• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.6
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• pp.1041-1045
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• 2010

Short-term electric load forecasting of power systems is essential for the power system stability and the efficient power system operation. An accurate load forecasting scheme improves the power system security and saves some economic losses in power system operations. Due to scarcity of the historical same type of holiday load data, most big electric load forecasting errors occur on load forecasting for the holidays. The fuzzy linear regression model has showed good accuracy for the load forecasting of the holidays. However, it is not good enough to forecast the load of the election day. The concept of the load variation rate for the load forecasting of the election day is introduced. The proposed algorithm shows its good accuracy in that the average percentage error for the short-term 24 hourly loads forecasting of the election days is 2.27%. The accuracy of the proposed 24 hourly loads forecasting of the election days is compared with the fuzzy linear regression method. The proposed method gives much better forecasting accuracy with overall average error of 2.27%, which improved about average error of 2% as compared to the fuzzy linear regression method.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.83-93
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• 2020

This empirical research aims to modeling and improving the forecasting accuracy of the volatility pattern by employing the Saudi Arabia stock market (Tadawul)by studying daily closed price index data from October 2011 to December 2019 with a number of observations being 2048. In order to achieve significant results, this study employs many mathematical functions which are non-linear spectral model Maximum overlapping Discrete Wavelet Transform (MODWT) based on the best localized function (Bl14), autoregressive integrated moving average (ARIMA) model and generalized autoregressive conditional heteroskedasticity (GARCH) models. Therefore, the major findings of this study show that all the previous events during the mentioned period of time will be explained and a new forecasting model will be suggested by combining the best MODWT function (Bl14 function) and the fitted GARCH model. Therefore, the results show that the ability of MODWT in decomposition the stock market data, highlighting the significant events which have the most highly volatile data and improving the forecasting accuracy will be showed based on some mathematical criteria such as Mean Absolute Percentage Error (MAPE), Mean Absolute Scaled Error (MASE), Root Means Squared Error (RMSE), Akaike information criterion. These results will be implemented using MATLAB software and R- software.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2009.05b
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• pp.45-48
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• 2009

The performance of concrete mainly depend upon its water-cement ratio. If water percentage is excess in concrete, it may cause the degradation of performance. Because of these reasons, the change of water content is managed by using various evaluation method of unit water content. And criterion for the change of water content is regulated and used. However the criterion is set only considering production error and measurement error but criterion does not consider performance degradation of concrete. Therefore this study tries to investigate degradation of performance while adding extra water by artificial manipulation or management error in concrete, The contents of extra water for tests are set as 0, 20, 40, 60kg/m3, to examine the performance degradation of concrete, strength, shrinkage, cracks, carbonation are tested This study conclude that, when extra water content is excess than 20kg/m3 in concrete, then performance level of concrete declined rapidly. 80 it is very important to maintain quality of concrete for its better performance.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.585-588
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• 2019

Predictions of Energy Consumption for Industries gain an important place in energy management and control system, as there are dynamic and seasonal changes in the demand and supply of energy. This paper presents and discusses the predictive models for energy consumption of the steel industry. Data used includes lagging and leading current reactive power, lagging and leading current power factor, carbon dioxide (tCO2) emission and load type. In the test set, four statistical models are trained and evaluated: (a) Linear regression (LR), (b) Support Vector Machine with radial kernel (SVM RBF), (c) Gradient Boosting Machine (GBM), (d) random forest (RF). Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used to measure the prediction efficiency of regression designs. When using all the predictors, the best model RF can provide RMSE value 7.33 in the test set.

• Nuclear Engineering and Technology
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• v.50 no.7
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• pp.1154-1159
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• 2018

Precise prediction of the radiation interaction position in scintillators plays an important role in medical and industrial imaging systems. In this research, the incident position of the gamma rays was predicted precisely in a plastic rod scintillator by using attenuation technique and multilayer perceptron (MLP) neural network, for the first time. Also, this procedure was performed using nonlinear regression (NLR) method. The experimental setup is comprised of a plastic rod scintillator (BC400) coupled with two PMTs at two sides, a $^{60}Co$ gamma source and two counters that record count rates. Using two proposed techniques (ANN and NLR), the radiation interaction position was predicted in a plastic rod scintillator with a mean relative error percentage less than 4.6% and 14.6%, respectively. The mean absolute error was measured less than 2.5 and 5.5. The correlation coefficient was calculated 0.998 and 0.984, respectively. Also, the ANN technique was confirmed by leave-one-out (LOO) method with 1% error. These results presented the superiority of the ANN method in comparison with NLR and the other methods. The technique and set up used are simpler and faster than other the previous position sensitive detectors. Thus, the time, cost and shielding and electronics requirements are minimized and optimized.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.440-449
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• 2022

In recent years, the air pollution and Air Quality Index (AQI) has been a pivotal point for researchers due to its effect on human health. Various research has been done in predicting the AQI but most of these studies, either lack dense temporal data or cover one or two air pollutant elements. In this paper, a hybrid Convolutional Neural approach integrated with recurrent neural network architecture (CNN-LSTM), is presented to find air pollution inference using a multivariate air pollutant elements dataset. The aim of this research is to design a robust and real-time air pollutant forecasting system by exploiting a neural network. The proposed approach is implemented on a 24-month dataset from Seoul, Republic of Korea. The predicted results are cross-validated with the real dataset and compared with the state-of-the-art techniques to evaluate its robustness and performance. The proposed model outperforms SVM, SVM-Polynomial, ANN, and RF models with 60.17%, 68.99%, 14.6%, and 6.29%, respectively. The model performs SVM and SVM-Polynomial in predicting O3 by 78.04% and 83.79%, respectively. Overall performance of the model is measured in terms of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE).

• Structural Engineering and Mechanics
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• v.86 no.5
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• pp.621-633
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• 2023

Stability analysis and support system estimation of the Beheshtabad water transmission tunnel is investigated in this research. A combination approach based on the rock mass rating (RMR) and rock mass quality index (Q) is used for this purpose. In the first step, 40 datasets related to the petrological, structural, hydrological, physical, and mechanical properties of tunnel host rocks are measured in the field and laboratory. Then, RMR, Q, and height of influenced zone above the tunnel roof are computed and sorted into five general groups to analyze the tunnel stability and determine its support system. Accordingly, tunnel stand-up time, rock load, and required support system are estimated for five sorted rock groups. In addition, various empirical relations between RMR and Q i.e., linear, exponential, logarithmic, and power functions are developed using the analysis of variance (ANOVA). Based on the significance level (sig.), determination coefficient (R²) and Fisher-test (F) indices, power and logarithmic equations are proposed as the optimum relations between RMR and Q. To validate the proposed relations, their results are compared with the results of previous similar equations by using the variance account for (VAF), root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE) indices. Comparison results showed that the accuracy of proposed RMR-Q relations is better than the previous similar relations and their outputs are more consistent with actual data. Therefore, they can be practically utilized in designing the tunneling projects with an acceptable level of accuracy and reliability.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.3
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• pp.73-78
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• 2001

BP neural network model and multiple-regression model were composed for forecasting the special-days load. Special-days load was forecasted using that neural network model made use of pattern conversion ratio and multiple-regression made use of weekday-change ratio. This methods identified the suitable as that special-days load of short and long term was forecasted with the weekly average percentage error of 1∼2[%] in the weekly peak load forecasting model using pattern conversion ratio. But this methods were hard with special-days load forecasting of summertime. therefore it was forecasted with the multiple-regression models. This models were used to the weekday-change ratio, and the temperature-humidity and discomfort-index as explanatory variable. This methods identified the suitable as that compared forecasting result of weekday load with forecasting result of special-days load because months average percentage error was alike. And, the fit of the presented forecast models using statistical tests had been proved. Big difficult problem of peak load forecasting had been solved that because identified the fit of the methods of special-days load forecasting in the paper presented.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.3
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• pp.1488-1494
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• 2011

In this paper, we propose an algorithm using linear regression model that forecasts the demand of heated water in winter. To supply heated water to apartments, stores and office buildings, Korea District Heating Corp.(KDHC) operates boilers including electric power generators. In order to operate facilities generating heated water economically, it is essential to forecast daily demand of heated water with accuracy. Analysis of history data of Kangnam Branch of KDHC in 2006 and 2007 reveals that heated water supply on previous day as well as temperature are the most important factors to forecast the daily demand of heated water. When calculated by the proposed regression model, mean absolute percentage error for the demand of heated water in winter of the year 2006 through 2009 does not exceed 3.87%.

• International Journal of Highway Engineering
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• v.16 no.5
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• pp.59-66
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• 2014

PURPOSES : This study is to predict the Sound Pressure Level(SPL) obtained from the Noble Close ProXimity(NCPX) method by using the Extended Kalman Filter Algorithm employing the taylor series and Linear Regression Analysis based on the least square method. The objective of utilizing EKF Algorithm is to consider stochastically the effect of error because the Regression analysis is not the method for the statical approach. METHODS : For measuring the friction noise between the surface and vehicle's tire, NCPX method was used. With NCPX method, SPL can be obtained using the frequency analysis such as Discrete Fourier Transform(DFT), Fast Fourier Transform(FFT) and Constant Percentage Bandwidth(CPB) Analysis. In this research, CPB analysis was only conducted for deriving A-weighted SPL from the sound power level in terms of frequencies. EKF Algorithm and Regression analysis were performed for estimating the SPL regarding the vehicle velocities. RESULTS : The study has shown that the results related to the coefficient of determination and RMSE from EKF Algorithm have been improved by comparing to Regression analysis. CONCLUSIONS : The more the vehicle is fast, the more the SPL must be high. But in the results of EKF Algorithm, SPLs are irregular. The reason of that is the EKF algorithm can be reflected by the error covariance from the measurements.