太阳能光伏发电的混合预测方法使用正常的云优化算法与极端学习机器集成
Huachen Liu1, Changlong Cai2,3, Pangyue Li1
1School of Opto-electronical Engineering, Xi'an Technological University, Xi'an, 710032, China.
Scientific reports
|February 22, 2025
概括
一种新的混合预测方法,即正常云优化-极端学习机器 (NCPO-ELM),可以改善可再生能源的预测. 这种方法提高了光伏发电的准确性,这对于在可变太阳能中电网稳定性至关重要.
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
- 气象预报 气象预报
背景情况:
- 可持续能源的需求日益增加,突出了可再生能源 (如光伏) 的关键作用.
- 太阳能发电的季节性变化和间歇性质对精确的能源预测和电网稳定性构成重大挑战.
- 现有的预测方法往往难以捕捉气象数据中的复杂的空间和时间依赖性.
研究的目的:
- 提出一种新的混合预测方法,NCPO-ELM,以提高可再生能源发电的预测.
- 开发一个优化算法,正常云优化 (NCPO),以提高极端学习机器 (ELM) 的性能.
- 为了解决标准ELM在处理由于随机初始化导致的噪音和不稳定的数据方面的局限性.
主要方法:
- 开发了正常云优化 (NCPO) 算法,灵感来自群行为和云模型理论,包含五种搜索策略和随机结构.
- 在NCPO中集成正常云模型以生成特定的随机样本,改进解决方案空间的探索.
- 优化极端学习机器 (ELM) 的超参数,包括单层传送网络 (SLFN) 的隐藏层权重和偏差,使用开发的NCPO算法.
主要成果:
- 与现有的混合预测技术相比,拟议的NCPO-ELM方法显示出更高的预测准确性和性能.
- 该方法有效地捕捉了气象数据中的空间和时间依赖性,以便更可靠地预测光伏功率.
- NCPO-ELM显示了对噪声和不稳定性有所改善的稳定性,表现优于基准ELM模型.
结论:
- NCPO-ELM混合预测方法在预测可再生能源产量方面取得了重大进展,特别是在光伏发电方面.
- 新的NCPO算法有效优化了ELM,从而提高了能源预测的准确性和稳定性.
- 这种方法非常适合具有不同特征和季节性变化的时间序列数据,有助于更可靠的可再生能源电网集成.
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