Journal ArticleDOI
Estimating wind speed probability distribution by diffusion-based kernel density method
Xiaoyuan Xu,Zheng Yan,Shaolun Xu +2 more
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TLDR
An improved non-parametric method to estimate wind speed probability distributions based on the diffusion partial differential equation in finite domain, which accounts for both bandwidth selection and boundary correction of kernel density estimation.About:
This article is published in Electric Power Systems Research.The article was published on 2015-04-01. It has received 93 citations till now. The article focuses on the topics: Parametric model & Probability distribution.read more
Citations
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Journal ArticleDOI
Clustering by fast search and find of density peaks via heat diffusion
TL;DR: CFSFDP-HD proposes a nonparametric method for estimating the probability distribution of a given dataset based on heat diffusion in an infinite domain, which accounts for both selection of the cutoff distance and boundary correction of the kernel density estimation.
Journal ArticleDOI
Wind speed probability distribution estimation and wind energy assessment
TL;DR: In this paper, Wang et al. compared the performance of parametric and non-parametric models for wind speed probability distribution and the estimation methods for these models' parameters (the widely used methods and stochastic heuristic optimization algorithm).
Journal ArticleDOI
Wind speed distribution selection – A review of recent development and progress
Christopher Jung,Dirk Schindler +1 more
TL;DR: In this paper, the authors compared the goodness-of-fit of different theoretical parametric distributions, and found that the two-parameter Weibull distribution is by far the most frequently evaluated distribution.
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Comparison of numerical methods and metaheuristic optimization algorithms for estimating parameters for wind energy potential assessment in low wind regions
TL;DR: In this article, three comparison metaheuristic optimization algorithms (MOAs), including bat algorithm (BA), cuckoo search algorithm (CS), and particle swarm optimization (PSO) are employed as comparison methods to tune the optimal parameters.
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Review of criteria for the selection of probability distributions for wind speed data and introduction of the moment and L-moment ratio diagram methods, with a case study
TL;DR: In this paper, the authors compare the goodness-of-fit of probability density functions (pdfs) to wind speed records, and discuss their advantages and disadvantages, and propose moment ratio and L-moment ratio diagram methods as alternative methods for the choice of the pdfs.
References
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BookDOI
Density estimation for statistics and data analysis
TL;DR: The Kernel Method for Multivariate Data: Three Important Methods and Density Estimation in Action.
Journal ArticleDOI
A reliable data-based bandwidth selection method for kernel density estimation
Simon J. Sheather,M. C. Jones +1 more
TL;DR: The key to the success of the current procedure is the reintroduction of a non- stochastic term which was previously omitted together with use of the bandwidth to reduce bias in estimation without inflating variance.
Journal ArticleDOI
Kernel density estimation via diffusion
TL;DR: A new adaptive kernel density estimator based on linear diffusion processes that builds on existing ideas for adaptive smoothing by incorporating information from a pilot density estimate and a new plug-in bandwidth selection method that is free from the arbitrary normal reference rules used by existing methods.
Journal ArticleDOI
Kernel density estimation via diffusion
TL;DR: In this article, a new adaptive kernel density estimator based on linear diffusion processes is proposed, which builds on existing ideas for adaptive smoothing by incorporating information from a pilot density estimate.
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A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands
TL;DR: In this paper, a review of the use of the probability density function (PDF) of wind speed is carried out for a wide collection of models, and the methods that have been used to estimate the parameters on which these models depend are reviewed and the degree of complexity of the estimation is analyzed in function of the model selected.