Research on Multi-Step Prediction of Wind Speed Time Series Based on Parallel Support Vector Regression

Aiming at the problem of traditional iterative way to achieve method of multi-step prediction, new method of multi-step prediction based on parallel Support Vector Regression (SVR) was proposed. To begin with the time delay of time series will be calculated in this method, resample the time series according to the interval of time delay. What’s more the time series will be classified into several sets of data, and it sets up SVR model for the sets of each. Finally, the parallel prediction of each set is composed to get multi-step prediction result. This method not only eliminates the accumulated error, improves the accuracy of prediction, but also saves the computation time.

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Nowcasting of Wind speed using support vector regression. Experiments with time series from Gran Canaria

Renewable Energy and Power Quality Journal ◽

10.24084/repqj09.428 ◽

2011 ◽

pp. 700-705 ◽

Cited By ~ 3

Author(s):

I. Espino ◽

M. Hernández

Keyword(s):

Time Series ◽

Wind Speed ◽

Support Vector Regression ◽

Support Vector ◽

Gran Canaria

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A combined model for short-term wind speed forecasting based on empirical mode decomposition, feature selection, support vector regression and cross-validated lasso

PeerJ Computer Science ◽

10.7717/peerj-cs.732 ◽

2021 ◽

Vol 7 ◽

pp. e732

Author(s):

Tao Wang

Keyword(s):

Time Series ◽

Feature Selection ◽

Wind Speed ◽

Support Vector Regression ◽

Prediction Performance ◽

Support Vector ◽

Short Term ◽

Combined Model ◽

Wind Speed Forecasting ◽

Mode Decomposition

Background The planning and control of wind power production rely heavily on short-term wind speed forecasting. Due to the non-linearity and non-stationarity of wind, it is difficult to carry out accurate modeling and prediction through traditional wind speed forecasting models. Methods In the paper, we combine empirical mode decomposition (EMD), feature selection (FS), support vector regression (SVR) and cross-validated lasso (LassoCV) to develop a new wind speed forecasting model, aiming to improve the prediction performance of wind speed. EMD is used to extract the intrinsic mode functions (IMFs) from the original wind speed time series to eliminate the non-stationarity in the time series. FS and SVR are combined to predict the high-frequency IMF obtained by EMD. LassoCV is used to complete the prediction of low-frequency IMF and trend. Results Data collected from two wind stations in Michigan, USA are adopted to test the proposed combined model. Experimental results show that in multi-step wind speed forecasting, compared with the classic individual and traditional EMD-based combined models, the proposed model has better prediction performance. Conclusions Through the proposed combined model, the wind speed forecast can be effectively improved.

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Empirical mode decomposition based denoising method with support vector regression for time series prediction: A case study for electricity load forecasting

Measurement ◽

10.1016/j.measurement.2017.02.007 ◽

2017 ◽

Vol 103 ◽

pp. 52-61 ◽

Cited By ~ 43

Author(s):

Yusuf Yaslan ◽

Bahadır Bican

Keyword(s):

Time Series ◽

Support Vector Regression ◽

Time Series Prediction ◽

Load Forecasting ◽

Support Vector ◽

Denoising Method ◽

Mode Decomposition ◽

Electricity Load ◽

Electricity Load Forecasting

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Using the AR–SVR–CPSO hybrid model to forecast vibration signals in a high-speed train transmission system

Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit ◽

10.1177/0954409718804908 ◽

2018 ◽

Vol 233 (7) ◽

pp. 701-714

Author(s):

Yumei Liu ◽

Ningguo Qiao ◽

Congcong Zhao ◽

Jiaojiao Zhuang ◽

Guangdong Tian

Keyword(s):

Time Series ◽

Regression Model ◽

Support Vector Regression ◽

Hybrid Model ◽

High Speed ◽

Support Vector ◽

High Speed Train ◽

Support Vector Regression Model ◽

Vibration Time ◽

Auto Regression

Accurate vibration time series modeling can mine the internal law of data and provide valuable references for reliability assessment. To improve the prediction accuracy, this study proposes a hybrid model – called the AR–SVR–CPSO hybrid model – that combines the auto regression (AR) and support vector regression (SVR) models, with the weights optimized by the chaotic particle swarm optimization (CPSO) algorithm. First, the auto regression model with the difference method is employed to model the vibration time series. Second, the support vector regression model with the phase space reconstruction is constructed for predicting the vibration time series once more. Finally, the predictions of the AR and SVR models are weighted and summed together, with the weights being optimized by the CPSO. In addition, the data collected from the reliability test platform of high-speed train transmission systems and the “NASA prognostics data repository” are used to validate the hybrid model. The experimental results demonstrate that the hybrid model proposed in this study outperforms the traditional AR and SVR models.

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