适应性专家融合模型用于在线风能预测
Renfang Wang1, Jingtong Wu2, Xu Cheng3
1College of big data and software engineering, Zhejiang Wanli University, 315200 Ningbo, China.
概括
一个新的自适应专家融合模型 (EFM+) 通过动态组合XGBoost和LSTM模型来改进在线风能预测. 这种方法提高了准确性和稳定性,这对于管理可变风能发电至关重要.
科学领域:
- 可再生能源系统可再生能源系统
- 人工智能在能源中的作用
背景情况:
- 风力发电预测对于电网稳定至关重要,因为它具有固有的变性.
- 现有的方法难以实时适应变化的天气和数据分布.
研究的目的:
- 推出一种新的自适应专家融合模型 (EFM+),用于准确的在线风力发电预测.
- 提高风力发电预测模型的适应性和稳定性.
主要方法:
- 开发了一个集体模型 (EFM+),集成XGBoost和自注意LSTM与动态权重.
- 根据最近的样本性能和错误实施了自适应权重更新.
- 启用贝叶斯推理来实时量化不确定性.
主要成果:
- 与现有模型相比,EFM+显示出更高的预测准确度和更少的误差.
- 该模型在各种操作场景中表现出高强度和稳定性.
- 灵敏度和剥离分析证实了EFM+组件的有效性.
结论:
- EFM+为在线风力发电预测提供了一个有希望的解决方案,有效地解决了非静止性和不确定性.
- 适应性聚变方法提高了电力系统运行预测的可靠性.
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