BILSTM-SimAM:一个改进的算法用于基于多特征的短期电荷预测
Mingju Chen1,2, Fuhong Qiu1, Xingzhong Xiong1,2
1School of Automation and Information Engineering, Sichuan University of Science & Engineering, Yibin 644002, China.
Mathematical biosciences and engineering : MBE
|March 8, 2024
概括
这项研究引入了一种新的BILSTM-SimAM模型,用于准确的短期功率负载预测. 该模型增强了特征提取和预测准确度,优于现有方法.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 分配系统中用户端资源的不断增长导致不平衡,需要准确的短期电力负载预测.
- 现有的预测方法在负载数据的多功能提取和噪声降低方面扎.
研究的目的:
- 开发一个先进的深度学习模型,以改进电荷预测.
- 增强从负载数据中提取多个功能,并强调关键的历史信息.
主要方法:
- 变化模式分解 (VMD) 用于将负载数据分解成内在模式函数 (IMF).
- 卷积神经网络 (CNN) 带有 Dropout,用于改进特征识别和更快的融合.
- 双向长期短期记忆 (BILSTM) 与无参数注意力机制 (SimAM) 结合,用于多功能提取.
主要成果:
- 该BILSTM-SimAM模型实现了97.8%的R2,超过了变压器,MLP和Prophet模型.
- 与主流预测模型相比,在减少错误指标的基础上表现出卓越的性能.
- 验证了VMD,CNN和BILSTM-SimAM集成用于负载预测的有效性.
结论:
- 拟议的BILSTM-SimAM网络为短期功率负载预测提供了强大而准确的解决方案.
- 这种新的方法有效地处理数据噪声,并提取关键特征以提高预测.
- 这种方法在智能电网负载管理和稳定性方面取得了重大进展.
相关概念视频
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