基于混合深度学习网络和气象数据的光伏电力预测
1School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, China.
Sensors (Basel, Switzerland)
|March 13, 2024
概括
本研究介绍了QRKDDN,这是一个用于光伏发电预测的新型深度学习模型. 它准确地预测发电,用先进的技术来解决不确定性,以可靠地整合可再生能源.
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
- 统计建模 统计建模
背景情况:
- 光伏 (PV) 发电本质上是随机和波动的,挑战传统的点预测方法.
- 准确预测光伏发电对于电网稳定性和高效的可再生能源整合至关重要.
- 现有的方法往往无法捕捉与光伏功率输出相关的全部不确定性.
研究的目的:
- 开发一个强大的预测模型,QRKDDN (量子回归和内核密度估计深度学习网络),用于增强光伏功率预测.
- 为了提高确定性,间隔和概率的PV功率预测的准确性.
- 为了利用历史的气象和光伏功率数据,实现更高的预测性能.
主要方法:
- 使用定量回归 (QR) 和核密度估计 (KDE) 来量化不确定性.
- 用于气象因素选择的Pearson相关系数.
- 开发了一个结合CNN,BiGRU和注意力机制的深度学习网络,并将高斯混合模型 (GMM) 纳入日常集群.
主要成果:
- QRKDDN在确定性,间隔和概率的光伏功率预测中表现出色.
- 实验结果使用来自澳大利亚DKASC研究中心的数据验证了该模型的有效性.
- 废除实验和比较证实了QRKDDN在经典机器学习模型上的优势.
结论:
- QRKDDN在光伏电力预测方面取得了重大进展,有效地处理不确定性.
- 该模型的混合深度学习架构和集成统计方法提供了准确可靠的预测.
- 这种方法增强了可变可再生能源在电网中的整合.
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