4.7 Article

Optimal Bandwidth Selection Methods with Application to Wind Speed Distribution

Related references

Note: Only part of the references are listed.
Article Mathematics

Modelling wind speed with a univariate probability distribution depending on two baseline functions

Fabio Verissimo Jaques Silveira et al.

Summary: Characterizing the wind speed distribution is crucial for wind farms' energy production, but existing mixture models often suffer from the undesirable property of non-identifiability. In this study, we propose a new identifiable distribution model, the Normal-Weibull-Weibull, which can fit wind speed data effectively. We discuss the structural properties of the model class and perform a Monte Carlo simulation study to analyze the behavior of the parameter estimates. Finally, we apply the new distribution model to wind speed data from five cities in the Northeastern Region of Brazil.

HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS (2023)

Article Computer Science, Interdisciplinary Applications

Bandwidth selection for kernel density estimation of fat-tailed and skewed distributions

Daniel J. Henderson et al.

Summary: Applied researchers often assume that the reference density is Normal and propose optimal bandwidth rules based on this assumption. However, we introduce four new optimal bandwidth rules-of-thumb based on other infinitely supported distributions. We also propose a psuedo rule-of-thumb bandwidth that is linked to the empirical skewness and kurtosis of the data. These new bandwidths require minimal intellectual investment and their behavior is compared to the Normal reference ROT. We also propose model selection criteria for bandwidth choice when the true underlying density is unknown, and evaluate the performance of these new ROT bandwidths in simulations and empirical illustrations.

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION (2023)

Article Thermodynamics

Unbiased cross-validation kernel density estimation for wind and PV probabilistic modelling

Maisam Wahbah et al.

Summary: Uncertainties in wind energy and photovoltaic power systems pose challenges for power system planners and operators. This article proposes a novel probability density estimation method for wind speed and solar irradiance, outperforming traditional parametric and nonparametric approaches. The results demonstrate the accuracy and robustness of the proposed model's probability density estimates.

ENERGY CONVERSION AND MANAGEMENT (2022)

Article Green & Sustainable Science & Technology

Estimation of wind speed distribution with time window and new kernel function

Ling Liu et al.

Summary: This study proposes three new kernel functions and applies a new time window analysis method to improve the accuracy of wind speed distribution estimation. The research also discovers significant differences in wind speed distribution under different time windows.

JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY (2022)

Article Meteorology & Atmospheric Sciences

Estimating vertical wind power density using tower observation and empirical models over varied desert steppe terrain in northern China

Shaohui Zhou et al.

Summary: A complex and varied terrain has a significant impact on the distribution of wind energy resources, leading to uncertainty in accurately assessing wind energy resources. This study compares three wind speed distribution models and investigates the influence of key parameters on wind energy resources. The results show that the Weibull distribution is the most suitable model for the terrain.

ATMOSPHERIC MEASUREMENT TECHNIQUES (2022)

Review Energy & Fuels

Wind Speed Distributions Used in Wind Energy Assessment: A Review

Huanyu Shi et al.

Summary: With economic development and population growth, the demand for energy is increasing, making the development of renewable energy sources crucial. Wind energy, as a significant renewable energy source, plays a key role in both environmental protection and economic growth. Assessing the characteristics and potential of wind energy is essential for its effective development.

FRONTIERS IN ENERGY RESEARCH (2021)

Article Social Sciences, Mathematical Methods

How Important is the Choice of Bandwidth in Kernel Equating?

Gabriel Wallin et al.

Summary: Kernel equating is a method that uses kernel smoothing techniques to continuousize discrete score distributions, with the smoothness of the continuous approximations determined by bandwidth selection. The study found that sample size and test length are important factors for equating accuracy and precision, and that the four bandwidth selection methods perform similarly in terms of mean squared error and differences in equated scores.

APPLIED PSYCHOLOGICAL MEASUREMENT (2021)

Article Computer Science, Information Systems

Novel kernel density estimator based on ensemble unbiased cross-validation

Yu-Lin He et al.

Summary: This paper proposes a novel ensemble UCV based KDE (EUCV-KDE), which determines the expectation of an estimated PDF using an ensemble of data-block based UCVs. A novel objective function is designed for EUCV-KDE by considering the empirical and structural risk of KDE together. The experimental results show that EUCV-KDE is more stable and performs better than classical UCV-KDE and RCV-KDE.

INFORMATION SCIENCES (2021)

Article Green & Sustainable Science & Technology

Kernel density estimation model for wind speed probability distribution with applicability to wind energy assessment in China

Qinkai Han et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2019)

Review Engineering, Electrical & Electronic

Wind speed model based on kernel density estimation and its application in reliability assessment of generating systems

Bo Hu et al.

JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY (2017)

Article Thermodynamics

A mixture kernel density model for wind speed probability distribution estimation

Shuwei Miao et al.

ENERGY CONVERSION AND MANAGEMENT (2016)

Article Psychology, Multidisciplinary

How Bandwidth Selection Algorithms Impact Exploratory Data Analysis Using Kernel Density Estimation

Jared K. Harpole et al.

PSYCHOLOGICAL METHODS (2014)

Article Statistics & Probability

Density estimation

SJ Sheather

STATISTICAL SCIENCE (2004)