Name
Abstract | ||
|---|---|---|
Renewable energies, in particular wind energy, are characterized as highly variable and unpredictable in terms of production, and they are increasingly more important in the context of the smart grid energy production. In this scenario, accurate prediction models and techniques are desirable to optimize the renewable energy production and reduce the environmental impact. In this article, we propose the development of predictive techniques based on mathematical models, and the integration in a simulation framework that enables the simulation of variable conditions in wind energy production. The system also offers the possibility to automatically select the most reliable model for the current conditions. Our results show an accuracy of prediction (model fit) of up to 84%. The proposed simulation framework has been stressed with real data acquired from wind turbines in the area of Spain, providing efficient model selection and tuning of optimization parameters. |
Year | Venue | Keywords |
|---|---|---|
2018 | M&S AND COMPLEXITY IN INTELLIGENT, ADAPTIVE AND AUTONOMOUS SYSTEMS SYMPOSIUM (MSCIAAS 2018) | ARIMA,N4SID,DEVS,renewable energy,smart-grid |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Laura Pérez-Vilarelle | 1 | 0 | 0.34 |
| José Luis Risco-Martín | 2 | 54 | 7.71 |
| José L. Ayala | 3 | 180 | 20.44 |