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In view of the high fluctuation and randomness of wind turbine output, which affects the safe and stable operation of power system as well as the accuracy of wind power prediction, a wind power prediction method based on the fluctuation characteristics of wind power is proposed. First, the fluctuation characteristics of wind power are analyzed in terms of time scale and unit scale, and the appropriate wind power data is selected for wind power prediction. Then, a wind turbine short-term power prediction model based on least squares-support vector machine (LS-SVM) is established. The adaptive variational mode decomposition (AVMD) is used to decompose the wind power data to achieve frequency division, and the improved particle swarm optimization(IPSO) is used to optimize the model parameters affecting the re-gression prediction in the LS-SVM model. Experimental results show that the prediction model has strong adaptability, and the effectiveness of the prediction method can be proved by prediction error evaluation indexes.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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