• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.12
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• pp.2215-2220
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• 2011

The accurate short-term load forecasting is essential for the efficient power system operation and the system marginal price decision of the electricity market. So far, errors of load forecasting for Chuseok Holiday are very big compared with forecasting errors for the other special days. In order to improve the accuracy of load forecasting for Chuseok Holiday, selection of input data, the daily normalized load patterns and load forecasting model are investigated. The efficient data selection and daily normalized load pattern based on fuzzy linear regression model is proposed. The proposed load forecasting method for Chuseok Holiday is tested in recent 5 years from 2006 to 2010, and improved the accuracy of the load forecasting compared with the former research.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.11 no.5
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• pp.62-66
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• 1997

This paper is presented to short-term load forecast algorithm using weekday change ratio. The week periodicity was excluded from weekday change ratio. That was composed with the power demand forecast term of five and multiple regression model of the three form. The precision was good with 2.8[%]. Also the power demand of special day(weekend) of completely difficult forecast case of using the multiple regression model was able to forecast at this paper. Therefore, the forecast precision was enhanced and the reliable forecast model was constructed.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.03a
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• pp.871-876
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• 2010

When surface Concrete Face Rock fill Dam constructs than existent center core type rock fill dam, it is much prevalent form in domestic these day by quality control of that is profitable and weather condition etc. of coreZone. C.F.R.D is less research about seismic survey(Refractional Seismic Prospectin, Resistivity Prospecting) of levee body than fill dam. Thus as C.F.R.D seismic survey is less, safety of that consist is short most development flue is high reason. That is not checking target of minuteness safety diagnosis and so on by short operation period. Wish to analyze inquiry incidental and difference with center core type dam and acquire C.F.R.D preservation administration upper necessary inquiry condition forward hereafter.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.29-31
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• 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%.

• Journal of Energy Engineering
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• v.6 no.1
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• pp.68-76
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• 1997

This paper presents a new neural network training algorithm which reduces the required training time considerably and overcomes many of the shortcomings presented by the conventional back-propagation algorithm. The algorithm uses a modified form of the back-propagation algorithm to minimize the mean squared error between the desired and actual outputs with respect to the inputs to the nonlinearities. Artificial Neural Network (ANN) model using the new algorithm is applied to forecast the short-term electric load. Inputs to the ANN are past loads and the output of the ANN is the hourly load forecast for a given day.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.914-916
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• 1997

This paper presents the development of an Artificial Neural Networks(ANN) for Short-Term Load Forecasting(STLF). First, used historical load data is divided into 5 patterns for the each seasonal data using Kohonen networks. Second, classified data is used as inputs of Back-propagation networks for next day hourly load forecasting. The proposed method was tested with KEPCO hourly record (1994-95) and we obtained desirable results.

• Asian-Australasian Journal of Animal Sciences
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• v.10 no.2
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• pp.229-232
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• 1997

Effects of short-term treatment with somidobove (recombinantly produced bovine somatotropin, BST) on estrous cyclicity and fertility were studied in dairy buffaloes. Twenty buffaloes of Nili-Ravi breed calving during the same season were assigned to either control (n=8) or treated group (n=12). The buffaloes of treated group received single infection (prolonged release) of 320 mg of somidobove on day-60 postpartum. The mean values for interval to first postpartum estrus, first service conception rate, services per conception, service period and calving interval for the treated group were 96.4 days, 66.7%, 1.70, 164 days and 473 days, respectively. The corresponding values for the control group were 92.5 days, 62.5%, 1.87, 135 days and 439 days. Means of all variables did not differ between control and treated group (p > 0.05). Three buffaloes of the control and four buffaloes of the treated group did not conceive at first service. Out of these, two buffaloes of control and one buffalo of treated group exhibited normal estrous cycles. It is concluded from these data that short term BST-treatment has no adverse effect on reproductive functions of dairy buffaloes.

• Journal of Korean Biological Nursing Science
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• v.13 no.2
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• pp.179-184
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• 2011

Purpose: The purpose of this study was to examine the effect of short-term undernutrition on muscle weight and Type I and II fiber cross-sectional area of hindlimb muscles in undernourished rats. Methods: Adult male Sprague-Dawley rats were randomly assigned to one of two groups: The undernourished (UN) group (n=9) and the control (C) group (n=9). A control group was allowed to have water and pellet ad libitum for 5 days. Undernutrition was induced by providing 32% of total intake of the control group for 5 days. Body weight of two groups and food intake of the control group were measured every day. At 6 days all rats were anesthetized and soleus, plantaris and gastrocnemius muscles, and liver were dissected. Body weight, food intake, muscle weight, liver weight and cross-sectional area were determined. Results: The UN group at 6 days after undernutrition showed significant decreases, as compared to the control group in body weight, liver weight, muscle weight of soleus, plantaris, and gastrocnemius, and Type I fiber cross-sectional area of soleus and gastrocnemius muscles and Type II fiber cross-sectional area of plantaris and gastrocnemius muscles. Conclusion: Hindlimb muscle atrophy occurs from the short-term undernutrition.

• Atmosphere
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• v.17 no.4
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• pp.327-337
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• 2007

In this study, we examined the new ensemble training approach to reduce the systematic error and improve prediction skill of wind by using the Short-range Ensemble prediction system (SENSE), which is the mesoscale multi-model ensemble prediction system. The SENSE has 16 ensemble members based on the MM5, WRF ARW, and WRF NMM. We evaluated the skill of surface wind prediction compared with AWS (Automatic Weather Station) observation during the summer season (June - August, 2006). At first stage, the correction of initial state for each member was performed with respect to the observed values, and the corrected members get the training stage to find out an adaptive weight function, which is formulated by Root Mean Square Vector Error (RMSVE). It was found that the optimal training period was 1-day through the experiments of sensitivity to the training interval. We obtained the weighted ensemble average which reveals smaller errors of the spatial and temporal pattern of wind speed than those of the simple ensemble average.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.1882-1890
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• 2014

This paper proposes an augmented Lagrange Hopfield network (ALHN) based method for solving multi-objective short term fixed head hydrothermal scheduling problem. The main objective of the problem is to minimize both total power generation cost and emissions of $NO_x$, $SO_2$, and $CO_2$ over a scheduling period of one day while satisfying power balance, hydraulic, and generator operating limits constraints. The ALHN method is a combination of augmented Lagrange relaxation and continuous Hopfield neural network where the augmented Lagrange function is directly used as the energy function of the network. For implementation of the ALHN based method for solving the problem, ALHN is implemented for obtaining non-dominated solutions and fuzzy set theory is applied for obtaining the best compromise solution. The proposed method has been tested on different systems with different analyses and the obtained results have been compared to those from other methods available in the literature. The result comparisons have indicated that the proposed method is very efficient for solving the problem with good optimal solution and fast computational time. Therefore, the proposed ALHN can be a very favorable method for solving the multi-objective short term fixed head hydrothermal scheduling problems.