使用Q-CNN-GRUU进行太阳能光伏发电的季节性量子预测
Louiza Ait Mouloud1, Aissa Kheldoun1, Samira Oussidhoum2
1Laboratory of Signals Systems, Institute of Electrical and Electronic Engineering, University M'hamed Bougara, Boumerdes, 35000, Algeria.
Scientific reports
|July 27, 2025
概括
本研究引入了一种混合量子-CNN-GRU模型,用于准确的太阳能预测,其性能优于现有的方法. 整合数值天气预报数据进一步提高了预测准确度,这对电网可靠性至关重要.
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
- 气候建模和气候预测
背景情况:
- 准确的太阳能预测对于电网可靠性和可再生能源整合至关重要.
- 现有的深度学习模型在捕捉不同气候和时间地平线的复杂太阳变化方面面临挑战.
- 概率预测对于量化太阳能发电的不确定性至关重要.
研究的目的:
- 开发和评估一种新的混合量子-卷积神经网络门式反复单元 (CNN-GRU) 模型,用于日内概率太阳能预测.
- 评估模型在不同地理,气候和季节条件下的适应性和性能.
- 调查纳入数字天气预报 (NWP) 数据对预报准确性的影响.
主要方法:
- 开发了一种混合量子-CNN-GRU模型,将CNN用于空间特征提取和GRU用于时间依赖性.
- 该模型使用来自全球不同地区 (荷兰,爱丽丝泉,河北) 的太阳能数据集进行了训练和验证.
- 使用连续排列概率得分 (CRPS) 和可靠性图表评估性能,与量子-GRU和量子-LSTM模型进行基准测试.
主要成果:
- 与独立的Quantile-GRU和Quantile-LSTM模型相比,Quantile-CNN-GRU模型在各种预测时间和季节的概率太阳能预测中表现优异.
- 整合NWP数据显著提高了预测能力,特别是在更长的预测时间 (12小时,24小时) 和过渡季节 (春季,秋季) 中.
- 敏感性分析证实了NWP数据对预测准确性和可靠性的积极影响.
结论:
- 混合量子-CNN-GRU模型为概率太阳能预测提供了强大而准确的解决方案.
- 集成NWP数据是提高太阳能预测准确性的关键策略,特别是在可变条件下.
- 这种先进的预测方法支持改进的电网管理和增加可再生能源透率.
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