Scenario Generation of Wind Power Based on Statistical Uncertainty and Variability

By Xiyuan Ma, Yuan-zhang Sun, Hua-Liang Fang. IEEE Transactions on Sustainable Energy, 2013

286 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/dab53644faf5b1267afaf5294047cb14c6b3aca6

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In Electrical and Electronic Engineering

1. Kari Torkkola
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Citations in the corpus

• Wind Speed Interval Prediction Based on Lorenz Disturbance Distribution (2020, background): “of wind speed include Weibull distribution [8]–[10], Gamma distribution [11], [12], Rayleigh distribution [13], [14], normal distribution [15], [22], Gumbel distribution [18] and so on; The”
• Model-Free Renewable Scenario Generation Using Generative Adversarial Networks (2017, unknown): “In [18], an empirical cumulative distribution function is used to replace copula estimation to model the”
• Deep Learning-Based Multivariate Probabilistic Forecasting for Short-Term Scheduling in Power Markets (2019, methodology): “In [15]–[17], the scenarios are constructed by computing the complex covariance matrix based on a multivariate Gaussian distribution assumption.”