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Under the background of flexible peak regulation, in order to adapt to the dynamic change of direct air-cooled unit load and the interference of environmental factors, an online learning neural network method is proposed to predict the backpressure of direct air-cooled unit. Firstly, the historical data are cleaned and Spearman correlation analysis is used to determine the important features of low redundancy affecting backpressure. Then, the Hammerstein model is used to identify the model parameters online for the backpressure. At the same time, the backpressure prediction model of direct air-cooled unit is established by using long-short memory neural network and attention mechanism, and the model is updated by online learning. The experiments results show that, the model has an absolute percentage error (MAPE) of less than 9% in predicting backpressure at different time spans within the next 1 hour, and a MAPE of less than 1% in predicting backpressure within 30 seconds. Finally, the actual power plant system is used to verify that the model can run stably in practical applications. The results of this study provide an effective method for real-time prediction of the backpressure of direct air-cooled unit, which is of great significance for the operation and management of direct air-cooled unit with flexible peak regulation.
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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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