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基于泥甲虫优化算法的混合深度学习模型,用于超短期的光伏功率预测.

Rui Quan^1,2, Zhizhuo Qiu^1,2, Hang Wan^1,2

¹Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China.

|November 11, 2024

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一个新的混合型号提高了超短期光伏功率预测的准确性. 这种先进的方法显著减少了预测错误,改善了太阳能预测能力.

关键词:

人工智能的人工智能是人工智能.能源建模能源建模工程工程师工程师工程师

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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
对于电网的机器学习

背景情况:

准确的超短期光伏 (PV) 功率预测对于电网稳定性和有效的太阳能集成至关重要.
现有的预测模型经常与复杂的时间动态和与光伏发电固有的相关性作斗争.
由于可变可再生能源的透率越来越高,因此需要先进的预测技术.

研究的目的:

开发和评估一种新的混合模型,以提高超短期光伏功率预测的准确性.
为了利用自我注意时间卷积网络 (SATCN) 和双向长期短期记忆 (BiLSTM) 网络的优势来提取特征.
为了优化模型性能,使用元启发式算法进行增强预测.

主要方法:

建议采用混合模型,将SATCN与BiLSTM网络集成用于时间和相关性特征提取.
在SATCN中使用了自我注意机制来捕捉复杂的时间依赖.
超参数优化是在一年的光伏电力数据集上使用虫优化算法进行的.

主要成果:

拟议的混合型号与独立的CNN,BiLSTM和TCN模型相比,表现出卓越的性能.
与其他模型相比,平均平方根误差 (RMSE) 减少了33.1%.
该模型获得了0.175的平均绝对误差 (MAE),4.821的加权平均绝对百分比误差 (wMAPE) 和0.997.99的R平方 (R2) 值.

结论:

混合SATCN-BiLSTM模型为超短期光伏功率预测提供了高度准确的解决方案.
自动注意机制和BiLSTM的整合有效地捕捉了复杂的光伏功率模式.
优化的模型显示了太阳能开发和电网管理中的实际应用的巨大潜力.