• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.9
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• pp.1186-1191
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• 2014

Weekly and monthly electric load forecasting are essential for the generator maintenance plan and the systematic operation of the electric power reserve. This paper proposes the weekly maximum electric load forecasting model for 104 weeks with the multiple regression model. Input variables of the multiple regression model are temperatures and GDP that are highly correlated with electric loads. The weekly variable is added as input variable to improve the accuracy of electric load forecasting. Test results show that the proposed algorithm improves the accuracy of electric load forecasting over the seasonal autoregressive integrated moving average model. We expect that the proposed algorithm can contribute to the systematic operation of the power system by improving the accuracy of the electric load forecasting.

• 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%.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.3
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• pp.109-117
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• 2002

High accuracy of the load forecasting for power systems improves the security of the power system and generation cost. However, the forecasting problem is difficult to handle due to the nonlinear and the random-like behavior of system loads as well as weather conditions and variation of economical environments. So far. many studies on the problem have been made to improve the prediction accuracy using deterministic, stochastic, knowledge based and artificial neural net(ANN) method. In the conventional load forecasting method, the load forecasting maximum error occurred for the holidays on Saturday and Monday. In order to reduce the load forecasting error of the daily peak load for the holidays on Saturday and Monday, fuzzy concept and linear regression theory have been adopted into the load forecasting problem. The proposed algorithm shows its good accuracy that the average percentage errors are 2.11% in 1996 and 2.84% in 1997.

• IE interfaces
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• v.16 no.1
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• pp.16-26
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• 2003

In Korea, cooling power load, which occupies about 20% of peak load in 2000 and fluctuates depending on the popular usage of air conditioning systems, has been recently the focus of the load management. The first work of KEPCO (Korea Electric Power Corporation) to regulate cooling load as low as possible was to estimate its approximate scale and to develop the indirect methods to estimate it from the available time series data for the average hourly loads. However, KEPCO would like to have their methods improved both theoretically and practically. In this paper, we analyze their current indirect methods and detect their faults to design better indirect estimation methods. Under one of the assumptions of "no cooling load in April or May", the linear relationship between basic loads and GDP's, and the normalized seasonal factors of the Winters' multiplicative seasonal model, we provide ten indirect estimation methods in total and suggest the estimated cooling load(1988-1999) based on our various indirect methods.

• Journal of the Korea Concrete Institute
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• v.13 no.6
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• pp.568-574
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• 2001

From measured responses of concrete three-point bend tests, the average values of the responses have been calculated. The fracture behavior of continuously propagating concrete crack has been analyzed from the average responses. The experimental parameters of this study were the initial notch sizes of 25.4㎜ and 6.4 ㎜ and the processing times of 2,000 sec. and 20 sec . The different notch sizes were used for the effects of the size of fracture process zone and specimen geometry, and the processing times for those of initial creep. However the load-point displacement rate in this study did not affect the experimental responses seriously. The average loads were calculated from the average external work of a series of tests, and average crack lengths were determined by using strain gages. Before the peak load, the resistance curve could be determined from the size of fracture process zone, but unstable crack propagation of 88㎜ occurred at the load-point displacement of 0.088∼0.154㎜ after the peak load. The average fracture energy density G$\_$F/$\^$ave/ = 115 N/m occurred during the unstable crack propagation. The fracture process zones were fully developed at the crack length of 111㎜, and the sizes of fracture process zone for initial notches of 25.4㎜ and 6.4㎜ were 86㎜ and 105㎜, respectively. Average fracture energy densities of the resistance curves after full development of fracture process zone were 229 N/m for the initial notch of 25.4㎜ and 284 N/m for 6.4㎜. The values were more than twice of G$\_$F/$\^$ave/.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.4
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• pp.482-488
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• 2013

Load forecasting is essential to the economical and the stable power system operations. In general, the forecasting days can be classified into weekdays, weekends, special days and special light-load periods in short-term load forecast. Special light-load periods are the consecutive holidays such as Lunar New Years holidays, Korean Thanksgiving holidays and summer special light-load period. For the weekdays and the weekends forecast, the conventional methods based on the statistics are mainly used and show excellent results for the most part. The forecast algorithms for special days yield good results also but its forecast error is relatively high than the results of the weekdays and the weekends forecast methods. For summer special light-load period, none of the previous studies have been performed ever before so if the conventional methods are applied to this period, forecasting errors of the conventional methods are considerably high. Therefore, short-term load forecast for summer special light-load period have mainly relied on the experience of power system operation experts. In this study, the trends of load profiles during summer special light-load period are classified into three patterns and new forecast algorithms for each pattern are suggested. The proposed method was tested with the last ten years' summer special light-load periods. The simulation results show the excellent average forecast error near 2%.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.3
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• pp.341-351
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• 1996

The values for the average and rms currents in a three-level PWM inverter are required in order to select the various components such as power semiconductor devices, capacitors and reactors of inverter circuit. And those are very useful for the designing of the heat sink. In this paper, therefore, the simple current equations are proposed for a three-level PWM inverter. Analysis of inverter current waveforms indicate that the average and rms inverter currents are dependent on the load power factor and PWM modulation index (Ma). Error analysis and experiment results verify the effectiveness of the proposed current equations. (author). refs., figs., tabs.

• The Magazine of the Society of Air-Conditioning and Refrigerating Engineers of Korea
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• v.14 no.2
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• pp.125-137
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• 1985

Standard weather data for Seoul has been developed for use in computer calculations for energy requirements, and the 8760 sequential hourly values for seven weather elements have been placed in magnetic tape and cards. Applying the method determining average month, developed by Japanese Society of Heating, Air-Conditioning and Sanitary Engineers, the standard year data have been selected from the monthly average values for three weather elements during the 10year period of 1971 through 1980. The followings are obtained. 1. The Test Reference Year, consisting of 12 months chosen from different calendar years, has been determined, and TRY tape which contains seven weather elements has been prepared. 2. The Typical Weather Year, which means a year close to the average value, is the year of 1978 during the above 10 year period. 3. During the period, Winter Season needs the maximum heating load is from Dec. 1976 to Mar. 1977 and Summer Season needs the maximum cool ins load is from Jun. to Sep, 1978.

• Journal of the Korean GEO-environmental Society
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• v.18 no.9
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• pp.19-31
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• 2017

In case of USA, the drilled shaft and the driven pile in the field showed a good correlation in the analysis of the bearing capacity between the dynamic load test and the static load test. However, in Korea, we mainly install the bored pile, which is not widely used overseas and we tried to confirm the reliability of the dynamic load test on the bored pile, because many people questioned the reliability of it. In this study, load tests were carried out on PHC bored piles in LH field (Cheonan, Incheon, Uijeongbu), and the bearing capacity of the dynamic load test (EOID 7times, Restrike 7times) and the static load test (7times) were compared and analyzed. As a result, the average of the bearing capacity of the static load test was 27% higher than that of the dynamic load test (reliability : 0.73, coefficient of variation : 0.3). And the average of the bearing capacity of the static load test (Davisson) was 27% higher than that of the bearing capacity of the dynamic load test (Davisson) (reliability : 0.73, coefficient of variation : 0.2). To reduce the difference between the bearing capacity of the dynamic load test and the static load test, we proposed modified bearing capacity of dynamic load test (base bearing capacity of EOID + skin frictional force of restrike) and difference between the bearing capacities was reduced to 9% (reliability : 0.91, coefficient of variation : 0.2). And the coefficient of variation was reduced to 0.2 and the consistency of analysis increased.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3394-3408
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• 2014

Conventional mobile state (MS) and base station (BS) association based on average signal strength often results in imbalance of cell load which may require more powerful processor at BSs and degrades the perceived transmission rate of MSs. To deal with this problem, a Markov decision process (MDP) for load balancing in a multi-cell system with multi-carriers is formulated. To solve the problem, exploiting Sarsa algorithm of on-line learning type [12], ${\alpha}$-controllable load balancing algorithm is proposed. It is designed to control tradeoff between the cell load deviation of BSs and the perceived transmission rates of MSs. We also propose an ${\varepsilon}$-differential soft greedy policy for on-line learning which is proven to be asymptotically convergent to the optimal greedy policy under some condition. Simulation results verify that the ${\alpha}$-controllable load balancing algorithm controls the behavior of the algorithm depending on the choice of ${\alpha}$. It is shown to be very efficient in balancing cell loads of BSs with low ${\alpha}$.