一个基于深度学习的双阶段混合模型,用于每天预测风速
Shahab S Band1,2, Rasoul Ameri2, Sultan Noman Qasem3,4
1Future Technology Research Center, National Yunlin University of Science and Technology, Douliu, Taiwan.
Heliyon
|January 13, 2025
概括
这项研究引入了新的混合人工智能 (AI) 模型,用于增强风速 (WS) 预测. 新的TVFEMD-GB-LSTM模型显著优于现有方法,提高了风能效率.
科学领域:
- 可再生能源系统可再生能源系统
- 环境科学中的人工智能
- 时间序列预测时间序列预测
背景情况:
- 全球越来越多的风能采用需要精确的风速 (WS) 预测,以优化轮机.
- 目前的WS模型通常利用人工智能 (AI),但需要进一步提高可靠性.
- 开发先进的AI驱动的WS模型对于高效的风能发电至关重要.
研究的目的:
- 开发和评估用于改进风速 (WS) 预测的新型混合AI模型.
- 调查基于时间变异过器的经验模式分解 (TVFEMD) 与梯度增强 (GB) 和长短期记忆 (LSTM) 模型的性能.
- 建立一个优越的混合WS预测模型,用于风能应用.
主要方法:
- 电视FEMD与独立的梯度增强 (GB) 和长短期内存 (LSTM) 模型的混合化.
- 开发用于比较分析的TVFEMD-GB和TVFEMD-LSTM混合模型.
- 创建一个同时混合TVFEMD-GB-LSTM模型,整合所有三个组件.
主要成果:
- 新型混合动力车型与独立的GB和LSTM车型相比,表现出更高的性能.
- 集成的TVFEMD-GB-LSTM模型在WS预测中实现了最高的准确性.
- 该研究成功验证了拟议的混合方法的增强预测能力.
结论:
- 开发的混合人工智能模型在风速 (WS) 预测准确度方面取得了重大进展.
- TVFEMD-GB-LSTM模型为风能可靠的WS预测提供了一个有希望的替代方案.
- 这项研究通过先进的AI技术为优化风力轮机性能开辟了新的途径.
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