scholarly journals Parameter Estimation of Photovoltaic Models via Cuckoo Search

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Jieming Ma ◽  
T. O. Ting ◽  
Ka Lok Man ◽  
Nan Zhang ◽  
Sheng-Uei Guan ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Mean Squared Error ◽  
Estimation Method ◽  
Cuckoo Search ◽  
High Accuracy ◽  
Flight Behavior ◽  
Squared Error ◽  
Simulation Results ◽  
Bioinspired Algorithms ◽  
Significant Attention

Since conventional methods are incapable of estimating the parameters of Photovoltaic (PV) models with high accuracy, bioinspired algorithms have attracted significant attention in the last decade. Cuckoo Search (CS) is invented based on the inspiration of brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior. In this paper, a CS-based parameter estimation method is proposed to extract the parameters of single-diode models for commercial PV generators. Simulation results and experimental data show that the CS algorithm is capable of obtaining all the parameters with extremely high accuracy, depicted by a low Root-Mean-Squared-Error (RMSE) value. The proposed method outperforms other algorithms applied in this study.

scholarly journals Marginal quantile regression for longitudinal data analysis in the presence of time-dependent covariates

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
I-Chen Chen ◽  
Philip M. Westgate
Keyword(s):  
Parameter Estimation ◽  
Longitudinal Data ◽  
Quantile Regression ◽  
Mean Squared Error ◽  
Time Dependent ◽  
Regression Methods ◽  
Squared Error ◽  
Highly Correlated ◽  
Time Dependent Covariates ◽  
Independence Structure

AbstractWhen observations are correlated, modeling the within-subject correlation structure using quantile regression for longitudinal data can be difficult unless a working independence structure is utilized. Although this approach ensures consistent estimators of the regression coefficients, it may result in less efficient regression parameter estimation when data are highly correlated. Therefore, several marginal quantile regression methods have been proposed to improve parameter estimation. In a longitudinal study some of the covariates may change their values over time, and the topic of time-dependent covariate has not been explored in the marginal quantile literature. As a result, we propose an approach for marginal quantile regression in the presence of time-dependent covariates, which includes a strategy to select a working type of time-dependency. In this manuscript, we demonstrate that our proposed method has the potential to improve power relative to the independence estimating equations approach due to the reduction of mean squared error.

scholarly journals A mean-squared-error condition for weighting ionospheric delays in GNSS baselines

2021 ◽  
Vol 95 (11) ◽  
Author(s):  
P. J. G. Teunissen ◽  
A. Khodabandeh
Keyword(s):  
Parameter Estimation ◽  
Mean Squared Error ◽  
Present Contribution ◽  
Squared Error ◽  
Fixed Solution ◽  
Error Condition

AbstractAlthough ionosphere-weighted GNSS parameter estimation is a popular technique for strengthening estimator performance in the presence of ionospheric delays, no provable rules yet exist that specify the needed weighting in dependence on ionospheric circumstances. The goal of the present contribution is therefore to develop and present the ionospheric conditions that need to be satisfied in order for the ionosphere-weighted solution to be mean squared error (MSE) superior to the ionosphere-float solution. When satisfied, the presented conditions guarantee from an MSE performance view, when (a) the ionosphere-fixed solution can be used, (b) the ionosphere-float solution must be used, or (c) an ionosphere-weighted solution can be used.

scholarly journals Estimating Systematic Risk under Extremely Adverse Market Conditions*

2017 ◽  
Vol 17 (3) ◽  
pp. 432-461 ◽  
Author(s):  
Maarten R C van Oordt ◽  
Chen Zhou
Keyword(s):  
Mean Squared Error ◽  
Explanatory Variable ◽  
Asymptotic Properties ◽  
Estimation Method ◽  
Tail Dependence ◽  
Stock Portfolios ◽  
Squared Error ◽  
Regression Approach ◽  
Model Coefficient ◽  
Heavy Tailed

AbstractThis paper considers the problem of estimating a linear model between two heavy-tailed variables if the explanatory variable has an extremely low (or high) value. We propose an estimator for the model coefficient by exploiting the tail dependence between the two variables and prove its asymptotic properties. Simulations show that our estimation method yields a lower mean-squared error than regressions conditional on tail observations. In an empirical application, we illustrate the better performance of our approach relative to the conditional regression approach in projecting the losses of industry-specific stock portfolios in the event of a market crash.

Parameter Estimation of Low-Frequency Signal with Negative Frequency Contribution

2013 ◽  
Vol 380-384 ◽  
pp. 3457-3460 ◽  
Author(s):  
Si Wei Tan ◽  
Zhi Liang Ren ◽  
Jiong Sun
Keyword(s):  
Parameter Estimation ◽  
Phase Difference ◽  
Spectrum Analysis ◽  
Low Frequency ◽  
High Accuracy ◽  
Signal Parameter ◽  
Frequency Signal ◽  
Spectrum Method ◽  
Analysis Theory ◽  
Simulation Results

According to the problem that there is a decline in accuracy of low-frequency signal parameter estimation by using the algorithm of all-phase FFT, an improved phase difference correcting spectrum method based on all-phase FFT is proposed. The contribution of negative frequency to FFT calculation was considered while using phase difference correcting spectrum method. The all-phase FFT spectrum analysis theory was presented as well as a traditional phase difference correcting method based on it. The equations of parameter estimation such as frequency, amplitude and phase for low-frequency signals were derived with the negative frequency contribution to spectrum analysis. The simulation results show that the method proposed in this paper can be used to estimate the parameters of low-frequency signals in a high accuracy, and also achieves an improvement in anti-noise ability.

scholarly journals THE EFFECT OF OPENING TYPE ON THERMAL CONVENIENCE OF THE OLD AND NEW BUILDING CLASSROOM

2019 ◽  
Vol 24 (2) ◽  
pp. 75-87
Author(s):  
Ali Anton Senoaji ◽  
Arif Kusumawanto ◽  
Sentagi Sesotya Utami
Keyword(s):  
Wind Speed ◽  
Air Temperature ◽  
Mean Squared Error ◽  
Comparative Method ◽  
Field Measurements ◽  
Root Mean Squared Error ◽  
Wind Speeds ◽  
Squared Error ◽  
Simulation Results ◽  
New Buildings

This study was aimed at analyzing the effect of opening type on the thermal convenience of classrooms in old and new buildings at SMK Negeri 3 Yogyakarta. This study used a qualitative comparative method and the simulation of IES VE 2018. The field air measurement is carried out at 10 measurement points and 5 measurement points in each class, with a height of 1.5 m. Field measurements were carried out in March 2019, at 06.30-16.30 WIB. The parameters used in the study were air temperature, humidity and wind speed. Air temperature and humidity were measured using a Thermo hygrometer. Wind speed was measured using an anemometer. The data collection method is carried out by observation and measurement. Root Mean Squared Error (RMSE) was used to validate the data. The results show the best thermal convenience of the classroom was obtained during the simulation using the type of Windows Awning, with a full aperture area. Simulation results show a comfortable distribution of airflow in the classroom at wind speeds above 0.15-0.28 m/sec, Temperature 25.07-27.10oC.PENGARUH TIPE BUKAAN TERHADAP KENYAMANAN TERMAL RUANG KELAS BANGUNAN LAMA DAN BARU Tujuan dari penelitian yaitu menganalisis pengaruh bukaan terhadap kenyamanan termal ruang kelas pada bangunan lama dan baru, di SMK Negeri 3 Yogyakarta. Penelitian ini menggunakan metode komparatif kualitatif yaitu dan hasil simulasi IES VE 2018. Pengukuran udara luar dilakukan pada 10 titik pengukuran dan sebanyak 5 titik pengukuran disetiap kelasnya, dengan ketinggian 1,5 m. Pengukuran lapangan dilakukan pada bulan Maret tahun 2019, waktu 06.30-16.30 WIB. Parameter yang digunakan dalam penelitian yaitu temperatur udara, kelembaban dan kecepatan angin. Temperatur udara dan kelembaban diukur dengan menggunakan alat thermo hygrometer. Kecepatan angin diukur dengan menggunakan alat anemometer. Metode pengumpulan data dilakukan dengan metode pengamatan dan pengukuran. Validasi data menggunakan Root Mean Squared Error (RMSE). Hasil penelitian menunjukkan kenyamanan termal ruang kelas terbaik diperoleh pada saat simulasi menggunakan tipe bukaan ke atas atau Awning Windows, dengan area bukaan penuh. Hasil simulasi menunjukkan distribusi aliran udara yang nyaman di dalam ruang kelas pada kecepatan angin di atas 0,15-0,28 m/det, Temperatur 25,07 -27,10o C. 

scholarly journals SPECTRAL AGROMETEOROLOGICAL MODELING ADAPTED BY MEANS OF SIMPLIFIED TRIANGLE METHOD FOR SOYBEAN IN PARANÁ STATE – BRAZIL

2018 ◽  
Vol 44 ◽  
pp. 154
Author(s):  
Daniela Fernanda da Silva Fuzzo
Keyword(s):  
Mean Squared Error ◽  
High Accuracy ◽  
P Value ◽  
Soybean Yield ◽  
Surface Information ◽  
Squared Error ◽  
Significant Difference ◽  
Weather And Climate ◽  
Triangle Method ◽  
National Systems

Agriculture is an economic activity with high dependence on weather and climate. Special geotechnology and agrometeorological modeling can be used to optimize productivity in regional and national systems, while minimizing costs. The aim was to test the agrometeorological model for estimating crop soybean yield proposed by Doorenbos and Kassam (1979), using only spectral data as input variable in the model obtained by a simplified triangle method applied in Paraná state, for crop years 2002/03 to 2011/12. A high accuracy of the data was found, the model values for the parameter d1 ("d1" modified Willmott) were between 0.8 and 0.95, whereas the root mean squared error showed that there was low variation between 30.81 to 116.88 (kg ha-1) and the p-value was used as the indicator significance of the model at the level of 5%, indicating that there was no statistically significant difference between the estimated and observed data, this means that the average of the data estimated by the model were statistically equal the average of the observed data. Thus, we can say that images of remote sensing can be used as tools in the absence of surface information, in agrometeorological modeling to estimate crop soybean yield.

scholarly journals Modified Robust Ridge M-Estimators in Two-Parameter Ridge Regression Model

2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Seyab Yasin ◽  
Sultan Salem ◽  
Hamdi Ayed ◽  
Shahid Kamal ◽  
Muhammad Suhail ◽  
...  
Keyword(s):  
Ridge Regression ◽  
Mean Squared Error ◽  
Least Square ◽  
Ridge Estimator ◽  
Least Square Estimator ◽  
Squared Error ◽  
Joint Problem ◽  
Ridge Regression Estimator ◽  
Simulation Results ◽  
Two Parameter

The methods of two-parameter ridge and ordinary ridge regression are very sensitive to the presence of the joint problem of multicollinearity and outliers in the y-direction. To overcome this problem, modified robust ridge M-estimators are proposed. The new estimators are then compared with the existing ones by means of extensive Monte Carlo simulations. According to mean squared error (MSE) criterion, the new estimators outperform the least square estimator, ridge regression estimator, and two-parameter ridge estimator in many considered scenarios. Two numerical examples are also presented to illustrate the simulation results.

scholarly journals Two Different Classes of Shrinkage Estimators for the Scale Parameter of the Rayleigh Distribution

2021 ◽  
Vol 19 (1) ◽  
pp. 2-21
Author(s):  
Talha Omer ◽  
Zawar Hussain ◽  
Muhammad Qasim ◽  
Said Farooq Shah ◽  
Akbar Ali Khan
Keyword(s):  
Simulation Study ◽  
Mean Squared Error ◽  
Scale Parameter ◽  
Rayleigh Distribution ◽  
Shrinkage Estimators ◽  
Unbiased Estimators ◽  
Squared Error ◽  
The Mean ◽  
Simulation Results

Shrinkage estimators are introduced for the scale parameter of the Rayleigh distribution by using two different shrinkage techniques. The mean squared error properties of the proposed estimator have been derived. The comparison of proposed classes of the estimators is made with the respective conventional unbiased estimators by means of mean squared error in the simulation study. Simulation results show that the proposed shrinkage estimators yield smaller mean squared error than the existence of unbiased estimators.

scholarly journals PENERAPAN METODE PENDUGAAN AREA KECIL (SMALL AREA ESTIMATION) PADA PENENTUAN PROPORSI RUMAH TANGGA MISKIN DI KABUPATEN KLUNGKUNG

2013 ◽  
Vol 2 (3) ◽  
pp. 35 ◽  
Author(s):  
PUTU EKA ARIWIJAYANTHI ◽  
I WAYAN SUMARJAYA ◽  
TJOKORDA BAGUS OKA
Keyword(s):  
Small Area ◽  
Empirical Bayes ◽  
Mean Squared Error ◽  
Estimation Method ◽  
Parameter Estimates ◽  
Bayes Methods ◽  
Mean Squared Errors ◽  
Direct Estimation ◽  
Squared Error ◽  
Empirical Bayes Methods

Small area is an area with insufficient sample for direct estimation. Limited survey objects, cause direct estimation can not produce better parameter estimates. Based on this, an indirect estimation method called empirical Bayes is used to obtain a better estimate. This study will compare means squared error by  direct estimation method and empirical Bayes method to find a better method on a small area. Jackknife is used to get the means squared error in the empirical Bayes. The results is, empirical Bayes methods give a better parameters based on mean squared errors. Empirical Bayes can produce a smaller mean squared error more than direct estimation in small area.

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