**Conflict of interest**

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

and B amount to 8.78% and 6.04%, respectively.

short-term forecasting of wind farm power output.

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

**5. Conclusion**

**Figure 10.**

7.5% and 5.4%, respectively.

optimize their results.

**Acknowledgements**

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

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

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

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

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

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

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

**94**

The authors declare no conflict of interest.
