基于两阶段不确定性量化的光伏发电超短期功率预测

张家安; 郝峰; 董存; 刘辉; 李志军

doi:10.19912/j.0254-0096.tynxb.2021-0887

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太阳能学报 ›› 2023, Vol. 44 ›› Issue (1) : 69-77. DOI: 10.19912/j.0254-0096.tynxb.2021-0887

基于两阶段不确定性量化的光伏发电超短期功率预测

张家安¹, 郝峰², 董存³, 刘辉⁴, 李志军¹

作者信息 +

ULTRA-SHORT-TERM POWER FORECASTING OF PHOTOVOLTAIC POWER GENERATION BASED ON TWO-STAGE UNCERTAINTY QUANTIZATION

Zhang Jiaan¹, Hao Feng², Dong Cun³, Liu Hui⁴, Li Zhijun¹

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文章历史 +

摘要

针对光伏功率预测,提出一种光伏发电出力不确定性量化分析的两阶段模型。第1阶段,首先选取待预测日之前一段时间的光伏输出功率历史数据作为训练样本,引入模糊熵（FE）将不同天气类型量化并作为输入量;然后利用集成经验模态分解（EEMD）将光伏发电功率时间序列分解为多个模态分量,再利用Hurst指数分析将不同模态分量重构为中尺度和宏尺度2个子序列,基于双向长短期记忆神经网络并引入注意力机制对重构后的2个子序列分别进行预测;最后对中尺度子序列对应的误差序列进行修正,得到光伏发电出力的点预测结果。第2阶段,根据第1阶段点预测结果得到的误差统计,采用核密度估计（KDE）方法预测光伏发电出力的区间,分别获取在95%、90%、85%及80%置信水平下的区间覆盖率（PICP）。应用中国西北地区某光伏电站运行数据作为算例,验证了该文预测方法的有效性。

Abstract

A two-stage model for quantitative analysis of output uncertainty of photovoltaic power generation is proposed. In the first stage, the photovoltaic power for a period of time before the prediction day is used as the training sample, and the fuzzy entropy is introduced to quantify different weather types as the input. Then, based on the employing integrated empirical mode decomposition （EEMD）, the photovoltaic power time series is decomposed into multiple modal components, which are reconstructed into mesoscale and macro scale subsequences with Hurst index analysis. The BiLSTM based on attention mechanism （BiLSTM-Attention） is used to predict the two subsequences respectively. The error sequence corresponding to the mesoscale subsequence is corrected to obtain the point prediction results of photovoltaic power generation. In the second stage, the nuclear density estimation （KDE） method is used to predict the interval of photovoltaic power generation according to the error statistics obtained from the point prediction results in the first stage. The prediction interval coverage probability （PICP） at 95%, 90%, 85% and 80% confidence levels are obtained respectively. Taking the operation data of a photovoltaic power station in Northwest China as an example, the effectiveness of this method is verified.

导出引用

张家安, 郝峰, 董存, 刘辉, 李志军. 基于两阶段不确定性量化的光伏发电超短期功率预测[J]. 太阳能学报. 2023, 44(1): 69-77 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0887

Zhang Jiaan, Hao Feng, Dong Cun, Liu Hui, Li Zhijun. ULTRA-SHORT-TERM POWER FORECASTING OF PHOTOVOLTAIC POWER GENERATION BASED ON TWO-STAGE UNCERTAINTY QUANTIZATION[J]. Acta Energiae Solaris Sinica. 2023, 44(1): 69-77 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0887

中图分类号： TM615

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