一个强大的混沌灵感的人工智能模型,用于处理风速预测中的非线性动态.
Caner Barış1, Cağfer Yanarateş2, Aytaç Altan1
1Department of Electrical and Electronics Engineering, Zonguldak Bülent Ecevit University, Zonguldak, Turkey.
PeerJ. Computer science
|December 9, 2024
概括
准确的风速预测对于高效的风能集成至关重要. 这项研究开发了一个混合模型,使用了强大的实证模式分解和由非洲算法优化的长期短期记忆网络,以改进可再生能源发电.
科学领域:
- 可再生能源系统可再生能源系统
- 气候变化缓解缓解 气候变化缓解
- 人工智能在能源中的作用
背景情况:
- 由化石燃料驱动的气候变化需要采用可再生能源.
- 风能是关键的可再生能源,但其整合受到风速变化的挑战.
- 准确的风速预测对于优化风能效率和电网稳定性至关重要.
研究的目的:
- 开发一个强大的风速预测模型来处理非线性动态.
- 提高风能发电的准确性和效率.
- 评估一种用于预测风速的新型混合深度学习方法.
主要方法:
- 来自土耳其班迪尔玛的风速数据使用强有力的实证模式分解 (REMD) 来分解.
- 内在模式函数 (IMFs) 被长期短期记忆 (LSTM) 网络处理.
- 模型参数使用非洲优化 (AVO) 算法与帐混乱映射进行了优化,与混乱粒子群优化 (CPSO) 相比.
主要成果:
- 拟议的混合模型在预测风速方面表现出高准确度.
- 与CPSO相比,非洲优化算法有效地改善了LSTM模型参数.
- 该研究验证了先进优化和深度学习对风能应用的有效性.
结论:
- 先进的优化技术和深度学习模型显著提高了风速预测的准确性.
- 开发的混合型号为高效和可持续的风能发电提供了强大的解决方案.
- 这项研究有助于克服风能整合方面的挑战,并最大限度地发挥其潜力.
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