scholarly journals Prediction of Wind Speed Using Hybrid Techniques

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6284
Author(s):  
Luis Lopez ◽  
Ingrid Oliveros ◽  
Luis Torres ◽  
Lacides Ripoll ◽  
Jose Soto ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Least Squares ◽  
Support Vector ◽  
Single Time ◽  
Hybrid Techniques ◽  
Mode Decomposition ◽  
Vector Machines ◽  
Auto Regressive ◽  
Using Data

This paper presents a methodology to calculate day-ahead wind speed predictions based on historical measurements done by weather stations. The methodology was tested for three locations: Colombia, Ecuador, and Spain. The data is input into the process in two ways: (1) As a single time series containing all measurements, and (2) as twenty-four separate parallel sequences, corresponding to the values of wind speed at each of the 24 h in the day over several months. The methodology relies on the use of three non-parametric techniques: Least-squares support vector machines, empirical mode decomposition, and the wavelet transform. Moreover, the traditional and simple auto-regressive model is applied. The combination of the aforementioned techniques results in nine methods for performing wind prediction. Experiments using a matlab implementation showed that the least-squares support vector machine using data as a single time series outperformed the other combinations, obtaining the least root mean square error (RMSE).

scholarly journals Prediction of Wind Speed Using Hybrid Techniques. Three locations: Colombia, Ecuador and Spain

2020 ◽  
Author(s):  
Luis Lopez ◽  
Ingrid Oliveros ◽  
Luis Torres ◽  
Lacides Ripoll ◽  
Jose Soto ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Least Squares ◽  
Support Vector ◽  
Least Mean Square ◽  
Single Time ◽  
Hybrid Techniques ◽  
Mode Decomposition ◽  
Auto Regressive ◽  
Using Data

This paper presents a methodology to calculate day-ahead wind speed predictions based on historical measurements done by weather stations. The methodology was tested for three locations: Colombia, Ecuador, and Spain. The data is input into the process in two ways: 1) as a single time series containing all measurements, and 2) as twenty-four separate parallel sequences, corresponding to the values of wind speed at each of the 24 hours in the day over several months. The methodology relies on the use of three non-parametric techniques: Least-Squares Support Vector Machines, Empirical Mode Decomposition, and the Wavelet Transform. Also, the traditional and simple Auto-Regressive model is applied. The combination of the aforementioned techniques results in nine methods for performing wind prediction. Experiments using a MATLAB implementation showed that the Least-squares Support Vector Machine using data as a single time series outperformed the other combinations, obtaining the least mean square error.

Study on Prediction of Chaotic Time Series Using Least Squares Support Vector Machines

2014 ◽  
Vol 1061-1062 ◽  
pp. 935-938
Author(s):  
Xin You Wang ◽  
Guo Fei Gao ◽  
Zhan Qu ◽  
Hai Feng Pu
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Least Squares ◽  
Prediction Accuracy ◽  
Chaotic Time Series ◽  
Support Vector ◽  
Vector Machines ◽  
Online Prediction ◽  
Better Than

The predictions of chaotic time series by applying the least squares support vector machine (LS-SVM), with comparison with the traditional-SVM and-SVM, were specified. The results show that, compared with the traditional SVM, the prediction accuracy of LS-SVM is better than the traditional SVM and more suitable for time series online prediction.

MODELING OF CHAOTIC SYSTEMS WITH MULTIWAVELET TRANSFORM COMBINED WITH RECURRENT LEAST SQUARES SUPPORT VECTOR MACHINES

2007 ◽  
Vol 05 (01) ◽  
pp. 1-13
Author(s):  
ZHENG XIANG ◽  
TAIYI ZHANG ◽  
JIANCHENG SUN
Keyword(s):  
Time Series ◽  
Support Vector Machines ◽  
Least Squares ◽  
Chaotic Systems ◽  
Support Vector ◽  
Vector Machines ◽  
Temporal Structures ◽  
Multiwavelet Transform ◽  
Original Time ◽  
Dynamic Invariants

A new algorithm for modeling of chaotic systems is presented in this paper. First, more information is acquired utilizing the reconstructed embedding phase space, and the multiwavelets transform provides a sensible decomposition of the data so that the underlying temporal structures of the original time series become more tractable. Second, based on the Recurrent Least Squares Support Vector Machines (RLS-SVM), modeling of the chaotic system is realized. To demonstrate the effectiveness of our algorithm, we use the power spectrum and dynamic invariants involving the Lyapunov exponents and the correlation dimension as criterions, and then apply our method to Chua's circuit time series. The similarity of dynamic invariants between the original and generated time series shows that the proposed method can capture the dynamics of the chaotic time series more effectively.

Financial time series prediction using least squares support vector machines within the evidence framework

2001 ◽  
Vol 12 (4) ◽  
pp. 809-821 ◽  
Author(s):  
T. Van Gestel ◽  
J.A.K. Suykens ◽  
D.-E. Baestaens ◽  
A. Lambrechts ◽  
G. Lanckriet ◽  
...  
Keyword(s):  
Time Series ◽  
Support Vector Machines ◽  
Least Squares ◽  
Financial Time Series ◽  
Time Series Prediction ◽  
Support Vector ◽  
Financial Time ◽  
Evidence Framework ◽  
Vector Machines

scholarly journals Short-Term Wind Speed Forecasting Based on Hybrid Variational Mode Decomposition and Least Squares Support Vector Machine Optimized by Bat Algorithm Model

2019 ◽  
Vol 11 (3) ◽  
pp. 652 ◽  
Author(s):  
Qunli Wu ◽  
Huaxing Lin
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Least Squares ◽  
Bat Algorithm ◽  
Support Vector ◽  
Forecasting Model ◽  
Variational Mode Decomposition ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Mode Decomposition

With the integration of wind energy into electricity grids, wind speed forecasting plays an important role in energy generation planning, power grid integration and turbine maintenance scheduling. This study proposes a hybrid wind speed forecasting model to enhance prediction performance. Variational mode decomposition (VMD) was applied to decompose the original wind speed series into different sub-series with various frequencies. A least squares support vector machine (LSSVM) model with the pertinent parameters being optimized by a bat algorithm (BA) was established to forecast those sub-series extracted from VMD. The ultimate forecast of wind speed can be obtained by accumulating the prediction values of each sub-series. The results show that: (a) VMD-BA-LSSVM displays better capacity for the prediction of ultra short-term (15 min) and short-term (1 h) wind speed forecasting; (b) the proposed forecasting model was compared with wavelet decomposition (WD) and ensemble empirical mode decomposition (EEMD), and the results indicate that VMD has stronger decomposition ability than WD and EEMD, thus, significant improvements in forecasting accuracy were obtained with the proposed forecasting models compared with other forecasting methods.

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