基于编码解码网络和多点聚焦线性注意力机制的短期风力发电预测
Jinlong Mei1, Chengqun Wang2, Shuyun Luo2
1School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
精确的风力发电预测对于电网稳定至关重要. 一个新的复合模型,MLL-MPFLA,结合了多层感知器 (MLP) 和LSTM网络,改善了短期预测,提高了电网安全性.
科学领域:
- 可再生能源系统可再生能源系统
- 人工智能在电力工程中的应用
- 风力发电的电网整合 风力发电的电网整合
背景情况:
- 风能是一种清洁但不可预测的能源,对电网稳定性构成挑战.
- 准确的短期风力发电预测对于减轻电网整合风险至关重要.
- 现有的模型往往难以捕捉风力发电数据的复杂时间和多维特征.
研究的目的:
- 提出一种新的复合模型,MLL-MPFLA,用于增强短期风力发电预测.
- 提高风力发电预测的准确性和可靠性,以改善电网管理.
- 根据既有预测技术验证拟议模型的性能.
主要方法:
- 一个复合模型 (MLL-MPFLA) 集成一个多层感知子 (MLP) 用于特征提取和一个基于LSTM的编码器-解码器网络用于时间分析.
- 在解码阶段利用多点聚焦线性注意力机制来完善预测.
- 对MLP,LSTM,LSTM-注意力-LSTM,LSTM-自我_注意力-LSTM和CNN-LSTM-注意力模型进行比较性绩效评估.
主要成果:
- MLL-MPFLA模型在关键指标中表现出卓越的预测性能:平均绝对误差 (MAE),根平均平方误差 (RMSE),平均绝对百分比误差 (MAPE) 和R平方 (R2).
- 结合MLP用于多维特征提取和LSTM用于时间依赖性探索的组合被证明是有效的.
- 多点聚焦线性注意力机制显著促进了预测准确度的提高.
结论:
- 拟议的MLL-MPFLA模型在短期风力发电预测方面取得了重大进展.
- 该模型将多维和时间特征集成的能力导致更准确的预测,这对于电网稳定性至关重要.
- MLL-MPFLA为优化将风能集成到电网提供了一个强大的解决方案.
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