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In a certain environment where regional wind farms distribute irregularly, the traditional convolutional neural network prediction method cannot reflect the distribution states or influence relationship of regional wind farms, and it is difficult to accurately predict the wind speed. First, to solve this problem, the technology of graph convolutional networks is used for feature modeling, and the connected graph and weight matrix are established according to the topology of multiple wind farms and the cross-correlation coefficient of wind speed in each region. Second, depending on the time dynamic characteristics of wind speed at wind farms, an improved parallel convolution structure is used to obtain the correlation between wind speed series in multiple time periods at the same wind farm. Third, based on the spatial correlation and delay effect of wind speed at wind farms, the spatio-temporal characteristics of wind speed in different regions are aggregated by using a second-order aggregation method. Finally, the verification of data from one regional wind farm shows that the proposed method can extract the spatio-temporal characteristics and improve the performance of ultra short-term wind speed prediction for multiple wind farms on 0-4 h prediction scale.
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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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