• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2011년도 제42회 하계학술대회
• /
• pp.670-672
• /
• 2011

본 논문에서는 전력시장가격(SMP, SYstem Marginal Pricing, 이하 SMP)이 급등하여 발전비용이 증가할 경우, Utility의 신호에 의해 전력사용을 줄이거나 소비량이 낮은 시간대로 이동을 유도함으로써 전력소비자의 패턴을 바꾸도록 유도하는 상시수요관리에 있어서, 전력소비자의 기저부하(CBL, Customer Baseline Load, 이하 CBL) 산정 방법에 관하여 기술한다. 제안하는 방법은 현재 KEPCO에서 시행하고 있는 상시수요관리에 대해 소개하고, 전력소비자의 CBL산정방법을 제시하고, 실제 적용된 정확도를 분석하고 그 결과를 제시하였다.

• 한국조명전기설비학회:학술대회논문집
• /
• 한국조명전기설비학회 2007년도 춘계학술대회 논문집
• /
• pp.79-84
• /
• 2007

Recently, there are rapid increases in golf population. People are enjoying play golf not only during the day but also at night as well. Although there are not many golf courses that offer night play, people inquire place where they can play night games due to their busy daily lives. To supply the demand in national golf courses night-lighting has been pushed ahead competitively, however there are few standards of lighting design in golf course. Because it is not clear that the standards of lighting in the world yet, this study aimed at presentation of standard of lighting design for planning golf course. Investigating the cases of golf course, this study suggested illumination standard of golf course to support comfortable visual-environment and measures to prevention glare for visitors.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2005년도 추계학술대회 논문집 전력기술부문
• /
• pp.148-150
• /
• 2005

Since the SMP(System Marginal Price) is a vital factor to the market participants who intend to maximize the their profit and to the ISO(Independent System Operator) who wish to operate the electricity market in a stable sense, the short-term marginal price forecasting should be performed correctly. This paper presents a methodology of a day-ahead SMP forecasting using ARIMA(Autoregressive Integrated Moving Average) based on the Time Series. And also we suggested a correction algorithm to minimize the forecasting error in order to improve efficiency and accuracy of the SMP forecasting. To show the efficiency and effectiveness of the proposed method, the numerical studies have been performed using Historical data of SMP in 2004 published by KPX(Korea Power Exchange).

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1993년도 정기총회 및 추계학술대회 논문집 학회본부
• /
• pp.29-31
• /
• 1993

This paper presents Neural Network(NN) approach to short-term load forecasting. Input to the NN are past loads and the output is the predicted load for a given day. The NN is used to learn the relationship among past, current and future temperature and loads. Three different cases are presented. Case 1 divides into weekday and weekendday load pattern. Case 2 forcasts 24-hour ahead load. Case 3 searchs for the same load pattern as present load pattern in past load pattern. From result of forecasting, an average absolute percentage errors of case 1 shows 2.0%. That of case 2 shows 2.2, and That of case 3 shows 1.6%.

• 상하수도학회지
• /
• 제26권4호
• /
• pp.547-554
• /
• 2012

As a national river remediation project has been completed, this study has a special interest on the capabilities to predict water levels at various points of the Geum River. To be endowed with intelligent forecasting capabilities, the author formulate the neuro-genetic algorithm associated with the short-term water level prediction model. The results show that neuro-genetic algorithm has considerable potentials to be practically used for water level forecasting, revealing that (1) model optimization can be obtained easily and systematically, and (2) validity in predicting one- or two-day ahead water levels can be fully proved at various points.

• Journal of Electrical Engineering and Technology
• /
• 제10권3호
• /
• pp.832-837
• /
• 2015

The group method of data handling (GMDH) algorithm has proven to be a powerful and effective way to extract rules or polynomials from an electric load pattern. However, because it is nonstationary, the load pattern needs to be decomposed using a discrete wavelet transform. In addition, if a load pattern has a complicated curve pattern, GMDH should use a higher polynomial, which requires complex computing and consumes a lot of time. This paper suggests a method for short-term electric load forecasting that uses a wavelet transform and a GMDH algorithm. Case studies with the proposed algorithm were carried out for one-day-ahead forecasting of hourly electric loads using data during the years 2008-2011. To prove the effectiveness of our proposed approach, the results were evaluated and compared with those obtained by Holt-Winters method and artificial neural network. Our suggested method resulted in better performance than either comparison group.

• 대한전기학회논문지:전력기술부문A
• /
• 제50권9호
• /
• pp.399-399
• /
• 2001

This paper proposes TAR(Threshold Autoregressive) model for short-term load forecasting including temperature variable. In the scatter diagram of daily peak load versus daily high or low temperature, we can find out that the load-temperature relationship has a negative slope in the lower regime and a positive slope in the upper regime due to the heating and cooling load, respectively. TAR model is adequate for analyzing these phenomena since TAR model is a piecewise linear autoregressive model. In this paper, we estimated and forecasted one day-ahead daily peak load by applying TAR model using this load-temperature characteristic in these regimes. The results are compared with those of linear and quadratic regression models.

• 대한전기학회논문지:전력기술부문A
• /
• 제50권9호
• /
• pp.309-405
• /
• 2001

This paper proposes TAR(Threshold Autoregressive) model for short-term load forecasting including temperature variable. In the scatter diagram of daily peak load versus daily high or low temperature, we can find out that the load-temperature relationship has a negative slope in the lower regime and a positive slope in the upper regime due to the heating and cooling load, respectively. TAR model is adequate for analyzing these phenomena since TAR model is a piecewise linear autoregressive model. In this paper, we estimated and forecasted one day-ahead daily peak load by applying TAR model using this load-temperature characteristic in these regimes. The results are compared with those of linear and quadratic regression models.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2004년도 하계학술대회 논문집 A
• /
• pp.105-106
• /
• 2004

This paper explains a network for various participants of the electricity market to make bids on the power exchange. The power exchange accepts bids for various markets such as day-ahead, realtime, and financial over this interface. It exists on the IT plane of the market hierarchy and the participants are able to access it over the internet.

• Communications for Statistical Applications and Methods
• /
• 제25권6호
• /
• pp.605-618
• /
• 2018

Risk management has been a crucial part of the daily operations of the financial industry over the past two decades. Value at Risk (VaR), a quantitative measure introduced by JP Morgan in 1995, is the most popular and simplest quantitative measure of risk. VaR has been widely applied to the risk evaluation over all types of financial activities, including portfolio management and asset allocation. This paper uses the implementations of multivariate GARCH models and copula methods to illustrate the performance of a one-day-ahead VaR prediction modeling process for high-dimensional portfolios. Many factors, such as the interaction among included assets, are included in the modeling process. Additionally, empirical data analyses and backtesting results are demonstrated through a rolling analysis, which help capture the instability of parameter estimates. We find that our way of modeling is relatively robust and flexible.