Support vector machines for wind speed prediction

By Mohamed A. Mohandes, Talal O. Halawani, Shafiqur Rehman, Ahmed A. Hussain. 2004

762 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Civil and Structural Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/bcf1ee46b898bfa4d0f4eca0e1bc5fd723d4bb84

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In Aerospace Engineering

In Civil and Structural Engineering

In Electrical and Electronic Engineering

Citations in the corpus

A review of combined approaches for prediction of short-term wind speed and power (2014, methodology): “investigated the performance of the SVM algorithm against that of Multilayer Perceptron (MLP) NNs and proved the effectiveness of SVMs for wind forecasting [20].”
Short-Term Wind Speed or Power Forecasting With Heteroscedastic Support Vector Regression (2016, methodology): “It could be modeled by various methods, including the persistence method, autoregressive models [13], [14], artificial neural networks (ANNs) [15], SVR machines [16]–[18], and the hybrid method [19].”
Financial time series forecasting using independent component analysis and support vector regression (2009, methodology): “It has been successfully applied in different problems of time series prediction such as production value forecast of machinery industry, engine reliability prediction, wind speed prediction and financial time series forecasting [7, 9, 26, 30, 45, 48, 55, 56, 57].”
Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model (2012, methodology): “And under the long-term scales, those are important for site selection, performance prediction, planning of windmills and the selection of an optimal size of the wind machine for a particular site [4].”
Atmospheric Temperature Prediction using Support Vector Machines (2009, methodology): “Various techniques like linear regression, auto regression, Multi Layer Perceptron, Radial Basis Function networks are applied to predict atmospheric parameters like temperature, wind speed, rainfall, meteorological pollution etc. [4], [5], [6], [7], [10], [11].”
Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach (2020, neutral): “32% SVM [51] 8.”
Current methods and advances in forecasting of wind power generation (2012, neutral): “[40] compared SVM to a multi-layer perceptron ANN model to predict wind speed.”
A case study on a hybrid wind speed forecasting method using BP neural network (2011, methodology): “[19] introduced a support vector machines (SVM) algorithm for wind speed prediction and compared its”