Full
Outline
With the gradual increase of photovoltaic installed capacity, the stable and safe operation of power system is facing challenges. By improving the prediction accuracy of photovoltaic power generation, the operation stability and safety of power system can be improved. Therefore, this paper proposes a DBO -CNN -BiLSTM -AM short -term photovoltaic power generation prediction method based on feature optimization. Firstly, Variance Inflation Factor (VIF) and Extremely Randomized Trees (ERT) are used to calculate and select the features with low VIF value and high importance score as input. Secondly, a CNN -BiLSTM -AM prediction model is established by using Convolutional Neural Network (CNN), Bi-directional Long Short Term Memory (BiLSTM) and Attention Mechanism (AM) with dynamic adjustment ability of feature weights. Finally, the Dung Beetle Optimization algorithm (DBO) is used to perform global optimization of hyperparameters to maximize the prediction accuracy. The experimental results show that the prediction accuracy of the short-term photovoltaic power prediction method proposed in this paper is significantly higher than that of the traditional method, which provides a good basis for optimizing power dis-patching and stable operation of power system.
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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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