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Wind turbine power curve modeling for reliable power prediction using monotonic regression
Renewable Energy, 2020Abstract Wind turbine power curve modeling plays an important role in wind energy management and power forecasting and it is often done based on parametric or non-parametric methods. As wind-power data are often noisy, even after polishing data using proper methods, fitted wind turbine power curves could be very different from the theoretical ones ...
Mehrdad Mehrjoo +2 more
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Modeling tropical tuna shifts: An inflated power logit regression approach
Biometrical JournalAbstractWe introduce a new class of zero‐or‐one inflated power logit (IPL) regression models, which serve as a versatile tool for analyzing bounded continuous data with observations at a boundary. These models are applied to explore the effects of climate changes on the distribution of tropical tuna within the North Atlantic Ocean. Our findings suggest
Francisco F. Queiroz +1 more
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The Power of Approximate Tests for the Regression Coefficients in a Gamma Regression Model
IEEE Transactions on Reliability, 1986Summary: A gamma regression model which has wide application in life-testing and analysis of point processes is considered. Approximate statistical tests for the regression coefficients based on maximum likelihood and weighted least squares fits of the model are considered.
Al-Abood, Akram M., Young, David H.
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Photovoltaic Power Regression Model Based on Gauss Boltzmann Machine
2020 39th Chinese Control Conference (CCC), 2020Improving the short-term power forecast of photovoltaic panels is a key issue for solar photovoltaic power generation to effectively merge from distributed power sources into the current large-scale power grid, and has great significance for improving the utilization of solar photovoltaic power generation.
Zhiying Lu, Zehan Wang, Yimo Ren
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A regression model to determine load for maximum power output
Sports Biomechanics, 2008The aims of this study were to create a regression model of the relationship between load and muscle power output and to determine an optimal load for maximum power output during a countermovement squat and a bench press. 55 males and 48 females performed power testing at 0, 10, 30, 50, 70, 90, and 100% of their individual one-repetition maximum (1-RM)
Daniel, Jandacka, Frantisek, Vaverka
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Power Transformations and Reparameterizations in Nonlinear Regression Models
Technometrics, 1988A two-stage procedure is proposed to achieve normality and homoscedasticity of the classical error assumptions and to remove nonlinearity of the regression function. This systematic approach involves power transformations and reparameterization.
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Evaluating the discriminatory power of a multiple logistic regression model
Statistics in Medicine, 1988AbstractVarious measures for estimating the goodness‐of‐fit of the multiple logistic regression (MLR) model have been suggested, although there is no clear consensus as to which measure is most suitable. In this paper, a simple measure of the discriminatory power of the fitted MLR model, based on maximization of Youden's J index (J*), is proposed and ...
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Tests for Regression Parameters in Power Transformation Models.
1980Abstract : This report discusses tests of hypotheses for regression parameters in the power transformation model. In this model, a simple test consists of estimating the correct scale and then performing the usual linear model F-test in this estimated scale. We explore situations in which this test has the correct level asymptotically as well as better
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Chaos Powered Symbolic Regression in Be Stars Spectra Modeling
2014Be stars are characterized by prominent emission lines in their spectrum. In the past research has attention been given to creation a feature extraction method for classification of Be stars with focusing on the automated classification of Be stars based on typical shapes of their emission lines.
Ivan Zelinka +4 more
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Power Consumption Forecast Based on Ridge Regression Model
Proceedings of the 5th International Conference on Information Technologies and Electrical Engineering, 2022Jie Li +5 more
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