基于STL分解和TPA机制的光伏功率区间预测

李逸航; 肖辉; 易纯; 龙飞宇

doi:10.19912/j.0254-0096.tynxb.2023-2052

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (12) : 22-29. DOI: 10.19912/j.0254-0096.tynxb.2023-2052

基于STL分解和TPA机制的光伏功率区间预测

李逸航, 肖辉, 易纯, 龙飞宇

作者信息 +

PV POWER INTERVAL PREDICTION BASED ON STL DECOMPOSITION AND TPA MECHANISM

Li Yihang, Xiao Hui, Yi Chun, Long Feiyu

Author information +

文章历史 +

摘要

针对光伏功率点预测包含的信息不足,无法对电网的调度提供充分依据的问题,提出一种基于STL分解和TPA机制的光伏功率预测方法。首先将原有光伏功率序列进行STL分解,得到趋势项、季节项以及残差项3类子序列。接着通过极限学习机（ELM）对趋势项进行预测;采用基于时间模式注意力机制（TPA）的双向门控循环单元（BiGRU）对季节项以及残差项进行预测;最后通过分位数回归获得区间预测结果,二者区间结果叠加获得光伏输出区间预测结果。在湖南某地光伏输出数据集上进行算例实测,通过点预测结果及区间预测结果验证了所提方法的有效性。

Abstract

Aiming at the problem that the PV power point prediction contains insufficient information to provide a sufficient basis for grid scheduling, a PV interval prediction method based on STL decomposition and temporal pattern attention mechanism is proposed. Firstly, the original PV power sequence is decomposed by STL to obtain subsequences： trend term, seasonal term and residual term. Then, the trend term is predicted by extreme learning machine （ELM）; the seasonal term and the residual term are predicted by bi-directional gated recurrent unit （BiGRU） based on temporal pattern attention （TPA）. Finally, the interval prediction is obtained by quantile regression, and the PV output interval prediction is obtained by superposing the two outputs. The proposed method is validated by the point prediction results and interval prediction results on the PV output dataset of a certain place in Hunan province.

导出引用

李逸航, 肖辉, 易纯, 龙飞宇. 基于STL分解和TPA机制的光伏功率区间预测[J]. 太阳能学报. 2024, 45(12): 22-29 https://doi.org/10.19912/j.0254-0096.tynxb.2023-2052

Li Yihang, Xiao Hui, Yi Chun, Long Feiyu. PV POWER INTERVAL PREDICTION BASED ON STL DECOMPOSITION AND TPA MECHANISM[J]. Acta Energiae Solaris Sinica. 2024, 45(12): 22-29 https://doi.org/10.19912/j.0254-0096.tynxb.2023-2052

中图分类号： TM615

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