基于数据驱动的预测,对太阳能系统的可靠性评估,包括对发电计划的不确定性
Rohit Kumar1, Sudhansu Kumar Mishra1, Amit Kumar Sahoo2
1Birla Institute of Technology, Mesra, Ranchi, 835215, India.
Scientific reports
|March 19, 2025
概括
这项研究引入了在电力系统中可靠的太阳能建模的新框架. 非线性自回归神经网络 (NAR-Net) 提高了98%的太阳能预测准确度,以更好地规划发电.
科学领域:
- 电气工程 电气工程
- 可再生能源系统可再生能源系统
- 计算智能是一种计算智能.
背景情况:
- 精确的太阳能建模对于将可再生能源整合到电网中至关重要.
- 太阳辐射和面板输出不确定性影响电力系统的可靠性.
- 优化发电规划需要太阳能发电的强有力的预测方法.
研究的目的:
- 开发一个全面的框架来评估太阳能集成电力系统的运行可靠性.
- 引入和验证使用非线性自回归神经网络 (NAR-Net) 的太阳辐射新型预测方法.
- 通过改进的建模,提高发电规划和优化太阳能效率.
主要方法:
- 开发了一个强大的不确定性模型,用于太阳辐射变化.
- 采用动态太阳能电池板输出多状态建模方法.
- 利用基于时间序列的非线性自回归神经网络 (NAR-Net) 进行五天的太阳辐射预测.
- 使用IEEE RTS 96测试系统验证了框架,并将NAR-Net与AR,ARMA和MLP模型进行了比较.
主要成果:
- 与其同行相比,NAR-Net模型在太阳辐射预测方面表现出高效率,达到98%的准确性.
- 使用诸如平均平方误差,回归和计算时间等指标评估性能.
- 提出的基于预测的方法显著提高了发电计划的可靠性.
结论:
- 开发的框架有效地评估了太阳能集成系统的运行可靠性.
- NAR-Net提供了一种卓越的太阳辐射预测方法,优化太阳能效率.
- 该研究有助于改进发电规划,支持可持续的能源未来.
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