一个改进的变压器模型用于风力发电预测.
Dongjin Ma1, Yingcai Gao1, Qin Dai2
1State Key Laboratory of Low-carbon Smart Coal-fired Power Generation and Ultra-clean Emission, China Energy Science and Technology Research Institute Co., Ltd., Nanjing, Jiangsu, China.
PloS one
|October 25, 2024
概括
本研究介绍了LFformer,这是一个创新的超短期风力发电预测模型. 它通过解决风数据中复杂的非线性和多尺度模式,显著提高了预测准确性和稳定性.
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
- 时间序列预测时间序列预测
背景情况:
- 由于复杂的非线性和多尺度的时间模式,风电预测面临着挑战.
- 传统的预测方法难以准确地捕捉这些复杂的动态,影响电网稳定性和运营效率.
研究的目的:
- 开发一个创新的超短期风力发电预测模型,LFformer,能够解决多层次的复杂性.
- 提高风力发电预测的准确性和稳定性,特别是在关键的超短时间内.
主要方法:
- 该LFformer模型使用编码器-解码器架构用于信息集中和Devlin规范化用于数据缩放.
- 列德多项式将数据投射到一个有界的空间中,然后通过富里埃变换的低级近似进行特征压缩和选择.
- 一个多层混合机制将预测输入多层感知器,在回归正常化后提供最终输出.
主要成果:
- 与现有方法相比,LFformer模型显示出更高的预测准确性和稳定性.
- 在超短期风力发电预测场景中观察到显著的优势.
- 该模型有效地处理各种时间尺度的复杂非线性特征和行为模式.
结论:
- 拟议的LFformer模型为超短期风力发电预测提供了一个强大的解决方案.
- 改进的预测可以提高风力发电系统的整体效率和电网稳定性.
- 这项研究为竞争激烈的能源市场中的风电企业提供技术支持.
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