# Prediction of Wind Speed Using Hybrid Techniques

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## Abstract

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## 1. Introduction

- Least-squares support vector machine (LSSVM)
- Empirical mode decomposition (EMD)
- Wavelet transform (WT)

## 2. Materials and Methods: Wind Speed Prediction

- Method I (series prediction): SER-LSSVM.
- Method II (series prediction): EMD with WT, elimination of high variability component, signal reconstruction (REC) and then use of LSSVM (SER-WT-REC-LSSVM).
- Method III (series prediction): EMD, elimination of high variability component, signal reconstruction and then use of LSSVM (SER-EMD-REC-LSSVM).
- Method IV (series prediction): Decomposition with WT, elimination of high variability component, use of LSSVM to estimate each WT component and then signal reconstruction. (SER-WT-LSSVM-REC).
- Method V (series prediction): EMD, elimination of high variability component, use of LSSVM to estimate each EMD component and then signal reconstruction (SER-EMD-LSSVM-REC).
- Method VI (parallel prediction): Autoregressive model that estimates the wind in one hour using the simple average of the wind at that same time for previous days (PAR-AVE).
- Method VII (parallel prediction): LSSVM at hourly winds for several days (PAR-LSSVM).
- Method VIII (parallel prediction): Decomposition with WT at hourly winds for several days, eliminating the high variability component and then signal reconstruction. Subsequently, LSSVM is used to estimate winds in each of the 24 h (PAR-WT-REC-LSSVM).
- Method IX (parallel prediction): EMD at hourly winds for several days, elimination of the high variability component and then signal reconstruction. Subsequently, LSSVM is used to estimate winds in each of the 24 h (PAR-EMD-REC-LSSVM).

#### 2.1. Wavelet Transform (WT)

#### 2.2. Empirical Mode Decomposition (EMD)

#### 2.3. Least Square Support Vector Machine (LSSVM)

## 3. Test and Results

#### 3.1. Test Case

#### 3.2. Results

- Method I: LSSVM
- Method II: WT-LSSVM-REC

- Method VI: Autoregressive simple
- Method VII: LSSVM
- Method VIII: WT-REC-LSSVM

## 4. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 9.**Day-ahead prediction using least-squares support vector machine (LSSVM) for a typical day.

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**MDPI and ACS Style**

Lopez, L.; Oliveros, I.; Torres, L.; Ripoll, L.; Soto, J.; Salazar, G.; Cantillo, S. Prediction of Wind Speed Using Hybrid Techniques. *Energies* **2020**, *13*, 6284.
https://doi.org/10.3390/en13236284

**AMA Style**

Lopez L, Oliveros I, Torres L, Ripoll L, Soto J, Salazar G, Cantillo S. Prediction of Wind Speed Using Hybrid Techniques. *Energies*. 2020; 13(23):6284.
https://doi.org/10.3390/en13236284

**Chicago/Turabian Style**

Lopez, Luis, Ingrid Oliveros, Luis Torres, Lacides Ripoll, Jose Soto, Giovanny Salazar, and Santiago Cantillo. 2020. "Prediction of Wind Speed Using Hybrid Techniques" *Energies* 13, no. 23: 6284.
https://doi.org/10.3390/en13236284