一种基于改进的鱼优化规范化的极端学习机器预测风速和太阳辐射的新型混合方法
S Syama1, J Ramprabhakar2, R Anand3
1Department of Electrical and Electronics Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, India. s_syama@blr.amrita.edu.
Scientific reports
|December 31, 2024
概括
准确的日前预测太阳辐射和风速对于整合可再生能源至关重要. 这项研究引入了一种新的混合模型,可以提高预测准确性和电网稳定性.
科学领域:
- 可再生能源系统可再生能源系统
- 电网管理 电网管理
- 预测方法 预测方法
背景情况:
- 将太阳能和风能等间歇性可再生能源 (RES) 整合到现有电网中,带来了重大的控制和管理挑战.
- 确保稳定,高质量的电力供应需要精确预测太阳辐射和风速,以确保电网的稳定性和可靠性.
- 准确的预测对于可再生能源安全和经济地融入混合能源系统至关重要.
研究的目的:
- 提出一种新的混合方法来准确预测一天前的风速和太阳辐射.
- 通过结合先进的分解和优化技术来提高预测模型的性能.
- 提高可再生能源融入电网的稳定性和准确性.
主要方法:
- 利用完整的集体实证模式分解与自适应噪声 (CEEMDAN) 来将时间序列数据分解为内在模式函数 (IMF).
- 为了简化直接建模,在IMF复杂度提取,过和重建中采用了变量.
- 开发了一个非线性维度学习捕优化算法 (NDLHWOA),以优化规范化极端学习机器 (RELM) 参数用于子系列预测.
主要成果:
- 与五种既有预测模型相比,拟议的混合模型显示出更高的预测准确性和稳定性.
- 该NDLHWOA有效地优化了RELM参数,提高了它在重建的子系列中捕获隐性信息的能力.
- 综合的CEEMDAN-PE-NDLHWOA-RELM方法显著改善了风速和太阳辐射的预测性能.
结论:
- 开发的混合预测模型为间歇性可再生能源带来的挑战提供了强大的解决方案.
- 将高效的数据分解与优化的神经网络模型相结合,可显著提高预测准确性和系统稳定性.
- 该方法为安全和高效的可再生能源整合提供了可靠的框架,确保高质量的电力供应.
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