| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560396 |
| ISSN: | 1405-5546 |
| Autores: | Manero, Jaume1 Béjar, Javier1 Cortés, Ulises1 |
| Instituciones: | 1Universitat Politécnica de Catalunya, Barcelona, Cataluña. España 2Barcelona Supercomputing Center, Barcelona. España |
| Año: | 2018 |
| Periodo: | Oct-Dic |
| Volumen: | 22 |
| Número: | 4 |
| Paginación: | 1085-1098 |
| País: | México |
| Idioma: | Inglés |
| Resumen en inglés | Renewable energy is intermittent by nature and to integrate this energy into the Grid while assuring safety and stability the accurate forecasting of the renewable energy generation is critical. Wind Energy prediction is based on the ability to forecast wind. There are many methods for wind forecasting based on the statistical properties of the wind time series and in the integration of meteorological information, these methods are being used commercially around the world. But one family of new methods for wind power forecasting is surging based on Machine Learning Deep Learning techniques. This paper analyzes the characteristics of the Wind Speed time series data and performs a literature review of recently published works of wind power forecasting using Machine Learning approaches (neural and deep learning networks), which have been published in the last few years. |
| Keyword: | Wind power forecast, Wind speed forecast, Short-term prediction, Machine learning, Deep learning, Neural networks |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
