| Autor: |
Solmaz Farahbod, Taher Niknam, Mohammad Mohammadi, Jamshid Aghaei, Sattar Shojaeiyan |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 10, Pp 47063-47075 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2022.3171610 |
| Popis: |
Wind energy as one of the most promising energy alternatives brings a set of serious challenges in the operation of power systems because of the uncertain nature of wind speed. To address this problem, it is essential to establish a framework to forecast a comprehensive form of information about the wind speed. To this end, an ensemble residual regression deep network is designed to understand fully time-variant and spatial features from the historical data including wind speed and corresponding meteorological data. Then, to enhance the accuracy, a modified error-based loss function is proposed. Consequently, to provide a comprehensive form of information, a modified kernel density estimator is proposed to extract a set of probability density functions (PDFs) with a high level of accuracy and reliability. The simulation results and a comparative analysis on an actual dataset in London, U.K. demonstrate the high capability of the proposed probabilistic wind speed approach. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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