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The article addresses the problem of relatively low accuracy of traditional PV power prediction and proposes a hybrid TOPSISGRNN based mechanismdata driven PV plant power prediction model. Firstly, the correlation analysis of several meteorological indicators and the output power of PV power plant is carried out, and the meteorological data with high correlation is selected as the input factor of the model. The TOPSIS algorithm was used to select the optimal similar days, and then the theoretical values of their PV plant output power and meteorological data were used to build the GRNN prediction model. Finally, the model was simulated and validated by combining the historical meteorological data and power data on the DKASC website. The final test results yielded an average power prediction accuracy of 0.826 9 kW for RMSE, 3.45% for MAPE and 0.019 5 kW for MAE. The prediction accuracy of this forecasting method is significantly higher than that of a single forecasting model and has some theoretical and practical value.
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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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