• 상하수도학회지
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• 제30권3호
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• pp.225-231
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• 2016

Peak load rate(i.e., maximum daily flow/average daily flow) has not been considered for industrial water demand planning in Korea to date, while area unit method based on average daily flow has been applied to decide capacity of industrial water treatment plants(WTPs). Designers of industrial WTPs has assumed that peak load would not exist if operation rate of factories in industrial sites were close to 100%. However, peak load rates were calculated as 1.10~2.53 based on daily water flow from 2009 to 2014 for 9 industrial WTPs which have been operated more than 9 years(9-38 years). Furthermore, average operation rates of 9 industrial WTPs was less than 70% which means current area unit method has tendency to overestimate water demand. Therefore, it is not reasonable to consider peak load for the calculation of water demand under current area unit method application to prevent overestimation. However, for the precise future industrial water demand calculation more precise data gathering for average daily flow and consideration of peak load rate are recommended.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 추계학술대회 논문집 전력기술부문
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• pp.371-373
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• 2001

In this paper, we present 2-step load cycle for daily load curve of up to and including 100kVA distribution transformer in domestic. Daily load patterns are classified by two methods dependent upon possession information. In case we possess daily load profiles make use of K-mean algorithm and in case we have not daily load profiles, make use of customer information of KEPCO. As the parameters of the load pattern classification, we use are daily load profiles and customer information of each distribution transformers. Data management system is used for NT oracle. We can present peak load magnitude, initial load magnitude and peak load duration for daily load patterns by 2-step load cycle for daily load curve of up to and including 100kVA distribution transformer in domestic. We think that this paper contributes to enhancing the distribution transformer overload criterion.

• 전기학회논문지
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• 제67권1호
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• pp.96-102
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• 2018

For efficient operating strategy of electric power system, forecasting of daily peak electric load is an important but difficult problem. Therefore a daily peak electric load forecasting system using a neural network and fuzzy system is presented in this paper. First, original peak load data is interpolated in order to overcome the shortage of data for effective prediction. Next, the prediction of peak load using these interpolated data as input is performed in parallel by a neural network predictor and a fuzzy predictor. The neural network predictor shows better performance at drastic change of peak load, while the fuzzy predictor yields better prediction results in gradual changes. Finally, the superior one of two predictors is selected by the rules based on rough sets at every prediction time. To verify the effectiveness of the proposed method, the computer simulation is performed on peak load data in 2015 provided by KPX.

• 전기학회논문지
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• 제66권3호
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• pp.475-485
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• 2017

This paper develops a conversion function and method transforming from daily peak load curve used $LOLE_D$ [days/year] to hourly load curve used $LOLE_H$[hours/year]and describes relationship between $LOLE_D$ [days/year] and $LOLE_H$ [hours/year]. The indices can not only be transformed just arithmetically but also have different characteristics physically because of using their different load curves. The conversion function is formulated as variables of capacity and forced outage rate of generator, hourly load daily load factor and daily peak load yearly load factor, etc. Therefore, the conversion function (${\gamma}={\varphi}$(.)) can not be simple. In this study, therefore, the function is formulated as linear times of separated two functions. One is an exponential formed conversion function of daily load factor. Another is formulated with an exponential typed conversion function of daily peak load yearly load factor. Futhermore, this paper presents algorithm and flow chart for transforming from $LOLE_D$[days/year] to $LOLE_H$[hours/year]. The proposed conversion function is applied to sample system and actual KPS(Korea Power System) in 2015. The exponent coefficients of the conversion functions are assessed using proposed method. Finally, assessment errors using conversion function for case studies of sample system and actual system are evaluated to certify the firstly proposed method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권3호
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• pp.163-172
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• 2016

Electric load forecasting is essential for effective power system planning and operation. Complex and nonlinear relationships exist between the electric loads and their exogenous factors. In addition, time-series load data has non-stationary characteristics, such as trend, seasonality and anomalous day effects, making it difficult to predict the future loads. This paper proposes a locally-weighted polynomial neural network (LWPNN), which is a combination of a polynomial neural network (PNN) and locally-weighted regression (LWR) for daily shortterm peak load forecasting. Model over-fitting problems can be prevented effectively because PNN has an automatic structure identification mechanism for nonlinear system modeling. LWR applied to optimize the regression coefficients of LWPNN only uses the locally-weighted learning data points located in the neighborhood of the current query point instead of using all data points. LWPNN is very effective and suitable for predicting an electric load series with nonlinear and non-stationary characteristics. To confirm the effectiveness, the proposed LWPNN, standard PNN, support vector regression and artificial neural network are applied to a real world daily peak load dataset in Korea. The proposed LWPNN shows significantly good prediction accuracy compared to the other methods.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권1호
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• pp.39-49
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• 2013

This paper presents a method of improving the performance of a day-ahead 24-h load curve and peak load forecasting. The next-day load curve is forecasted using radial basis function (RBF) neural network models built using the best design parameters. To improve the forecasting accuracy, the load curve forecasted using the RBF network models is corrected by the weighted sum of both the error of the current prediction and the change in the errors between the current and the previous prediction. The optimal weights (called "gains" in the error correction) are identified by differential evolution. The peak load forecasted by the RBF network models is also corrected by combining the load curve outputs of the RBF models by linear addition with 24 coefficients. The optimal coefficients for reducing both the forecasting mean absolute percent error (MAPE) and the sum of errors are also identified using differential evolution. The proposed models are trained and tested using four years of hourly load data obtained from the Korea Power Exchange. Simulation results reveal satisfactory forecasts: 1.230% MAPE for daily peak load and 1.128% MAPE for daily load curve.

• 전기학회논문지
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• 제65권3호
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• pp.404-410
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• 2016

This paper proposed a program of an energy storage system(ESS) for peak shaving of high-speed railway substations The peak shaving saves cost of equipment and demand cost of the substation. To reduce the peak load, it is very important to know when the peak load appears. The past data based load profile forecasting method is easy and applicable to customers which have relatively fixed load profiles. And an optimal scheduling method of the ESS is helpful in reducing the electricity tariff and shaving the peak load efficiently. Based on these techniques, MS. NET based peak shaving program is developed. In case study, a specific daily load profile of the local substation was applied and simulated to verify performance of the proposed program.

• 조명전기설비학회논문지
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• 제15권6호
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• pp.35-42
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• 2001

1일 최대전력 부하 예측 자료는 계통의 경제적 운용과 전력 감시에 필수적이므로 정확한 예측기법이 요구된다. 신경회로망이나 퍼지이론을 한 예측비법의 장점은 정도(精度)가 높고 운용하기가 편리한 점은 있으나 학습시간이 길고, 부하가 급변할 때는 예측오차가 크게 발생한다. 본 연구에서는 이러한 단점을 개선하기 위하여 새로운 예측 기법을 제시하였으며 예측결과에서 타당성이 입증되었다.

• 대한전기학회논문지:전력기술부문A
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• 제50권9호
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• pp.399-399
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• 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
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• 제50권9호
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• pp.309-405
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• 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.