• The Transactions of the Korean Institute of Electrical Engineers
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• v.35 no.5
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• pp.193-198
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• 1986

In this paper, 1-168 hours ahead load prediction algorithm is developed for power system economic weekly operation. Total load is composed of three components, which are base load, week load and weather-sensitive load. Base load and week load are predicted by moving average and exponential smoothing method, respectively. The days of moving average and smoothing constant are optimally determined. Weather-sensitive load is modeled by linear form. The paramiters of weather load model are estimated by exponentially weighted recursive least square method. The load prediction of special day is very tedious, difficult and remains many problems which should be improved. Test results are given for the day of different types using the actual load data of KEPCO.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.11
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• pp.485-491
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• 2006

In this paper, we study the problem of model selection for Support Vector Machine(SVM) predictor for short-term load forecasting. The model selection amounts to tuning SVM parameters, such as the cost coefficient C and kernel parameters and so on, in order to maximize the prediction performance of SVM. We propose that Cross-Validation method can be used as a model selection algorithm for SVM-based load forecasting technique. Through the various experiments on several data sets, we found that the difference between the prediction error of SVM using Cross-Validation and that of ideal SVM is less than 5%. This shows that SVM parameters for load forecasting can be efficiently tuned by using Cross-Validation.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.8
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• pp.1487-1494
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• 2002

In this study, to propose the prediction method of the crack growth under flight-simulation loading, crack growth tests are conducted on 2124-7851 aluminum alloy specimens. The prediction of crack growth under flight-simulation loading is performed by the stochastic crack growth model which was developed in previous study. First of all, to reduce the complex load history into a number of constant amplitude events, rainflow counting is applied to the flight-simulation loading wave. The crack growth, then, is predicted by the stochastic crack growth model that can describe the load interaction effect as well as the variability in crack growth process. The material constants required in this model are obtained from crack growth tests under constant amplitude loading and single tensile overload. The curves predicted by the proposed model well describe the crack growth behavior under flight-simulation loading and agree with experimental data. In addition, this model well predicts the variability of fatigue lives.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.28 no.9
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• pp.355-360
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• 2016

Continuing to the modeling of heating load, this paper, as the second part of consecutive works, presents LS-SVM (least square support vector machine) based model of winter time apartment hot water supply load in a district heating system, so as to be used in prediction of heating energy usage. Similar, but more severely, to heating load, hot water supply load varies in highly nonlinear manner. Such nonlinearity makes analytical model of it hardly exist in the literatures. LS-SVM is known as a good modeling tool for the system, especially for the nonlinear system depended by many independent factors. We collect 26,208 data of hot water supply load over a 13-week period in winter time, from 12 heat exchangers in seven different apartments. Then part of the collected data were used to construct LS-SVM based model and the rest of those were used to test the formed model accuracy. In modeling, we first constructed the model of district heating system's hot water supply load, using the unit heating area's hot water supply load of seven apartments. Such model will be used to estimate the total hot water supply load of which the district heating system needs to provide. Then the individual apartment hot water supply load model is also formed, which can be used to predict and to control the energy consumption of the individual apartment. The results obtained show that the total hot water supply load, which will be provided by the district heating system in winter time, can be predicted within 10% in MAPE (mean absolute percentage error). Also the individual apartment models can predict the individual apartment energy consumption for hot water supply load within 10% ~ 20% in MAPE.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.533-538
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• 2004

This paper proposes a systematic method to develop short-term electrical load forecasting systems using neuro-fuzzy models. The proposed system predicts the electrical loads with the lead times of 1 hour, 24 hour, and 168 hour. To do so, the load forecasting system first builds an initial structure off-line for each hour of four day types and then stores the resultant initial structures in the initial structure bank. 96 initial structures are constructed for each prediction lead time. Whenever a prediction needs to be made, the proposed system initializes the neuro-fuzzy model with the appropriate initial structure stored and trains the initialized prediction modell. To improve the performance of the prediction system in terms of accuracy and reliability at the same time, the prediction model employs only two inputs. It makes possible to interpret the fuzzy rules to be learned. In order to demonstrate the viability of the proposed method, we develop a load forecasting system by using the real load data collected during 1996 and 1997 at KEPCO. Simulation results reveal that the prediction system developed in this paper can achieve a remarkable improvement on both accuracy and reliability

• Transactions of Materials Processing
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• v.18 no.4
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• pp.323-328
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• 2009

The prediction of drawing load is very important in the drawing process. However, it is not easy to calculate the drawing load for the shape drawing process through a theoretical model because of a complex arbitrary final cross section shape. The purpose of this study is to predict drawing load in shape drawing process. The cross section of product is divided with small angle as much as similar with fan-shape. The drawing load of each section was calculated by theoretical model of round to round drawing process. And the shape drawing load was determined by summation of drawing load of each section. The effectiveness of the proposed method was verified through the FE analysis and shape drawing experiment. It had a good agreement between proposed method, FE analysis and experiment within about 3% errors.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.32 no.6
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• pp.191-198
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• 1983

This paper describes the aplication of different model which can be used for short-term load prediction. The model is based on Bohlin's approach to first develop a load profile model representing the nominal load component and the Box-Jenkins approach is used to predict residuals. An on-line algorithm using Kalman Filter and Time Series is implemented for and hour-ahead prediction. In the Kalman Filter system equation and measurement equation were fixed and parameters of Time Series were varied week after week. A set of data for Korea Electric Power Corporation from April to June 1981 was used for the evaluation of the model. As the result of this simulation 1.2% rms error was acquired.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.11
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• pp.1590-1597
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• 2013

Electric power is an important part in economic development. Moreover, an accurate load forecast can make a financing planning, power supply strategy and market research planned effectively. This paper used the fuzzy logic system to predict the regional electric power load. To design the fuzzy prediction system, the correlation-based clustering algorithm and TSK fuzzy model were used. Also, to improve the prediction system's capability, the moving average technique and relative increasing rate were used in the preprocessing procedure. Finally, using four regional electric power load in Taiwan, this paper verified the performance of the proposed system and demonstrated its effectiveness and usefulness.

• Smart Structures and Systems
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• v.24 no.4
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• pp.507-524
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• 2019

A Bayesian dynamic linear model (BDLM) is presented for a data-driven analysis for response prediction and load effect separation of a revolving auditorium structure, where the main loads are self-weight and dead loads, temperature load, and audience load. Analyses are carried out based on the long-term monitoring data for static strains on several key members of the structure. Three improvements are introduced to the ordinary regression BDLM, which are a classificatory regression term to address the temporary audience load effect, improved inference for the variance of observation noise to be updated continuously, and component discount factors for effective load effect separation. The effects of those improvements are evaluated regarding the root mean square errors, standard deviations, and 95% confidence intervals of the predictions. Bayes factors are used for evaluating the probability distributions of the predictions, which are essential to structural condition assessments, such as outlier identification and reliability analysis. The performance of the present BDLM has been successfully verified based on the simulated data and the real data obtained from the structural health monitoring system installed on the revolving structure.

• International Journal of Precision Engineering and Manufacturing
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• v.3 no.4
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• pp.72-77
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• 2002

The load interaction effect can be best illustrated by the phenomenon of overload retardation. Some prediction methods for retardation are reviewed and the problems discussed in the present paper. The so-called under-load effect much of the retardation disappears if a very low level minimum stress follows the overload, is also of importance for a prediction model to work properly under random load spectrum. The concept of Interactive Zone (IZ) fully considering reversed plasticity during unloading was discussed. This IZ concept can be combined with existing models to derive some improved models that can naturally take account of the under-load effect. Some simulations by IZ improved models for test under complex load sequences including multiple overloads and both over/under loads are compared with test results. It is seen that the improvement by IZ concept greatly enhanced the ability of existing models to accommodate complex load interaction effects.