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In order to improve the efficiency of tugboat operation scheduling and energy utilization,
an improved intelligent load forecasting algorithm integrating dynamic data preprocessing and online learning is proposed. Based on a hybrid LSTM-Adaboost architecture, the algorithm addresses the issues of temporal feature degradation and multimodal data fusion through the integration of differentiated LSTM weak predictors and dynamic weight allocation (error sensitivity penalty mechanism), and designs an online learning trigger mechanism (automatic retraining based on prediction error threshold) to achieve dynamic model updating. Additionally, an environmental data collaborative optimization module, including tidal information, is introduced to enhance the adaptability of load forecasting to port conditions. The algorithm is compared with conventional LSTM-Adaboost to validate its effectiveness.
The results indicate that after iterative optimization, the mean squared error of the improved algorithm is reduced by 40.8% compared to the conventional LSTM-Adaboost algorithm, demonstrating higher prediction accuracy and environmental adaptability.
The research results can provide a reference for tugboat energy optimization, safety management, and intelligent scheduling in ports.
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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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