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

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.

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Prediction of Wind Speed Using Hybrid Techniques

Energies ◽

10.3390/en13236284 ◽

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).

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

Sustainability ◽

10.3390/su11030652 ◽

2019 ◽

Vol 11 (3) ◽

pp. 652 ◽

Cited By ~ 14

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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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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A novel recursive modal parameter estimator for operational time-varying structural dynamic systems based on least squares support vector machine and time series model

Computers & Structures ◽

10.1016/j.compstruc.2019.106173 ◽

2020 ◽

Vol 229 ◽

pp. 106173 ◽

Cited By ~ 1

Author(s):

Jie Kang ◽

Li Liu ◽

Si-Da Zhou ◽

Da-Yu Wang ◽

Yuan-Chen Ma

Keyword(s):

Time Series ◽

Support Vector Machine ◽

Least Squares ◽

Dynamic Systems ◽

Support Vector ◽

Time Varying ◽

Modal Parameter ◽

Parameter Estimator ◽

Structural Dynamic ◽

Operational Time

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An application of backtracking search optimization–based least squares support vector machine for prediction of short-term wind speed

Wind Engineering ◽

10.1177/0309524x19849843 ◽

2019 ◽

Vol 44 (3) ◽

pp. 266-281 ◽

Cited By ~ 1

Author(s):

Zhongda Tian ◽

Yi Ren ◽

Gang Wang

Keyword(s):

Support Vector Machine ◽

Wind Speed ◽

Prediction Model ◽

Least Squares ◽

Support Vector ◽

Short Term ◽

Backtracking Search ◽

Wind Speed Prediction ◽

Search Optimization ◽

Speed Prediction

Wind speed prediction is an important technology in the wind power field; however, because of their chaotic nature, predicting wind speed accurately is difficult. Aims at this challenge, a backtracking search optimization–based least squares support vector machine model is proposed for short-term wind speed prediction. In this article, the least squares support vector machine is chosen as the short-term wind speed prediction model and backtracking search optimization algorithm is used to optimize the important parameters which influence the least squares support vector machine regression model. Furthermore, the optimal parameters of the model are obtained, and the short-term wind speed prediction model of least squares support vector machine is established through parameter optimization. For time-varying systems similar to short-term wind speed time series, a model updating method based on prediction error accuracy combined with sliding window strategy is proposed. When the prediction model does not match the actual short-term wind model, least squares support vector machine trains and re-establishes. This model updating method avoids the mismatch problem between prediction model and actual wind speed data. The actual collected short-term wind speed time series is used as the research object. Multi-step prediction simulation of short-term wind speed is carried out. The simulation results show that backtracking search optimization algorithm–based least squares support vector machine model has higher prediction accuracy and reliability for the short-term wind speed. At the same time, the prediction performance indicators are also improved. The prediction result is that root mean square error is 0.1248, mean absolute error is 0.1374, mean absolute percentile error is 0.1589% and R2 is 0.9648. When the short-term wind speed varies from 0 to 4 m/s, the average value of absolute prediction error is 0.1113 m/s, and average value of absolute relative prediction error is 8.7111%. The proposed prediction model in this article has high engineering application value.

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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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A New Hybrid Approach for Wind Speed Forecasting Applying Support Vector Machine with Ensemble Empirical Mode Decomposition and Cuckoo Search Algorithm

Applied Sciences ◽

10.3390/app8101754 ◽

2018 ◽

Vol 8 (10) ◽

pp. 1754 ◽

Cited By ~ 7

Author(s):

Tongxiang Liu ◽

Shenzhong Liu ◽

Jiani Heng ◽

Yuyang Gao

Keyword(s):

Wind Speed ◽

Search Algorithm ◽

Wind Farms ◽

Cuckoo Search ◽

Ensemble Empirical Mode Decomposition ◽

Cuckoo Search Algorithm ◽

Support Vector ◽

Wind Speed Forecasting ◽

Mode Decomposition ◽

Proposed Model

Wind speed forecasting plays a crucial role in improving the efficiency of wind farms, and increases the competitive advantage of wind power in the global electricity market. Many forecasting models have been proposed, aiming to enhance the forecast performance. However, some traditional models used in our experiment have the drawback of ignoring the importance of data preprocessing and the necessity of parameter optimization, which often results in poor forecasting performance. Therefore, in order to achieve a more satisfying performance in forecasting wind speed data, a new short-term wind speed forecasting method which consists of Ensemble Empirical Mode Decomposition (EEMD) for data preprocessing, and the Support Vector Machine (SVM)—whose key parameters are optimized by the Cuckoo Search Algorithm (CSO)—is developed in this paper. This method avoids the shortcomings of some traditional models and effectively enhances the forecasting ability. To test the prediction ability of the proposed model, 10 min wind speed data from wind farms in Shandong Province, China, are used for conducting experiments. The experimental results indicate that the proposed model cannot only improve the forecasting accuracy, but can also be an effective tool in assisting the management of wind power plants.

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