基于EEMD-ALOCO-SVM模型的光伏功率短期区间预测

吴汉斌; 时珉; 郑焕坤; 张纪欣; 张华铭

doi:10.19912/j.0254-0096.tynxb.2022-1096

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太阳能学报 ›› 2023, Vol. 44 ›› Issue (11) : 64-71. DOI: 10.19912/j.0254-0096.tynxb.2022-1096

基于EEMD-ALOCO-SVM模型的光伏功率短期区间预测

吴汉斌¹, 时珉², 郑焕坤³, 张纪欣¹, 张华铭⁴

作者信息 +

SHORT-TERM INTERVAL PREDICTION OF PHOTOVOLTAIC POWER BASED ON EEMD-ALOCO-SVM MODEL

Wu Hanbin¹, Shi Min², Zheng Huankun³, Zhang Jixin¹, Zhang Huaming⁴

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文章历史 +

摘要

光伏发电功率的预测方法目前分为点值预测和区间预测两类,但点值预测方法难以适应光伏功率的随机性和波动性,因此,该文构建一种基于集合经验模态分解（EEMD）和混沌蚁狮算法（ALOCO）的支持向量机（SVM）光伏功率区间短期预测模型。首先,通过灰色关联度筛选出不同环境条件的相似日样本集,并利用EEMD将光伏出力序列分解成不同的本征模态函数;然后,利用混沌蚁狮算法对SVM的误差惩罚因子C和核函数参数γ进行优化,并利用分位数回归法对光伏的输出功率进行短期区间预测;最后,通过算例数据验证所建立模型的有效性。

Abstract

The prediction methods of photovoltaic power generation are currently divided into： point value prediction and interval prediction, but point value prediction methods are difficult to adapt to the randomness and volatility of photovoltaic power. So this paper proposes a support vector machine （SVM） short-term photovoltaic power interval prediction model based on ensemble empirical mode decomposition （EEMD） and ant lion optimizer based on chaos optimization （ALOCO）. First, similar daily sample sets with different environmental conditions are screened out by grey correlation degree, and the photovoltaic output sequence is decomposed into different eigenmode functions by EEMD. Then, the error penalty factor C and kernel function parameter γ of SVM are optimized by chaotic ant lion algorithm, The and the quantile regression method is used to predict the photovoltaic output power in a short-term interval. Finally, the validity of the established model is verified by example data.

导出引用

吴汉斌, 时珉, 郑焕坤, 张纪欣, 张华铭. 基于EEMD-ALOCO-SVM模型的光伏功率短期区间预测[J]. 太阳能学报. 2023, 44(11): 64-71 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1096

Wu Hanbin, Shi Min, Zheng Huankun, Zhang Jixin, Zhang Huaming. SHORT-TERM INTERVAL PREDICTION OF PHOTOVOLTAIC POWER BASED ON EEMD-ALOCO-SVM MODEL[J]. Acta Energiae Solaris Sinica. 2023, 44(11): 64-71 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1096

中图分类号： TK513.5

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