基于MEEMD-QUATRE-BILSTM的短期光伏出力区间预测

张程; 林谷青; 匡宇

doi:10.19912/j.0254-0096.tynxb.2022-1067

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太阳能学报 ›› 2023, Vol. 44 ›› Issue (11) : 40-54. DOI: 10.19912/j.0254-0096.tynxb.2022-1067

基于MEEMD-QUATRE-BILSTM的短期光伏出力区间预测

张程^1,2, 林谷青¹, 匡宇¹

作者信息 +

SHORT-TERM PV OUTPUT INTERVAL PREDICTION BASED ON MEEMD-QUATRE-BILSTM

Zhang Cheng^1,2, Lin Guqing¹, Kuang Yu¹

Author information +

文章历史 +

摘要

提出一种基于改进集成经验模态分解（MEEMD）和拟仿射变换（QUATRE）优化双向长短期记忆神经网络（BILSTM）的光伏出力区间预测模型。通过主成分分析法（PCA）对时间序列进行降维处理,利用K-均值算法将降维数据分成3种类型气象数据;然后采用MEEMD对每类光伏出力序列进行分解,将其输入QUATRE优化BILSTM神经网络和核密度估计算法（KDE）联合构建的短期光伏出力区间预测模型。最后基于宁夏光伏电站实例仿真评估模型区间预测性能,实验结果表明该模型可生成高水平光伏预测区间,能够为电力系统经济稳定运行提供可靠的决策保障。

Abstract

In this paper, a prediction model of PV output interval based on improved ensemble empirical mode decomposition （MEEMD） and quasi affine transformation （QUATRE） optimized BILSTM was proposed. Principal component analysis（PCA） was used to reduce dimension of time series, and K-means clustering algorithm was used to divide dimension reduction data into three types of meteorological data. Then MEEMD was used to decompose the output sequence of each type of PV and input it into the short-term PV output interval prediction model jointly constructed by QUATRE optimized BILSTM neural network and kernel density estimation（KDE） algorithm. Finally, the interval prediction performance of the model is evaluated based on the example of Ningxia photovoltaic power station. The experimental results show that the model can generate a high level of photovoltaic prediction interval and provide reliable decision-making guarantee for the economic and stable operation of the power system.

导出引用

张程, 林谷青, 匡宇. 基于MEEMD-QUATRE-BILSTM的短期光伏出力区间预测[J]. 太阳能学报. 2023, 44(11): 40-54 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1067

Zhang Cheng, Lin Guqing, Kuang Yu. SHORT-TERM PV OUTPUT INTERVAL PREDICTION BASED ON MEEMD-QUATRE-BILSTM[J]. Acta Energiae Solaris Sinica. 2023, 44(11): 40-54 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1067

中图分类号： TM615

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