• 농촌계획
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• 제24권4호
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• pp.99-119
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• 2018

Unit load factor, which is used for the quantification of non-point pollution in watersheds, has the limitation that it does not reflect spatial characteristics of soil, topography and temporal change due to the interannual or seasonal variability of precipitation. Therefore, we developed the method to estimate a watershed-scale non-point pollutant load using seasonal forecast data that forecast changes of precipitation up to 6 months from present time for watershed-scale water quality management. To establish a preemptive countermeasure against non-point pollution sources, it is possible to consider the unstructured management plan which is possible over several months timescale. Notably, it is possible to apply various management methods such as control of sowing and irrigation timing, control of irrigation through water management, and control of fertilizer through fertilization management. In this study, APEX-Paddy model, which can consider the farming method in field scale, was applied to evaluate the applicability of seasonal forecast data. It was confirmed that the rainfall amount during the growing season is an essential factor in the non-point pollution pollutant load. The APEX-Paddy model for quantifying non-point pollution according to various farming methods in paddy fields simulated similarly the annual variation tendency of TN and TP pollutant loads in rice paddies but showed a tendency to underestimate load quantitatively.

• 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 2000년도 하계학술대회 논문집 A
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• pp.295-297
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• 2000

In case of power demand forecasting the most important problem is to deal with the load of special-days, Accordingly, this paper presents a method that forecasting special-days load with regression models and neural networks. Special-days load in summer season was forecasted by the multiple regression models using weekday change ratio Neural networks models uses pattern conversion ratio, and orthogonal polynomial models was directly forecasted using past special-days load data. forecasting result obtains % forecast error of about $1{\sim}2[%]$. Therefore, it is possible to forecast long and short special-days load.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 A
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• pp.298-300
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• 2000

This paper proposes a fuzzy linear regression algorithm based on Tanaka's theory for holiday load forecasting. The load patterns of holidays are quite different from those of ordinary weekdays. It is difficult to accurately forecast the holiday load due to the insufficiency of the load patterns compared with ordinary weekdays. The test results show that the proposed method greatly improves the forecast accuracy for holidays.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 제39회 하계학술대회
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• pp.111-112
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• 2008

Although two types of uncertainty such as randomness and fuzziness simultaneously exist in power systems, yet they have been treated as distinct fields to evaluate the power system reliability. Thus, this paper presents a reliability assessment method based on a combined concept of fuzzy and probability. To reflect the two-fold uncertainty, a modified load duration curve(MLDC) is proposed using the probability distribution of historical load data in which a fuzzy model for the peak load forecast is embedded. IEEE RTS system was used to demonstrate the usefulness and applicability of the proposed method, and the reliability indices were obtained using the proposed MLDC. The results show a wider insight into impact of load fuzziness on uncertainties of reliability indices for power systems.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.562-564
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• 1995

This paper proposes a new algorithm for maintenance scheduling with considering load forecast uncertainty. The proposed algorithm is based on the equivalent load of effective load carrying capability(ELCC) of generators. The uncertainty of forecasted load is considered as a normal distribution probability density function. For maintenance scheduling, reserve levelization method and risk levelization method are used in this study. To test the algorithm, we applied the proposed method to IEEE reliability test system(IEEE RTS). As a result, we verified the validity of the proposed method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.578-580
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• 1995

In this paper, a method for the daily maximum load forecasting which uses a chaotic time series in power system and artificial neural network. We find the characteristics of chaos in power load curve and then determine a optimal embedding dimension and delay time, For the load forecast of one day ahead daily maximum power, we use the time series load data obtained in previous year. By using of embedding dimension and delay time, we construct a strange attractor in pseudo phase plane and the artificial neural network model trained with the attractor font mentioned above. The one day ahead forecast errors are about 1.4% of absolute percentage average error.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.835-837
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• 1996

In this paper, a method for the daily maximum load forecasting which uses a chaotic time series in power system and artificial neural network(Back-propagation) is proposed. We find the characteristics of chaos in power load curve and then determine a optimal embedding dimension and delay time. For the load forecast of one day ahead daily maximum power, we use the time series load data obtained in previous year. By using of embedding dimension and delay time, we construct a strange attractor in pseudo phase plane and the artificial neural network model trained with the attractor mentioned above. The one day ahead forecast errors are about 1.4% for absolute percentage average error.

• 전기학회논문지
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• 제65권7호
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• pp.1144-1150
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• 2016

Short-term load forecasting is essential to the electricity pricing and stable power system operations. The conventional weekday 24-hour load forecasting algorithms consider the temperature model to forecast maximum load and minimum load. But 24-hour load pattern forecasting models do not consider temperature effects, because hourly temperature forecasts were not present until the latest date. Recently, 3 hour temperature forecast is announced, therefore hourly temperature forecasts can be produced by mathematical techniques such as various interpolation methods. In this paper, a new 24-hour load pattern forecasting method is proposed by using similar day search considering the hourly temperature. The proposed method searches similar day input data based on the anomalous weather features such as continuous temperature drop or rise, which can enhance 24-hour load pattern forecasting performance, because it uses the past days having similar hourly temperature features as input data. In order to verify the effectiveness of the proposed method, it was applied to the case study. The case study results show high accuracy of 24-hour load pattern forecasting.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 D
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• pp.1144-1148
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• 1997

In this paper, a method for the daily maximum load forecasting which uses a chaotic time series in power system and artificial neural network. We find the characteristics of chaos in power load curve and then determine a optimal embedding dimension and delay time. For the load forecast of one day ahead daily maximum power, we use the time series load data obtained in previous year. By using of embedding dimension and delay time, we construct a strange attractor in pseudo phase plane and the artificial neural network model trained with the attractor mentioned above. The one day ahead forecast errors are about 1.4% for absolute percentage average error.