基于PCA-SSA-VMD和BiLSTM的超短期海上风电预测
Zhen Wang1, Youwei Ying1, Lei Kou1
1Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266075, China.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
本研究介绍了一种先进的海上风电预测模型,该模型结合了主要组件分析 (PCA),搜索算法 (SSA),变量模态分解 (VMD) 和双向长期和短期记忆 (BiLSTM) 网络,以提高准确性.
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:
- 由于数据的随机性和时间相关性,海上风电预测面临着挑战.
- 准确的预测对于海上风电场的经济调度和电网稳定性至关重要.
研究的目的:
- 开发一种新的混合模型,以提高海上风电预测的准确性.
- 解决现有的预测方法在处理复杂的风能数据方面的局限性.
主要方法:
- 使用主要组件分析 (PCA) 减少尺寸.
- 通过SSA优化的变量模态分解 (VMD) 进行风力发电数据的自适应分解.
- 使用SSA算法对双向长期和短期内存 (BiLSTM) 网络进行超参数优化.
主要成果:
- 拟议的混合模型通过模拟实验在预测准确度方面取得了显著的改进.
- PCA,SSA,VMD和BiLSTM的整合有效地处理了数据噪声和复杂的时间模式.
- 该模型在海上风电预测中的有效性得到了验证.
结论:
- 开发的PCA-SSA-VMD-BiLSTM模型为准确的海上风电预测提供了强大的解决方案.
- 这种方法提高了海上风电场的可靠性和经济调度能力.
- 该研究证实了将缩小维度,自适应分解和优化深度学习用于风能预测的协同效益.
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