• Structural Engineering and Mechanics
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• v.28 no.4
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• pp.357-372
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• 2008

This study investigates effect of uncertainty in natural vibration period on the seismic demand. It is shown that since this uncertainty affects the acceleration and displacement responses differently, two ratios, one relating peak acceleration responses and the other relating the peak displacement responses, are not equal and both must be employed in evaluating and defining the critical seismic demand. The evaluation of the ratios is carried out using more than 200 strong ground motion records. The results suggest that the uncertainty in the natural vibration period impacts significantly the statistics of the ratios relating the peak responses. By using the statistics of the ratios, a procedure and sets of empirical equations are developed for estimating the probability consistent seismic demand for both linear and nonlinear systems.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.63 no.3
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• pp.189-194
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• 2014

Due to the increasing of power consumption, it is difficult to construct accurate prediction model for daily peak power demand. It is very important work to know power demand in next day for manager and control power system. In this research, we develop a daily peak power demand prediction method based on hybrid type composed of AR and Neuro-Fuzzy model. Using data sets between 2006 and 2010 in Korea, the proposed method has been intensively tested. As the prediction results, we confirm that the proposed method makes it possible to effective estimate daily peak power demand than conventional methods.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.63 no.4
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• pp.307-311
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• 2014

Due to the increasing of power consumption, it is difficult to construct accurate prediction model for daily peak power demand. It is very important work to know power demand in next day for manager and control power system. In this research, we develop a daily peak power demand prediction method considering of characteristics of day of week. The proposed method is composed of liner model based on AR model and nonlinear model based on ELM to resolve the limitation of a single model. Using data sets between 2006 and 2010 in Korea, the proposed method has been intensively tested. As the prediction results, we confirm that the proposed method makes it possible to effective estimate daily peak power demand than conventional methods.

• Journal of the Korean Society for Railway
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• v.16 no.5
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• pp.372-378
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• 2013

This paper presents an approximate model for computing the peak demand power in a metro railway system. The peak demand of substations can be calculated using the current vector iteration method. But the existing method requires many repeated calculations to determine the peak demand power, which makes it difficult to apply to the real-time peak power control problem. In this paper, we assume that none of the conditions vary except source impedance and make an approximate model for rapid calculation based on changes in the impedance of the power substation. The proposed model result is approximately the same as the existing model, which is demonstrated through simulation.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.2
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• pp.87-96
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• 2004

This paper summarizes the research results of the load management for pole transformers done in 1997-1998 and 2000-2002. The purpose of the research is to enhance the accuracy of peak load estimation in pole transformers. We concentrated our effort on the acquisition of massive actual load data for modifying the load regression coefficients, which related to the peak load estimation of lamp-use customers, and adjusting the demand-factor coefficients, which used for the peak load prediction of motor-use customers. To enhance the load regression equations, the 264 load data acquisition devices are equipped to the sample pole transformers. For the modification of demand factor coefficients, the peak load currents are measured in each customer and pole transformer for 13 KEPCO (Korea Electric Power Corporation) distribution branch offices. Case studies for 50 sample pole transformers show that the proposed coefficients could reduce estimating error of the peak load for pole transformers, compared with the conventional one.

• Journal of Korean Society of Water and Wastewater
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• v.30 no.3
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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.

• Proceedings of the KIEE Conference
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• 2001.05a
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• pp.197-200
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• 2001

In this paper, a novel demand control technique using composite fuzzy model is developed for the peak load control. The outcome of the study clearly indicates that the composite model approach can be used as an attractive and effective means of the peak demand control.

• Steel and Composite Structures
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• v.33 no.5
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• pp.641-652
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• 2019

This paper investigates the effect of aftershocks on the seismic performance of self-centering (SC) prestressed concrete frames using the probabilistic seismic demand analysis methodology. For this purpose, a 4-story SC concrete frame and a conventional reinforced concrete (RC) frame are designed and numerically analyzed through nonlinear dynamic analyses based on a set of as-recorded mainshock-aftershock seismic sequences. The peak and residual story drifts are selected as the demand parameters. The probabilistic seismic demand models of the SC and RC frames are compared, and the SC frame is found to have less dispersion of peak and residual story drifts. The results of drift demand hazard analyses reveal that the SC frame experiences lower peak story drift hazards and significantly reduced residual story drift hazards than the RC frame when subjected to the mainshocks only or the mainshock-aftershock sequences, which demonstrates the advantages of the SC frame over the RC frame. For both the SC and RC frames, the influence of as-recorded aftershocks on the drift demand hazards is small. It is shown that artificial aftershocks can produce notably increased drift demand hazards of the RC frame, while the incremental effect of artificial aftershocks on the drift demand hazards of the SC frame is much smaller. It is also found that aftershock polarity does not influence the drift demand hazards of both the SC and RC frames.

• Proceedings of the KIEE Conference
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• 1993.07b
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• pp.1088-1090
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• 1993

This paper shows efficient peak demand control method in office buildings. With a rapid growth of national economics and living standard, electrical energy consumption markedly increased. Expecially, it is increased electrical energy comsumption in the office buildings and thus an energy conservation through efficient use of electricity became more important. From the data of electric equipment capacity and electric power consumption for 96 buildings, current levels of demand factor and a growth trend of peak loads by office buildings were surveyed and analyzed. In addition the efficient peak demand control method in office buildings were studied.

• The Korean Journal of Applied Statistics
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• v.26 no.2
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• pp.349-360
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• 2013

Forecasting the daily peak load for electricity demand is an important issue for future power plants and power management. We first introduce several time series models to predict the peak load for electricity demand and then compare the performance of models under the RMSE(root mean squared error) and MAPE(mean absolute percentage error) criteria.