Probabilistic forecasting for the ampacity of overhead transmission lines using quantile regression method

2016 ◽  
Author(s):  
Zhiqing Wei ◽  
Mengxia Wang ◽  
Xueshan Han ◽  
Haicheng Zhang ◽  
Qiang Zhang
Keyword(s):  
Quantile Regression ◽  
Transmission Lines ◽  
Regression Method ◽  
Probabilistic Forecasting ◽  
Overhead Transmission Lines ◽  
Quantile Regression Method

scholarly journals Friction Characteristics of Post-Tensioned Tendons of Full-Scale Structures Based on Site Tests

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Haoyun Yuan ◽  
Yuan Li ◽  
Bin Zhou ◽  
Shuanhai He ◽  
Peizhi Wang
Keyword(s):  
Friction Coefficient ◽  
Quantile Regression ◽  
Least Square Method ◽  
Regression Method ◽  
Full Scale ◽  
Least Square ◽  
Friction Characteristics ◽  
Bayesian Quantile Regression ◽  
Prestressing Force ◽  
Quantile Regression Method

In the design of prestressing concrete structures, the friction characteristics between strands and channels have an important influence on the distribution of prestressing force, which can be considered comprehensively by curvature and swing friction coefficients. However, the proposed friction coefficient varies widely and may lead to an inaccurate prestress estimation. In this study, four full-scale field specimens were established to measure the friction loss of prestressing tendons with electromagnetic sensors and anchor cable dynamometers to evaluate the friction coefficient. The least square method and Bayesian quantile regression method were adopted to calculate the friction coefficient, and the results were compared with that in the specifications. Field test results showed that Bayesian quantile regression method was more effective and significant in the estimation of the friction coefficient.

scholarly journals Statistically Efficient Construction of α-Risk-Minimizing Portfolio

2012 ◽  
Vol 2012 ◽  
pp. 1-17 ◽  
Author(s):  
Hiroyuki Taniai ◽  
Takayuki Shiohama
Keyword(s):  
Monte Carlo ◽  
Quantile Regression ◽  
Asymptotic Normality ◽  
Asymptotic Properties ◽  
Regression Method ◽  
General Reference ◽  
Regression Problem ◽  
Efficient Construction ◽  
Consistency And Asymptotic Normality ◽  
Quantile Regression Method

We propose a semiparametrically efficient estimator for α-risk-minimizing portfolio weights. Based on the work of Bassett et al. (2004), an α-risk-minimizing portfolio optimization is formulated as a linear quantile regression problem. The quantile regression method uses a pseudolikelihood based on an asymmetric Laplace reference density, and asymptotic properties such as consistency and asymptotic normality are obtained. We apply the results of Hallin et al. (2008) to the problem of constructing α-risk-minimizing portfolios using residual signs and ranks and a general reference density. Monte Carlo simulations assess the performance of the proposed method. Empirical applications are also investigated.

scholarly journals Probabilistic prediction for the ampacity of overhead lines using Quantile Regression Neural Network

2020 ◽  
Vol 185 ◽  
pp. 02022
Author(s):  
Xu Jin ◽  
Fudong Cai ◽  
Mengxia Wang ◽  
Yang Sun ◽  
Shengyuan Zhou
Keyword(s):  
Neural Network ◽  
Quantile Regression ◽  
Transmission Lines ◽  
Meteorological Data ◽  
Overhead Line ◽  
Probabilistic Prediction ◽  
Overhead Lines ◽  
Overhead Transmission Lines ◽  
Planning And Control ◽  
And Control

The ampacity of overhead transmission lines play a key role in power system planning and control. Due to the volatility of the meteorological elements, the ampacity of an overhead line is timevarying. In order to fully utilize the transfer capability of overhead transmission lines, it is necessary to provide system operators with accurate probabilistic prediction results of the ampacity. In this paper, a method based on the Quantile Regression Neural Network (QRNN) is proposed to improve the performance of the probabilistic prediction of the ampacity. The QRNN-based method uses a nonlinear model to comprehensively model the impacts of historical meteorological data and historical ampacity data on the ampacity at predictive time period. Numerical simulations based on the actual meteorological data around an overhead line verify the effectiveness of the proposed method.

scholarly journals ANALISIS PERANAN EMAS DAN OBLIGASI PEMERINTAH SEBAGAI SAFE HAVEN PERIODE 2014—2018

2020 ◽  
Vol 16 (2) ◽  
pp. 212-236
Author(s):  
Evamelia Evamelia ◽  
Yunia Panjaitan
Keyword(s):  
Quantile Regression ◽  
Capital Market ◽  
Regression Method ◽  
Government Policies ◽  
Safe Haven ◽  
Government Bonds ◽  
Bear Market ◽  
Political Conditions ◽  
Quantile Regression Method

The purpose of this research to identify the role of gold and government bonds role as safe haven in Indonesian capital market during 2014-2018. In this study we analyze the influence of stock on gold and government return on bear market conditions, using quantile regression. The quantile regression method was used to analyze the data.  The result if this study indicated that gold and government bonds cannot play a safe haven consistently throughout the study period due to political conditions, government policies and psychological factors (doubt) from investors. For the following research, researchers should examine more deeply about the factors that influence the loss of the role of safe haven in both investment instruments.

scholarly journals EFEKTIVITAS REGRESI KUANTIL DALAM MENGATASI PENCILAN PONTENSIAL

2020 ◽  
Vol 14 (2) ◽  
pp. 305-312
Author(s):  
Netti Herawati
Keyword(s):  
Quantile Regression ◽  
Mean Square Error ◽  
Akaike Information Criterion ◽  
Robust Regression ◽  
Information Criterion ◽  
Regression Method ◽  
Least Square ◽  
Mean Square ◽  
The Impact ◽  
Quantile Regression Method

Abstrak Regresi kuantil sebagai metode regresi yang robust dapat digunakan untuk mengatasi dampak kasus yang tidak biasa pada estimasi regresi. Tujuan dari penelitian ini adalah untuk mengevaluasi efektivitas regresi kuantil untuk menangani pencilan potensial dalam regresi linear berganda dibandingkan dengan metode kuadrat terkecil (MKT). Penelitian ini menggunakan data simulasi dengan p=3; n = 20, 40, 60, 100, 200 and   and  diulang 1000 kali. Efektivitas metode regresi kuantil dan MKT dalam pendugaan parameter β diukur dengan Mean square error (MSE) dan Akaike Information Criterion (AIC). Hasil penelitian menunjukkan bahwa regresi kuantil mampu menangani pencilan potensial dan memberikan penaksir yang lebih baik dibandingkan dengan MKT berdasarkan nilai MSE dan AIC. Kata kunci: AIC, MSE, pencilan, regresi kuantil Abstract Quantitative regression as a robust regression method can be used to overcome the impact of unusual cases on regression estimation. The purpose of this study is to evaluate the effectiveness of quantile regression to deal with potential outliers in multiple linear regression compared to the least squares methodordinary least square (OLS).   This study uses simulation data with p=3; n = 20, 40, 60, 100, 200 and   and  repeated 1000 times. The effectiveness of the quantile regression method and OLS in estimating β   parameters was measured by Mean square error (MSE) and Akaike Information Criterion (AIC). The results showed that quantile regression was able to handle potential outliers and provide better predictors compared to MKT based on MSE and AIC values. Keywords: AIC, MSE, outliers, quantile regression

scholarly journals Bayesian Quantile Regression Method to Construct the Low Birth Weight Model

2019 ◽  
Vol 1245 ◽  
pp. 012044
Author(s):  
Ferra Yanuar ◽  
Aidinil Zetra ◽  
Catrin Muharisa ◽  
Dodi Devianto ◽  
Arrival Rince Putri ◽  
...  
Keyword(s):  
Birth Weight ◽  
Low Birth Weight ◽  
Quantile Regression ◽  
Regression Method ◽  
Bayesian Quantile Regression ◽  
Quantile Regression Method ◽  
Weight Model
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