Journal Article10.1016/J.RENENE.2014.03.068
Wind forecasting using Principal Component Analysis
TL;DR: In this article, a new statistical wind forecasting tool based on Principal Component Analysis (PCA) is presented, which is trained on past data to predict the wind speed using an ensemble of dynamically similar past events, at the same time the method provides a prediction of the likely forecasting error.
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About: This article is published in Renewable Energy. The article was published on 01 Sep 2014. The article focuses on the topics: Probabilistic forecasting & Wind speed.
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Citations
Wind speed forecasting for wind farms: A method based on support vector regression
TL;DR: Results show that, forecasts made with the proposed hybrid methodology are more accurate for medium (5–23 h ahead) short term WSF and WPF than those made with persistence and autoregressive models.
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Short-term wind speed prediction model based on GA-ANN improved by VMD
TL;DR: Variational mode decomposition (VMD) algorithm can use VMD to decompose the wind speed signal to obtain different scale fluctuations or trends, so as to fully exploit the potential information of wind speed, and obtain more accurate prediction results.
277
Short-term wind power prediction based on LSSVM–GSA model
TL;DR: Compared with the Back Propagation neural network and support vector machine (SVM) model, the simulation results show that the hybrid LSSVM–GSA model based on exponential radial basis kernel function and GSA has higher accuracy for short-term wind power prediction.
265
A novel hybrid model based on VMD-WT and PCA-BP-RBF neural network for short-term wind speed forecasting
TL;DR: Combining the data of Spanish and Chinese wind farms, the experiment results show that the hybrid model proposed in this paper has greatly improved the accuracy in short-term wind speed forecasting.
237
Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information
Gerardo J. Osorio,João C. O. Matias,Joao P. S. Catalao,Joao P. S. Catalao,Joao P. S. Catalao +4 more
TL;DR: A new hybrid evolutionary-adaptive methodology for wind power forecasting in the short-term is proposed, successfully combining mutual information, wavelet transform, evolutionary particle swarm optimization, and the adaptive neuro-fuzzy inference system.
216
References
•Book
Applied Multivariate Data Analysis
J. D. Jobson
- 01 Jun 1992
TL;DR: In this article, applied multivariate data analysis was used to analyze the performance of a multivariate dataset in the context of data mining and analysis in the field of applied multi-dimensional data analysis.
1.8K
Current methods and advances in forecasting of wind power generation
TL;DR: A review of the current methods and advances in wind power forecasting and prediction can be found in this article, where numerical wind power prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed.
1.1K
ARMA based approaches for forecasting the tuple of wind speed and direction
Ergin Erdem,Jing Shi +1 more
TL;DR: In this paper, four approaches based on autoregressive moving average (ARMA) method are employed for short-term forecasting of wind speed and direction are employed to forecast wind turbine operation and efficient energy harvesting.
874
The state-of-the-art in short-term prediction of wind power. A literature overview
Gregor Giebel,Caroline Draxl,Richard Brownsword,Georges Kariniotakis,Michael Denhard +4 more
- 15 Jan 2011
TL;DR: In this paper, the authors present the state of the art in wind power forecasting using ANEMOS.plus (Advanced Tools for the Management of Electricity Grids with Large-Scale Wind Generation) and SafeWind projects.
747