtual Power Plant (VPP) systems, leveraging advanced artificial intelligence (AI) algorithms, namely Long Short-Term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA). Virtual power plants, which integrate diverse microgrids managed by Energy Management Systems (EMS), require precise forecasting techniques to balance energy supply and demand efficiently. The paper introduces a hybrid-method forecasting model combining a parametric-based statistical technique and an AI algorithm. The LSTM algorithm is particularly employed to discern pattern correlations over fixed intervals, crucial for predicting accurate future energy loads. SARIMA is applied to generate time-series forecasts, accounting for non-stationary and seasonal variations. The forecasting model incorporates a broad spectrum of distributed energy resources, including renewable energy sources and conventional power plants. Data spanning a decade, sourced from the Korea Power Exchange (KPX) Electrical Power Statistical Information System (EPSIS), were utilized to validate the model. The proposed hybrid LSTM-SARIMA model with parameter sets (1, 1, 1, 12) and (2, 1, 1, 12) demonstrated a high fidelity to the actual observed data. Thus, it is concluded that the optimized system notably surpasses traditional forecasting methods, indicating that this model offers a viable solution for EMS to enhance short-term load forecasting.

• Journal of Preventive Medicine and Public Health
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• 제40권3호
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• pp.239-248
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• 2007

Objectives : This study was conducted to investigate the systematic error, such as seasonal change or inadequate food items, in a food frequency questionnaire administered to workers in a Nuclear Power Plant, Korea. Methods : We performed three repeat-tests with 28 subjects on May 13, July 8 and Dec 16, 1992. Our food frequency questionnaire (FFQ) comprised 84 foods organized into 7 food-groups, and was composed of the items of usual intake frequency (8 categories) and the amount per intake (3 or 4 categories) over the previous year. We compared the means of intake frequency and the frequency of the portion-size according to each season using Repeated Measures ANOVA and Pearson's chisquare test with Fisher's exact test. Results : We found the significant seasonal changes of several food items in intake frequency measurement. These items were typical seasonal foods such as mandarin orange, plum and green vegetables, while the single questions consisted of inadequate food items such as thick beef or similar soup and various kimchi products. Significant seasonal changes in portion-size were found in only two items: cooked rice-brown and fresh frozen fishes. Conclusions : The systematic errors observed could caused loss of validity in the FFQ. Consideration should be given for seasonal variation in FFQ survey and methodological concerns are needed to improve the quality for measuring usual diet pattern.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 추계학술대회 논문집 전력기술부문
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• pp.134-137
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• 2004

본 논문에서는 향후 남한의 예비력 중대방안으로 동북아 지역 (러시아, 중국, 몽고, 북한, 한국, 일본)의 지역의 전력계통연계선로의 구성 및 동북아 지역의 계절별 패턴을 고려한 조류계산을 수행하여 전력수급의 분포도를 파악하고자 한다. 특히 한반도 전체의 전력수급을 고려하여 볼 때 남-북한의 수도권 및 영남 지역의 두 지역은 향후 계속적인 전력 수요의 증가로 인한 발전력의 부족상태가 계속되리라 여겨지며, 이러한 문제의 해결방안으로 북한의 신포지역에 2,000MW KEDO 경수로를 건설하여 공급하는 방안이 있겠으나 현재 여러 가지 정치적 상황으로는 건설을 중단하게 되었다. 이러한 정치적 상황의 변화로 다른 대안이 필요하게 되었다. 이들 중의 하나가 극동러시아나 시베리아 및 중국 그리고 일본과의 연계에 의한 예비력을 확보하는 방안이 될 것이다. 본 논문에서는 동북아 지역과 연계를 할 수 있는 가능한 지역을 고려하여 연계선로를 구성하고 계절별 효과를 고려한 조류계산을 실시하여 연계 시 융통전력의 분포도를 연구하고자 한다.

• 복식문화연구
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• 제31권5호
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• pp.607-626
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• 2023

This study analyzes consumer fashion purchase patterns from a big data perspective. Transaction data from 1 million transactions at two Korean fashion brands were collected. To analyze the data, R, Python, the SPADE algorithm, and network analysis were used. Various consumer purchase patterns, including overall purchase patterns, seasonal purchase patterns, and age-specific purchase patterns, were analyzed. Overall pattern analysis found that a continuous purchase pattern was formed around the brands' popular items such as t-shirts and blouses. Network analysis also showed that t-shirts and blouses were highly centralized items. This suggests that there are items that make consumers loyal to a brand rather than the cachet of the brand name itself. These results help us better understand the process of brand equity construction. Additionally, buying patterns varied by season, and more items were purchased in a single shopping trip during the spring season compared to other seasons. Consumer age also affected purchase patterns; findings showed an increase in purchasing the same item repeatedly as age increased. This likely reflects the difference in purchasing power according to age, and it suggests that the decision-making process for pur- chasing products simplifies as age increases. These findings offer insight for fashion companies' establishment of item-specific marketing strategies.

• 전기학회논문지
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• 제63권9호
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• pp.1186-1191
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• 2014

Weekly and monthly electric load forecasting are essential for the generator maintenance plan and the systematic operation of the electric power reserve. This paper proposes the weekly maximum electric load forecasting model for 104 weeks with the multiple regression model. Input variables of the multiple regression model are temperatures and GDP that are highly correlated with electric loads. The weekly variable is added as input variable to improve the accuracy of electric load forecasting. Test results show that the proposed algorithm improves the accuracy of electric load forecasting over the seasonal autoregressive integrated moving average model. We expect that the proposed algorithm can contribute to the systematic operation of the power system by improving the accuracy of the electric load forecasting.

• 대한전기학회논문지:전력기술부문A
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• 제55권3호
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• pp.128-132
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• 2006

Interstate electric power system, as an alternative for energy cooperation under regional economic bloc, has been hotly debated before progressing the restructure in electric power industry and rapidly expanded in many regions after 1990s. Especially, since northeast asia has strong supplementation in resource, load shape, fuel mix etc., electric power system interconnection in this region may bring considerable economic benefits. Moreover, since Korean electric power system has a great difficulty in a geographical condition to interrupt electricity transaction with other countries, it has been expanded as an independent system to supply all demand domestically. As a result, Korean electric power system makes considerable payment for maintaining system security and reliability and expands costly facilities to supply a temporary summer peak demand. Under this inefficiency, if there are electricity transactions with Russia via the North Korea route then economic electric power system operation nay be achieved using seasonal and hourly differences in electricity price and/or load pattern among these countries. In this paper, we estimate price cap of transacted electricity via interstate electric power system in northeast asia. For this study, we perform quantitative economic analysis on various system interconnection scenarios.