**Table 1.**

*Weather stations used in study.*

the data of the reference WS of each wind farm (WS1 and WS9) are combined with the data of each of the seven other weather stations, WS-2 to WS-8 (**Table 1**). Therefore, for Case B, 175 different models are generated (25 × 7). After applying these models to each wind farm, their results are then compared.

The number of neurons in the input layer also varies, depending on the value of *n*, from 15 (*n* = 3) to 180 (*n* = 36).

The variation in the number of output layer neurons is the same as in Case A.

#### **2.3 Metrics used to compare the different models**

To compare the performance of the different models generated for Cases A and B, metrics (1) and (2) were used:

$$\text{MARE} = \frac{\mathbf{1}}{m} \sum\_{j=1}^{m} \frac{\mathbf{1}}{(T - r)} \sum\_{i=1}^{T-r} \left( \frac{\left| P\_j - \dot{P}\_j \right|}{P\_j} \right)\_i = \frac{\mathbf{1}}{m} \text{MARE}\_j \tag{1}$$

$$R = \frac{1}{m} \sum\_{j=1}^{m} \frac{\sum\_{i=1}^{T-r} \left(P\_{j\_i} - \overline{P\_j}\right) \times \left(\dot{P}\_{jt} - \overline{\dot{P}\_j}\right)}{\sqrt{\left[\sum\_{i=1}^{T-r} \left(P\_{jt} - \overline{P\_j}\right)^2\right] \times \left[\sum\_{i=1}^{T-r} \left(\dot{P}\_{jt} - \overline{\dot{P}\_j}\right)^2\right]}} = \frac{1}{m} \sum\_{j=1}^{m} R\_j \tag{2}$$

**89**

**Table 2.**

*Optimization of the ANNs Models Performance in the Short-Term Forecasting of the Wind…*

The combined use of the two previous metrics is considered sufficient for the evaluation of the performance of the models and they have been widely used [38–41]. Alternatively, for the evaluation of future models, combinations of other metrics could be used [42]. For example, a combination of the Normalized Mean Absolute Error (NMAE) and the Index of Agreement (IoA) could be used.

The meteorological data (wind speed and direction) recorded by nine WSs located in four of the seven islands of the Canary Archipelago (**Table 1**) are used in this study. The mean hourly wind speed and direction data from 2008 are used in all cases. The heights of the WSs are expressed in metres above ground

To validate and compare the results obtained with the different models, information corresponding to two wind farms (WF) located on two of the seven islands of the Canary Archipelago is used. **Tables 2** and **3** shows the geographic coordinates of the wind turbines (WT) of the two wind farms (WF1 and WF2). The hourly

Stations WS1 and WS9 (**Table 1**) are the reference weather stations of wind farms WF1 and WF2, respectively. The WS1 and WS9 data and the wind power production values are provided by the respective owners of the wind farms. The data from the seven additional WSs used in the study are provided by the Canary Islands Technological Institute (Spanish initials: ITC), a publicly owned R&D company run by the Regional Government of the Canary Islands and Spain's State Meteorological

**Table 4** shows the results obtained for the coefficients of linear correlation (3)

1 1

where *CC* is the Pearson's coefficient of correlation between the wind speeds of two WSs; *NG* is the total number of data of the series. In this case, as a series of hourly data equivalent to one year is available, *NG* is equal to 8760. *V*i and *Vi*

**Code** *x* **(m)** *y* **(m)** *z* **(m)** WF1-WT1 461764 3086314 3 WF1-WT2 461839 3086301 1 WF1-WT3 461681 3086067 5 WF1-WT4 461753 3086038 2

= =

*i i i NG NG i i i i*

∑ ′

1

−× − <sup>=</sup>

∑ ∑

the speeds at instant *i* of the two WSs subject to correlation; *V* and *V*′ are the mean wind speeds of the two WSs subject to correlation for the available data series.

*NG*

=

( ) ( )

*VV V V*

( ) ( )

− × − ′

*VV V V*

2 2

′

(3)

′ are

′

wind farm power output data for 2008 are used for this study.

between the mean hourly wind speeds of the different WSs.

*CC*

Agency (Spanish initials: AEMET).

*Geographic coordinates of wind turbines in WF1.*

*DOI: http://dx.doi.org/10.5772/intechopen.97190*

**3. Materials**

level (magl).

where: MARE is the mean absolute relative error for the forecast horizon; *T* is the number of data in the test stage (see **Figure 1**); *r = T-m-n*; MAREj is the mean absolute relative error for the forecasting period *j*; Pj and *P <sup>j</sup>* are the actual and estimated wind farm power output in the forecasting period j, respectively; R is the mean value of Pearson's coefficient of correlation between the estimated and actual wind farm power output for the forecast horizon; and Rj is the mean Pearson correlation coefficient between the estimated and actual wind farm power output values for the forecasting period *j*.

*Optimization of the ANNs Models Performance in the Short-Term Forecasting of the Wind… DOI: http://dx.doi.org/10.5772/intechopen.97190*

The combined use of the two previous metrics is considered sufficient for the evaluation of the performance of the models and they have been widely used [38–41]. Alternatively, for the evaluation of future models, combinations of other metrics could be used [42]. For example, a combination of the Normalized Mean Absolute Error (NMAE) and the Index of Agreement (IoA) could be used.

#### **3. Materials**

*Theory of Complexity - Definitions, Models, and Applications*

**Code Height (magl) Latitude**

the data of the reference WS of each wind farm (WS1 and WS9) are combined with the data of each of the seven other weather stations, WS-2 to WS-8 (**Table 1**). Therefore, for Case B, 175 different models are generated (25 × 7). After applying

The number of neurons in the input layer also varies, depending on the value of

**(north)**

WS1 40 27°54′08" 15°23′17" 16 WS2 10 27°51′36" 15°23′13" 3 WS3 10 28°27′10" 13°51′54" 24 WS4 10 28°57′07" 13°36′00" 10 WS5 13 28°01′36" 15°23′16" 5 WS6 10 28°07′30" 15°40′37" 472 WS7 10 27°56′08" 15°25′24" 186 WS8 10 28°02′35" 16°34′16" 51 WS9 40 29°05′47" 13°30′21" 457

**Longitude (west)**

**Altitude (m)**

The variation in the number of output layer neurons is the same as in Case A.

To compare the performance of the different models generated for Cases A and

1 1 1 <sup>−</sup>

*MARE MARE*

 

<sup>2</sup> 1 1 <sup>2</sup>

−× −

<sup>=</sup> <sup>=</sup> − −

*j j T r T r ji j ji j i i*

∑ ∑

*ji <sup>j</sup> j j m m <sup>i</sup>*

*PP P P R R m m PP PP*

−× −

where: MARE is the mean absolute relative error for the forecast horizon; *T* is the number of data in the test stage (see **Figure 1**); *r = T-m-n*; MAREj is the mean

estimated wind farm power output in the forecasting period j, respectively; R is the mean value of Pearson's coefficient of correlation between the estimated and actual wind farm power output for the forecast horizon; and Rj is the mean Pearson correlation coefficient between the estimated and actual wind farm power output

*P P*

*i*

*m Tr P m* (1)

*j*

*j*

*P <sup>j</sup>* are the actual and

(2)

<sup>−</sup> <sup>=</sup> <sup>=</sup> <sup>−</sup>

*j i j*

( )

*i*

<sup>=</sup> <sup>=</sup>

1 1

1 1

= =

*j m T r j*

1 1 ( )

= =

( )

∑ ∑

1

− =

*T r*

∑

absolute relative error for the forecasting period *j*; Pj and

∑ ∑

these models to each wind farm, their results are then compared.

**2.3 Metrics used to compare the different models**

*n*, from 15 (*n* = 3) to 180 (*n* = 36).

**Table 1.**

*Weather stations used in study.*

B, metrics (1) and (2) were used:

values for the forecasting period *j*.

**88**

The meteorological data (wind speed and direction) recorded by nine WSs located in four of the seven islands of the Canary Archipelago (**Table 1**) are used in this study. The mean hourly wind speed and direction data from 2008 are used in all cases. The heights of the WSs are expressed in metres above ground level (magl).

To validate and compare the results obtained with the different models, information corresponding to two wind farms (WF) located on two of the seven islands of the Canary Archipelago is used. **Tables 2** and **3** shows the geographic coordinates of the wind turbines (WT) of the two wind farms (WF1 and WF2). The hourly wind farm power output data for 2008 are used for this study.

Stations WS1 and WS9 (**Table 1**) are the reference weather stations of wind farms WF1 and WF2, respectively. The WS1 and WS9 data and the wind power production values are provided by the respective owners of the wind farms. The data from the seven additional WSs used in the study are provided by the Canary Islands Technological Institute (Spanish initials: ITC), a publicly owned R&D company run by the Regional Government of the Canary Islands and Spain's State Meteorological Agency (Spanish initials: AEMET).

**Table 4** shows the results obtained for the coefficients of linear correlation (3) between the mean hourly wind speeds of the different WSs.

$$\text{CC} = \frac{\sum\_{i=1}^{NG} \left( V\_i - \overline{V} \right) \times \left( V\_i' - \overline{V'} \right)}{\sqrt{\sum\_{i=1}^{NG} \left( V\_i - \overline{V} \right)^2} \times \sqrt{\sum\_{i=1}^{NG} \left( V\_i' - \overline{V'} \right)^2}} \tag{3}$$

where *CC* is the Pearson's coefficient of correlation between the wind speeds of two WSs; *NG* is the total number of data of the series. In this case, as a series of hourly data equivalent to one year is available, *NG* is equal to 8760. *V*i and *Vi* ′ are the speeds at instant *i* of the two WSs subject to correlation; *V* and *V*′ are the mean wind speeds of the two WSs subject to correlation for the available data series.


#### **Table 2.**

*Geographic coordinates of wind turbines in WF1.*

#### *Theory of Complexity - Definitions, Models, and Applications*


#### **Table 3.**

*Geographic coordinates of wind turbines in WF2.*


**Table 4.**

*Coefficient of linear correlation between wind speeds of different weather stations in 2008.*

### **4. Results and discussion**

The discussion of the results centres on the two cases proposed in the methodology. For the various figures corresponding to the results, *t*-3 indicates that 2 periods prior to the forecasting period are chosen in addition to the forecasting period (*t*i, *t*i-1, *t*i-2), and *t* + 3 indicates a forecasting horizon of 3 periods, *t*i + 1, *t*i + 2, *t*i + 3, starting from the period for which the forecasting is being made, and so on for all combinations.

#### **4.1 Discussion of results for case A**

**Figures 4** and **5** show the results for the MARE and R metrics for the 25 generated models. In practically all cases, the MARE and R values improve as *n* increases. The only exception is for case *t*-36 in comparison with *t*-24, where the improvement is minimal or not observed. In addition, the degree of improvement increases as *m* increases (*t* + 12, *t* + 24 and *t* + 36).

**91**

*Optimization of the ANNs Models Performance in the Short-Term Forecasting of the Wind…*

For the forecasting horizons *t* + 12, *t* + 24, *t* + 36, the maximum improvements obtained for MARE between the values for *n* = 3 and *n* = 36, are 13.3%, 11.2% and 10%, respectively. For the same cases but for R, the corresponding improvements

To study the forecasting stability, an analysis has been made of the specific case of forecasting horizon *t* + 24, in which the number of periods to forecast is significant. **Figure 6** shows, for this specific case and differentiated according to *n,* the results of the variation of the relative error in the different forecasting periods,

The forecasting stability is analyzed for all the forecasting horizons (**Figure 7**). This analysis is made on the basis of the standard deviation of relative error in the

*j j*

where *SDV* is the mean standard deviation of the MARE for a forecasting time

It can be seen in **Figure 7** that for all the forecasting horizons, the SDV/MARE value decreases significantly as the number of prior hours *n* increases. This significant improvement in the stability of models is observed even for the lowest forecasting horizons. Only for the particular case of forecasting horizon *t +* 3 and when

*m*

( )

*MARE MARE*

1

2

<sup>∑</sup> (4)

*MARE*j. It can be seen how the relative error stabilizes earlier as *n* increases.

1

<sup>=</sup> <sup>−</sup> <sup>=</sup> <sup>−</sup>

*m*

the horizon passes from *t*-24 to *t*-36, no improvement is observed.

*SDV*

*DOI: http://dx.doi.org/10.5772/intechopen.97190*

are 7.9%, 8.9% and 9.2%, respectively.

forecasting horizon:.

horizon *m*.

**Figure 4.**

**Figure 5.** *R results in case A.*

*MARE results in case A.*

*Optimization of the ANNs Models Performance in the Short-Term Forecasting of the Wind… DOI: http://dx.doi.org/10.5772/intechopen.97190*

**Figure 4.** *MARE results in case A.*

*Theory of Complexity - Definitions, Models, and Applications*

**No. Coefficient of linear correlation**

*Geographic coordinates of wind turbines in WF2.*

*Coefficient of linear correlation between wind speeds of different weather stations in 2008.*

**WS1 WS2 WS3 WS4 WS5 WS6 WS7 WS8 WS9**

WS1 1.00 0.84 0.27 0.34 0.74 0.73 0.77 0.50 0.51 WS2 0.81 1.00 0.19 0.25 0.79 0.74 0.87 0.44 0.54 WS3 0.27 0.19 1.00 0.70 0.16 0.16 0.18 0.16 0.11 WS4 0.34 0.25 0.70 1.00 0.20 0.21 0.22 0.20 0.11 WS5 0.74 0.79 0.16 0.20 1.00 0.49 0.78 0.21 0.44 WS6 0.73 0.74 0.16 0.21 0.49 1.00 0.61 0.62 0.54 WS7 0.77 0.87 0.18 0.22 0.78 0.61 1.00 0.39 0.46 WS8 0.50 0.44 0.16 0.20 0.21 0.62 0.39 1.00 0.35 WS9 0.51 0.54 0.11 0.11 0.44 0.54 0.46 0.35 1.00

**Code** *x* **(m)** *y* **(m)** *z* **(m)** WF2-WT1 645043 3219819 486 WF2-WT2 645147 3219752 478 WF2-WT3 645186 3219638 473 WF2-WT4 645264 3219548 464 WF2-WT5 645333 3219462 456 WF2-WT6 645403 3219369 448 WF2-WT7 645406 3219213 440 WF2-WT8 645554 3219194 425 WF2-WT9 645664 3219133 405

The discussion of the results centres on the two cases proposed in the methodology. For the various figures corresponding to the results, *t*-3 indicates that 2 periods prior to the forecasting period are chosen in addition to the forecasting period (*t*i, *t*i-1, *t*i-2), and *t* + 3 indicates a forecasting horizon of 3 periods, *t*i + 1, *t*i + 2, *t*i + 3, starting from the period for which the forecasting is being made, and so on for all

**Figures 4** and **5** show the results for the MARE and R metrics for the 25 generated models. In practically all cases, the MARE and R values improve as *n* increases. The only exception is for case *t*-36 in comparison with *t*-24, where the improvement is minimal or not observed. In addition, the degree of improvement increases as *m*

**90**

**Table 4.**

**Table 3.**

combinations.

**4. Results and discussion**

**4.1 Discussion of results for case A**

increases (*t* + 12, *t* + 24 and *t* + 36).

#### **Figure 5.** *R results in case A.*

For the forecasting horizons *t* + 12, *t* + 24, *t* + 36, the maximum improvements obtained for MARE between the values for *n* = 3 and *n* = 36, are 13.3%, 11.2% and 10%, respectively. For the same cases but for R, the corresponding improvements are 7.9%, 8.9% and 9.2%, respectively.

To study the forecasting stability, an analysis has been made of the specific case of forecasting horizon *t* + 24, in which the number of periods to forecast is significant. **Figure 6** shows, for this specific case and differentiated according to *n,* the results of the variation of the relative error in the different forecasting periods, *MARE*j. It can be seen how the relative error stabilizes earlier as *n* increases.

The forecasting stability is analyzed for all the forecasting horizons (**Figure 7**). This analysis is made on the basis of the standard deviation of relative error in the forecasting horizon:.

$$SDV = \sqrt{\frac{\sum\_{j=1}^{m} \left(MARE\_j - MARE\right)^2}{m - 1}}\tag{4}$$

where *SDV* is the mean standard deviation of the MARE for a forecasting time horizon *m*.

It can be seen in **Figure 7** that for all the forecasting horizons, the SDV/MARE value decreases significantly as the number of prior hours *n* increases. This significant improvement in the stability of models is observed even for the lowest forecasting horizons. Only for the particular case of forecasting horizon *t +* 3 and when the horizon passes from *t*-24 to *t*-36, no improvement is observed.

**Figure 6.** *MARE variation of different prediction periods: Case of a forecasting horizon t + 24.*

**Figure 7.** *Stability of relative error SDV in forecasting horizon.*

By way of example, we will now proceed to analyze the specific cases of the forecasting models *t* + 12 and *t +* 24. To date, in the ANN models studied in the literature, the number of prior periods *n* chosen to generate the models has always been fixed. Assume that the *n* chosen for a standard model is 12. In this case, the MARE value is 0.2866 for the *t* + 12 model and 0.3382 for the *t* + 24 model (**Figure 4**). The corresponding values for the stability of the relative error are 17.4% and 14.4% (**Figure 7**), respectively. According to the analysis made with Case A, the performance of these models can be improved by choosing a higher value of *n*. If *n* is 24, the MARE values decrease to 0.2783 and 0.3206, respectively (**Figure 4**). Similarly, for an *n* of 24, the stability of the relative error in the forecasting improves to the values of 15.8% and 12.8%, respectively (**Figure 7**).

#### **4.2 Discussion of results for case B**

For the analysis of Case B, the MARE and R results of this case, with two WSs, are compared with those of Case A, with one WS. For this purpose, (5) and (6) are used.

**93**

**Figure 9.**

**Figure 8.**

*Optimization of the ANNs Models Performance in the Short-Term Forecasting of the Wind…*

2 <sup>7</sup> <sup>1</sup>

*MARE MARE*

1 1

*R R*

<sup>−</sup> <sup>=</sup> <sup>∑</sup> <sup>×</sup> <sup>2</sup> <sup>7</sup> <sup>1</sup>

*p with WS*

1 1

<sup>=</sup> *R*

<sup>=</sup> *MARE*

*p with WS*

( ) ( ) ( )

<sup>7</sup> (6)

*with WSs p with WS*

<sup>1</sup> 100%

− <sup>∆</sup> <sup>=</sup> ∑ <sup>×</sup> (5)

> ( ) ( ) ( ) *with WSs p with WS*

It can be seen in **Figures 8** and **9** how all the models generated for Case B obtain an additional improvement in performance to that already obtained for Case A. This additional improvement is in relation to ANN models developed to date which

It can also be observed that, in general, the degree of improvement increases as *m* increases. This degree of improvement slows down for forecasting horizons

The maximum additional improvements in model performance are seen in forecasting horizons *t* + 24 and *t* + 36 (7.5% and 5.5% for MARE and 3.7% and 5.4% for R, respectively). Even for the shortest forecasting horizons, *t* + 3 and *t* + 6, the maximum improvements in the MARE metric are significant (3% and 4.9%, respectively). Continuing with the specific example proposed in the analysis of results for Case A (using models *t* + 12 and *t* + 24), **Figure 10** shows the additional improvements in performance that can be obtained through the incorporation in the input layer of

<sup>1</sup> 100%

*DOI: http://dx.doi.org/10.5772/intechopen.97190*

*MARE*

always use exclusive data from a single WS.

longer than 24 hours.

data from a second WS (Case B).

*Comparison of MARE results for cases A and B.*

*Comparison of results obtained for R for cases A and B.*

7

*R*

∆

*Optimization of the ANNs Models Performance in the Short-Term Forecasting of the Wind… DOI: http://dx.doi.org/10.5772/intechopen.97190*

$$\Delta\text{ }\text{MAREE} = \frac{1}{7} \sum\_{p=1}^{7} \frac{\text{MAREE}\_{p\_{\text{(walk 1WS)}}} - \text{MAREE}\_{\text{(walk 1WS)}}}{\text{MAREE}\_{\text{(walk 1WS)}}} \times \text{1OO96} \tag{5}$$

$$
\Delta R = \frac{1}{7} \sum\_{p=1}^{7} \frac{R\_{p\_{\text{(wilt 1 WS)}}} - R\_{\text{(wilt 1 WS)}}}{R\_{\text{(wilt 1 WS)}}} \times \mathbf{100}\text{\%}\tag{6}
$$

It can be seen in **Figures 8** and **9** how all the models generated for Case B obtain an additional improvement in performance to that already obtained for Case A. This additional improvement is in relation to ANN models developed to date which always use exclusive data from a single WS.

It can also be observed that, in general, the degree of improvement increases as *m* increases. This degree of improvement slows down for forecasting horizons longer than 24 hours.

The maximum additional improvements in model performance are seen in forecasting horizons *t* + 24 and *t* + 36 (7.5% and 5.5% for MARE and 3.7% and 5.4% for R, respectively). Even for the shortest forecasting horizons, *t* + 3 and *t* + 6, the maximum improvements in the MARE metric are significant (3% and 4.9%, respectively).

Continuing with the specific example proposed in the analysis of results for Case A (using models *t* + 12 and *t* + 24), **Figure 10** shows the additional improvements in performance that can be obtained through the incorporation in the input layer of data from a second WS (Case B).

**Figure 8.** *Comparison of MARE results for cases A and B.*

**Figure 9.** *Comparison of results obtained for R for cases A and B.*

*Theory of Complexity - Definitions, Models, and Applications*

*MARE variation of different prediction periods: Case of a forecasting horizon t + 24.*

**92**

are used.

(**Figure 7**).

**Figure 6.**

**Figure 7.**

*Stability of relative error SDV in forecasting horizon.*

**4.2 Discussion of results for case B**

By way of example, we will now proceed to analyze the specific cases of the forecasting models *t* + 12 and *t +* 24. To date, in the ANN models studied in the literature, the number of prior periods *n* chosen to generate the models has always been fixed. Assume that the *n* chosen for a standard model is 12. In this case, the MARE value is 0.2866 for the *t* + 12 model and 0.3382 for the *t* + 24 model (**Figure 4**). The corresponding values for the stability of the relative error are 17.4% and 14.4% (**Figure 7**), respectively. According to the analysis made with Case A, the performance of these models can be improved by choosing a higher value of *n*. If *n* is 24, the MARE values decrease to 0.2783 and 0.3206, respectively (**Figure 4**). Similarly, for an *n* of 24, the stability of the relative error in the forecasting improves to the values of 15.8% and 12.8%, respectively

For the analysis of Case B, the MARE and R results of this case, with two WSs, are compared with those of Case A, with one WS. For this purpose, (5) and (6)

#### **Figure 10.**

*Improvements in error for two specific models due to implementation of cases A and B.*

Points A and B represent the error obtained when using a fixed *n* of 12 and only data from the reference WS of the wind farm. Points A1 and B1 represent the improvements obtained in the error when *n* is increased to 24. Points A2 and B2 represent the additional improvements obtained in the error when, in Case B, the data from a second WS are incorporated in the input layer of the ANN. For the two specific examples given, the overall improvements obtained by combining Cases A and B amount to 8.78% and 6.04%, respectively.

#### **5. Conclusion**

A series of interesting conclusions can be drawn from the results of this study with respect to possible improvements in the performance of ANN models for the short-term forecasting of wind farm power output.

The performance of the new ANN models generated for each forecast horizon improves with the increase in the number of prior 1-*h* periods (periods prior to the prediction hour), *n*, chosen for incorporation of the input layer parameters. For the forecasting horizons *t +* 12, *t* + 24 and *t* + 36, the maximum improvements obtained for MARE are 13.3%, 11.2% and 10%, respectively; and for R, the corresponding improvements are 7.9%, 8.9% and 9.2%, respectively.

A study is also made of the stability of the mean relative error for the different forecasting periods and for each forecasting horizon *m*. As *n* increases the stability of the error in the forecasting improves significantly for all forecasting horizons.

Additionally, in all the new models generated, the incorporation in the input layer of ANN of meteorological data from a second WS also improves the performance of the traditional models generated exclusively with data from the reference station of the wind farm. In general terms, the degree of improvement in model performance increases with *m*, attaining improvements in the MARE and R of up to 7.5% and 5.4%, respectively.

In view of the conclusions drawn from the present study, the original contributions described in this manuscript could be implemented in existing ANN models to optimize their results.

#### **Acknowledgements**

This research has been co-funded by ERDF funds, INTERREG MAC 2014-2020 programme, within the ENERMAC project (MAC/1.1a/117). No funding sources

**95**

**Author details**

Sergio Velázquez-Medina1

Canaria, Canary Islands, Spain

Palmas de Gran Canaria, Canary Islands, Spain

provided the original work is properly cited.

*Optimization of the ANNs Models Performance in the Short-Term Forecasting of the Wind…*

had any influence on study design, collection, analysis, or interpretation of data,

manuscript preparation, or the decision to submit for publication.

\* and Ulises Portero-Ajenjo2

1 Department of Electronics and Automatics Engineering, Universidad de Las

2 School of Industrial and Civil Engineering, University of Las Palmas de Gran

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to: sergio.velazquezmedina@ulpgc.es

*DOI: http://dx.doi.org/10.5772/intechopen.97190*

The authors declare no conflict of interest.

**Conflict of interest**

*Optimization of the ANNs Models Performance in the Short-Term Forecasting of the Wind… DOI: http://dx.doi.org/10.5772/intechopen.97190*

had any influence on study design, collection, analysis, or interpretation of data, manuscript preparation, or the decision to submit for publication.
