• 전기학회논문지
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• 제65권5호
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• pp.718-723
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• 2016

The temperature changes are very important in improving the accuracy of the load forecasting during the summer. It is because the cooling load in summer contribute to the increasing of the load. This paper proposes a weekend load forecasting algorithm using the temperature change characteristic in a summer of Jeju. The days before and after weekends in Jeju, when the load curves are quite different from those of normal weekdays. The temperature change characteristic are obtained by using weekends peak load and high temperature data. And load forecasted based on the sensitivity between unit temperature changes and load variations. Load forecast data with better accuracy are obtained by using the proposed temperature changes than by using the ordinary daily peak load forecasting. The method can be used to reduce the error rate of load forecast.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2011년도 춘계학술대회 논문집
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• pp.940-947
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• 2011

Load capacity varies according to a day of the week in traction power supply system, because time schedule in railway is changed as demand for passengers and freights. Therefore, Voltage drop also varies as load capacity. In Korea railway, Voltage collected from catenary in train is decreased, as load supplied traction power supply system is increased. Therefore, investigation about voltage drop should be performed, before development of countermeasure against voltage drop. The investigation can be performed by simulation or field test. Naturally, field test is more precise than simulation. In addition, field test should be carried out at peak load. This paper presents test and analysis about voltage drop in railway. The test is performed in both a day of the week and weekend. The analysis is figured out comparison load capacity between two days and voltage drop across terminal.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1991년도 추계학술대회 논문집 학회본부
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• pp.39-42
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• 1991

The Application of Artificial Neural Network(ANN) to forecast a load in a power system is investigated. The load forecasting is important in the electric utility industry. This technique, methodology based on the fact that parallel structure can process very fast much information is a promising approach to a load forecasting. ANN that is highly interconnected processing element in a hierachy activated by the each input. The load pattern can be divided distinctively into two patterns, that is, weekday and weekend. ANN is composed of a input layer, several hidden layers, and a output layer and the past data is used to activate input layer. The output of ANN is the load forecast for a given day. The result of this simulation can be used as a reference to a electric utility operation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2011년도 제42회 하계학술대회
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• pp.2210-2211
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• 2011

Load capacity varies according to a day of the week in traction power supply system, because time schedule in railway is changed as demand for passengers and freights. Therefore, Voltage drop also varies as load capacity. In Korea railway, Voltage collected from catenary in train is decreased, as load supplied traction power supply system is increased. Therefore, investigation about voltage drop should be performed, before development of countermeasure against voltage drop. The investigation can be performed by simulation or field test. Naturally, field test is more precise than simulation. In addition, field test should be carried out at peak load. This paper presents test and analysis about voltage drop in railway. The test is performed in both a day of the week and weekend. The analysis is figured out comparison load capacity between two days and voltage drop across terminal.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 춘계학술대회 논문집 전력기술부문
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• pp.230-232
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• 2001

This paper presents a model for short-term load forecasting using multiple time-series. We made one-hour ahead load forecasting without classifying load data according to daily load patterns(e.g. weekday. weekend and holiday) To verify its effectiveness. the results are compared with those of neuro-fuzzy forecasting model(5). The results show that the proposed model has more accurate estimate in forecasting.

• 대한전기학회논문지:전력기술부문A
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• 제48권1호
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• pp.8-14
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• 1999

This paper presents the methods of short-term load forecasting Kohonen neural networks and back-propagation neural networks. First, historical load data is divided into 5 patterns for the each seasonal data using Kohonen neural networks and using these results, load forecasting neural network is used for next day hourly load forecasting. Next day hourly load of weekdays and weekend except holidays are forecasted. For load forecasting in summer, max-temperature and min-temperature data as well as historical hourly load date are used as inputs of load forecasting neural networks for a better forecasting accuracy. To show the possibility of the proposed method, it was tested with hourly load data of Korea Electric Power Corporation(1994-95).

• 대한전기학회:학술대회논문집
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• 대한전기학회 2011년도 제42회 하계학술대회
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• pp.71-72
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• 2011

단기 전력수요예측의 오차를 줄여 불필요한 전력생산을 이전에 방지하는 것은 매우 중요하다. 본 논문에서는 오차율이 높은 연휴 전 평일의 단기 전력수요예측 정확도를 높이기 위해 이전 평일과 주말의 데이터를 이용한 새로운 예측 방법을 제안하고, 추석연휴 전 평일에 제안한 방법을 적용하여 수요예측에 대한 오차가 개선됨을 확인하였다.

• 한국조명전기설비학회지:조명전기설비
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• 제11권5호
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• pp.62-66
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• 1997

본 논문에서는 평일환산비를 사용하여 단기부하를 상정하는 알고리즘을 제시한다. 평일환산비로 주 주기성을 제거하고, 5개의 상정구간과 3 형태의 중회귀모델을 구성한다. 상정결과 상정도가 2.8〔%〕정도로 양호한 결과를 얻었다. 이로서 특수일(주말)부하의 전력수요상정도 가능하게 되었다. 중회귀 모델을 이용한 전력수요상정시의 큰 문제점인 특수일(주말)의 전력수요를 상정하는 방법이 제시됨으로서 상정도의 향상은 물론 신뢰성있는 상정모델의 구성이 가능하게 되었다.

• 한국태양에너지학회:학술대회논문집
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• 한국태양에너지학회 2011년도 춘계학술발표대회 논문집
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• pp.127-132
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• 2011

Worldwide, the building energy simulation becomes inevitable step for predicting the energy consumption in building. In simulation process, the expertise is required for the accurate analysis results. In Korea, however, most of user use the inconsistent data with Korea circumstance. In this step, we need to construct the standard input data matched building in Korea. In this study, electricity consumption of apartments in Daejeon is analyzed. The yearly data of a apartment complexes of 2009 are analyzed as monthly, daily(week and weekend), timely, and completion year. With this result, we are able to predict the demand pattern of electricity in a house and make the schedule by demand pattern. The results of this study are followed. The averaged amount of electricity consumption in winter is higher than summer because of the high capacity of heating equipment. All of the house has electric base load from 0.26kWh to 0.5kWh. The average of the electricity consumption of month is shown as 326.7kWh. A week is seperated as 4 part such as week, weekend, Saturday and Sunday. During week, the average of timely electricity consumption is shown as 0.442kWh. The Saturday consumption is 0.453kWh. The Sunday is 0.461kWh.

• 한국대기환경학회지
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• 제4권1호
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• pp.79-83
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• 1988

To improve working environment for the toll workers who were working at Kyungbu expressway in outskirts of Seoul and Suwon, the status of air pollution surrounding toll booth were measured from March 28 through June 14, 1986. The results were as follows: 1. The amount of TSP (total suspended particle) surrounding toll booth was directly proportional to the traffic load. The ratio of traffic load at Seoul and Suwon toll was 3.2:1 and of TSP was 2.6:1. 2. The proportion of particle larger than 5$\mum$ was 24.8 $\sim$ 34.9% of TSP at Seoul toll and 19.2 $\sim$ 32.7% at Suwon. The proportion of particle less than 2$\mum$ was 38.7 $\sim$ 51.8% of TSP at Seoul toll and 34.8 $\sim$ 54.8% at Suwon. 3. The concentration of respirable particle les than 7$\mum$ measured by personal air sampler was higher in Seoul toll booth than that of Suwon and it seems to be influenced by the exhausion of diesel engine. Especially the concentration of respirable particle of reformed toll booth with air curtain was 20% lower than unreformed one. 4. Concentration of Pb among suspended particles around Seoul toll was 5 times higher than Pb of Suwon toll. So it is considered that there were other possible pollution source of Pb beyond heavy traffic in Seoul toll area. The amount of Pb inside toll booth was extremely small but the concentration of benzo(a)pyrene showed a trend of increase according to traffic stagnation. 5. The concentration of $SO_2$ arround toll showed no difference between weekday and weekend and also showed no relation with traffic load. But the concentration of $NO_2$ was affected by traffic load.