The Experts below are selected from a list of 583890 Experts worldwide ranked by ideXlab platform
Gilsoo Jang - One of the best experts on this subject based on the ideXlab platform.
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short term load forecasting for the holidays using fuzzy linear Regression Method
IEEE Power Engineering Society General Meeting 2005, 2005Co-Authors: Kyungbin Song, Youngsik Baek, Dug Hun Hong, Gilsoo JangAbstract:Summary form only given. Average load forecasting errors for the holidays are much higher than those for weekdays. So far, many studies on the short-term load forecasting have been made to improve the prediction accuracy using various Methods such as deterministic, stochastic, artificial neural net (ANN) and neural network-fuzzy Methods. In order to reduce the load forecasting error of the 24 hourly loads for the holidays, the concept of fuzzy Regression analysis is employed in the short-term load forecasting problem. According to the historical load data, the same type of holiday showed a similar trend of load profile as in previous years. The fuzzy linear Regression model is made from the load data of the previous three years and the coefficients of the model are found by solving the mixed linear programming problem. The proposed algorithm shows good accuracy, and the average maximum percentage error is 3.57% in the load forecasting of the holidays for the years of 1996-1997.
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short term load forecasting for the holidays using fuzzy linear Regression Method
IEEE Transactions on Power Systems, 2005Co-Authors: Kyungbin Song, Youngsik Baek, Dug Hun Hong, Gilsoo JangAbstract:Average load forecasting errors for the holidays are much higher than those for weekdays. So far, many studies on the short-term load forecasting have been made to improve the prediction accuracy using various Methods such as deterministic, stochastic, artificial neural net (ANN) and neural network-fuzzy Methods. In order to reduce the load forecasting error of the 24 hourly loads for the holidays, the concept of fuzzy Regression analysis is employed in the short-term load forecasting problem. According to the historical load data, the same type of holiday showed a similar trend of load profile as in previous years. The fuzzy linear Regression model is made from the load data of the previous three years and the coefficients of the model are found by solving the mixed linear programming problem. The proposed algorithm shows good accuracy, and the average maximum percentage error is 3.57% in the load forecasting of the holidays for the years of 1996-1997.
Kyungbin Song - One of the best experts on this subject based on the ideXlab platform.
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short term load forecasting for the holidays using fuzzy linear Regression Method
IEEE Power Engineering Society General Meeting 2005, 2005Co-Authors: Kyungbin Song, Youngsik Baek, Dug Hun Hong, Gilsoo JangAbstract:Summary form only given. Average load forecasting errors for the holidays are much higher than those for weekdays. So far, many studies on the short-term load forecasting have been made to improve the prediction accuracy using various Methods such as deterministic, stochastic, artificial neural net (ANN) and neural network-fuzzy Methods. In order to reduce the load forecasting error of the 24 hourly loads for the holidays, the concept of fuzzy Regression analysis is employed in the short-term load forecasting problem. According to the historical load data, the same type of holiday showed a similar trend of load profile as in previous years. The fuzzy linear Regression model is made from the load data of the previous three years and the coefficients of the model are found by solving the mixed linear programming problem. The proposed algorithm shows good accuracy, and the average maximum percentage error is 3.57% in the load forecasting of the holidays for the years of 1996-1997.
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short term load forecasting for the holidays using fuzzy linear Regression Method
IEEE Transactions on Power Systems, 2005Co-Authors: Kyungbin Song, Youngsik Baek, Dug Hun Hong, Gilsoo JangAbstract:Average load forecasting errors for the holidays are much higher than those for weekdays. So far, many studies on the short-term load forecasting have been made to improve the prediction accuracy using various Methods such as deterministic, stochastic, artificial neural net (ANN) and neural network-fuzzy Methods. In order to reduce the load forecasting error of the 24 hourly loads for the holidays, the concept of fuzzy Regression analysis is employed in the short-term load forecasting problem. According to the historical load data, the same type of holiday showed a similar trend of load profile as in previous years. The fuzzy linear Regression model is made from the load data of the previous three years and the coefficients of the model are found by solving the mixed linear programming problem. The proposed algorithm shows good accuracy, and the average maximum percentage error is 3.57% in the load forecasting of the holidays for the years of 1996-1997.
Dug Hun Hong - One of the best experts on this subject based on the ideXlab platform.
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short term load forecasting for the holidays using fuzzy linear Regression Method
IEEE Power Engineering Society General Meeting 2005, 2005Co-Authors: Kyungbin Song, Youngsik Baek, Dug Hun Hong, Gilsoo JangAbstract:Summary form only given. Average load forecasting errors for the holidays are much higher than those for weekdays. So far, many studies on the short-term load forecasting have been made to improve the prediction accuracy using various Methods such as deterministic, stochastic, artificial neural net (ANN) and neural network-fuzzy Methods. In order to reduce the load forecasting error of the 24 hourly loads for the holidays, the concept of fuzzy Regression analysis is employed in the short-term load forecasting problem. According to the historical load data, the same type of holiday showed a similar trend of load profile as in previous years. The fuzzy linear Regression model is made from the load data of the previous three years and the coefficients of the model are found by solving the mixed linear programming problem. The proposed algorithm shows good accuracy, and the average maximum percentage error is 3.57% in the load forecasting of the holidays for the years of 1996-1997.
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short term load forecasting for the holidays using fuzzy linear Regression Method
IEEE Transactions on Power Systems, 2005Co-Authors: Kyungbin Song, Youngsik Baek, Dug Hun Hong, Gilsoo JangAbstract:Average load forecasting errors for the holidays are much higher than those for weekdays. So far, many studies on the short-term load forecasting have been made to improve the prediction accuracy using various Methods such as deterministic, stochastic, artificial neural net (ANN) and neural network-fuzzy Methods. In order to reduce the load forecasting error of the 24 hourly loads for the holidays, the concept of fuzzy Regression analysis is employed in the short-term load forecasting problem. According to the historical load data, the same type of holiday showed a similar trend of load profile as in previous years. The fuzzy linear Regression model is made from the load data of the previous three years and the coefficients of the model are found by solving the mixed linear programming problem. The proposed algorithm shows good accuracy, and the average maximum percentage error is 3.57% in the load forecasting of the holidays for the years of 1996-1997.
Youngsik Baek - One of the best experts on this subject based on the ideXlab platform.
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short term load forecasting for the holidays using fuzzy linear Regression Method
IEEE Power Engineering Society General Meeting 2005, 2005Co-Authors: Kyungbin Song, Youngsik Baek, Dug Hun Hong, Gilsoo JangAbstract:Summary form only given. Average load forecasting errors for the holidays are much higher than those for weekdays. So far, many studies on the short-term load forecasting have been made to improve the prediction accuracy using various Methods such as deterministic, stochastic, artificial neural net (ANN) and neural network-fuzzy Methods. In order to reduce the load forecasting error of the 24 hourly loads for the holidays, the concept of fuzzy Regression analysis is employed in the short-term load forecasting problem. According to the historical load data, the same type of holiday showed a similar trend of load profile as in previous years. The fuzzy linear Regression model is made from the load data of the previous three years and the coefficients of the model are found by solving the mixed linear programming problem. The proposed algorithm shows good accuracy, and the average maximum percentage error is 3.57% in the load forecasting of the holidays for the years of 1996-1997.
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short term load forecasting for the holidays using fuzzy linear Regression Method
IEEE Transactions on Power Systems, 2005Co-Authors: Kyungbin Song, Youngsik Baek, Dug Hun Hong, Gilsoo JangAbstract:Average load forecasting errors for the holidays are much higher than those for weekdays. So far, many studies on the short-term load forecasting have been made to improve the prediction accuracy using various Methods such as deterministic, stochastic, artificial neural net (ANN) and neural network-fuzzy Methods. In order to reduce the load forecasting error of the 24 hourly loads for the holidays, the concept of fuzzy Regression analysis is employed in the short-term load forecasting problem. According to the historical load data, the same type of holiday showed a similar trend of load profile as in previous years. The fuzzy linear Regression model is made from the load data of the previous three years and the coefficients of the model are found by solving the mixed linear programming problem. The proposed algorithm shows good accuracy, and the average maximum percentage error is 3.57% in the load forecasting of the holidays for the years of 1996-1997.
Ferra Yanuar - One of the best experts on this subject based on the ideXlab platform.
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Quantile Regression Approach to Model Censored Data
Science and Technology Indonesia, 2020Co-Authors: Sarmada Sarmada, Ferra YanuarAbstract:Abstract The censored quantile Regression model is derived from the censored model. This Method is used to overcome problems in modeling censored data as well as to overcome the assumptions of linear models that are not met. The purpose of this study is to compare the results of the analysis of the quantile Regression Method with the censored quantile Regression Method for censored data. Both Methods were applied to generated data of 150, 500, and 3000 sample size. The best model is then chosen based on the smallest absolute bias and the smallest standard error as an indicator of the goodness of the model. This study proves that the censored quantile Regression Method tends to produce smaller absolute bias and a smaller standard error than the quantile Regression Method for all three group data specified. Thus it can be concluded that the censored quantile Regression Method could result in acceptable model for censored data. Keywords: Censored data; quantile Regression; quantile Regression censored; standard error; absolute bias.
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Simulation Study of Autocorrelated Error Using Bayesian Quantile Regression
Science and Technology Indonesia, 2020Co-Authors: Nayla Desviona, Ferra YanuarAbstract:The purpose of this study is to compare the ability of the Classical Quantile Regression Method and the Bayesian Quantile Regression Method in estimating models that contain autocorrelated error problems using simulation studies. In the quantile Regression approach, the data response is divided into several pieces or quantiles conditions on indicator variables. Then, The parameter model is estimated for each selected quantiles. The parameters are estimated using conditional quantile functions obtained by minimizing absolute asymmetric errors. In the Bayesian quantile Regression Method, the data error is assumed to be asymmetric Laplace distribution. The Bayesian approach for quantile Regression uses the Markov Chain Monte Carlo Method with the Gibbs sample algorithm to produce a converging posterior mean. The best Method for estimating parameter is the Method that produces the smallest absolute value of bias and the smallest confidence interval. This study resulted that the Bayesian Quantile Method produces smaller absolute bias values and confidence intervals than the quantile Regression Method. These results proved that the Bayesian Quantile Regression Method tends to produce better estimate values than the Quantile Regression Method in the case of autocorrelation errors. Keywords: Quantile Regression Method, Bayesian Quantile Regression Method, Confidence Interval, Autocorrelation.
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Simulation Study The Using of Bayesian Quantile Regression in Nonnormal Error
CAUCHY, 2018Co-Authors: Catrin Muharisa, Ferra Yanuar, Dodi DeviantoAbstract:The purposes of this paper is to introduce the ability of the Bayesian quantile Regression Method in overcoming the problem of the nonnormal errors using asymmetric laplace distribution on simulation study. <strong>Method: </strong>We generate data and set distribution of error is asymmetric laplace distribution error, which is non normal data. In this research, we solve the nonnormal problem using quantile Regression Method and Bayesian quantile Regression Method and then we compare. The approach of the quantile Regression is to separate or divide the data into any quantiles, estimate the conditional quantile function and minimize absolute error that is asymmetrical. Bayesian Regression Method used the asymmetric laplace distribution in likelihood function. Markov Chain Monte Carlo Method using Gibbs sampling algorithm is applied then to estimate the parameter in Bayesian Regression Method. Convergency and confidence interval of parameter estimated are also checked. <strong>Result: </strong>Bayesian quantile Regression Method results has more significance parameter and smaller confidence interval than quantile Regression Method. <strong>Conclusion: </strong>This study proves that Bayesian quantile Regression Method can produce acceptable parameter estimate for nonnormal error.
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Modeling of Human Development Index Using Ridge Regression Method
EKSAKTA: Berkala Ilmiah Bidang MIPA, 2018Co-Authors: Ferra Yanuar, Mardha Tillah, Dodi DeviantoAbstract:This article aims to model factors affecting HDI (Human Development Index) in North Sumatera by 2015 using ridge Regression Method. This ridge Regression Method is used because in the IPM data there is a multicolinearity problem so that the least squares Regression Method, as Regression Method commonly used in statistical modeling, is not suitable for use any more. This study compares the models resulting from the use of the least squares Method and the ridge Regression Method to the HDI data. This study proves that the ridge Regression Method produces a better model and can eliminate the multicolinearity effect, while the least squares Method can not. The significant factors in affecting HDI on North Sumtera data in 2015 are Average School length and Total expenditure / capita / month. The indicator of the goodness of this ridge Regression model is 81.81% which means that the model is good and could be accepted.
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The Simulation Study to Test the Performance of Quantile Regression Method With Heteroscedastic Error Variance
CAUCHY, 2017Co-Authors: Ferra YanuarAbstract:The purpose of this article was to describe the ability of the quantile Regression Method in overcoming the violation of classical assumptions. The classical assumptions that are violated in this study are variations of non-homogeneous error or heteroscedasticity. To achieve this goal, the simulated data generated with the design of certain data distribution. This study did a comparison between the models resulting from the use of the ordinary least squares and the quantile Regression Method to the same simulated data. Consistency of both Methods was compared with conducting simulation studies as well. This study proved that the quantile Regression Method had standard error, confidence interval width and mean square error (MSE) value smaller than the ordinary least squares Method. Thus it can be concluded that the quantile Regression Method is able to solve the problem of heteroscedasticity and produce better model than the ordinary least squares. In addition the ordinary least squares is not able to solve the problem of heteroscedasticity.
