Photovoltaic Power Forecasting Method Based on a Long Short-Term Memory Network Optimized by Cascade Regression

GAO Shuping; ZHANG Haoyang; SONG Guobing; ZHANG Jingxing; ZHAO Mengyu

doi:10.7652/xjtuxb202609003

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Photovoltaic Power Forecasting Method Based on a Long Short-Term Memory Network Optimized by Cascade Regression

更新时间：2026-09-04

- Photovoltaic Power Forecasting Method Based on a Long Short-Term Memory Network Optimized by Cascade Regression
- Journal of Xi'an Jiaotong University Vol. 60, Issue 9, Pages: 19-29(2026)
- 作者机构：
  
  西安科技大学电气与控制工程学院，710054，西安
  西安科技大学西安市电气设备状态监测与供电安全重点实验室，710054，西安
  西安交通大学电气工程学院，710049，西安
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202609003
  CLC： TM615
- Received：23 February 2026，
  
  Online First：24 June 2026，
  
  Published：10 September 2026
- 稿件说明：
移动端阅览
GAO Shuping, ZHANG Haoyang, SONG Guobing, et al. Photovoltaic Power Forecasting Method Based on a Long Short-Term Memory Network Optimized by Cascade Regression[J]. Journal of Xi'an Jiaotong University,2026,60 (9):19-29. https://doi.org/10.7652/xjtuxb202609003.
DOI：

GAO Shuping, ZHANG Haoyang, SONG Guobing, et al. Photovoltaic Power Forecasting Method Based on a Long Short-Term Memory Network Optimized by Cascade Regression[J]. Journal of Xi'an Jiaotong University,2026,60 (9):19-29. https://doi.org/10.7652/xjtuxb202609003. DOI：

摘要

针对光伏发电功率序列波动性强、非线性特征显著以及预测精度易受噪声干扰等问题，提出一种基于改进自适应噪声完备集合经验模态分解（ICEEMDAN）与级联回归（CR）残差校正麻雀搜索算法优化长短期记忆网络（SSA-LSTM）的光伏发电功率预测方法。首先，采用ICEEMDAN分解对原始光伏发电功率序列进行分解，得到多个固有模态函数（IMF），以提取不同频率尺度下的时序特征并削弱噪声干扰；其次，引入麻雀搜索算法（SSA）对长短期记忆网络（LSTM）模型的关键参数进行寻优，并利用优化后的LSTM模型对各IMF分量分别进行预测；最后，采用级联回归对各分量重构后的预测结果进行残差校正，进一步减小模型输出偏差，提高整体预测精度。仿真结果表明：按四季测试样本数加权平均计算，相较于传统LSTM模型，所提方法平均绝对误差（MAE）降低了69.97%，均方根误差（RMSE）降低了67.49%；相较于未校正模型，MAE降低了34.01%，RMSE降低了27.78%，决定系数

大于0.96。研究结果表明，该方法能够有效降低预测偏差，提高模型在不同季节条件下的稳定性和适应性，为光伏发电功率预测提供了高精度、高鲁棒性的技术手段。

Abstract

To address the strong volatility and significant nonlinear characteristics of photovoltaic power sequences

as well as the susceptibility of forecasting accuracy to noise interference

a photovoltaic power forecasting method integrating improved complete ensemble empirical mode decomposition with adaptive noise（ICEEMDAN）

a sparrow search algorithm-optimized long short-term memory network（SSA-LSTM）

and cascade regression（CR）error correction is proposed.The original power sequence is decomposed using ICEEMDAN to obtain multiple intrinsic mode functions（IMFs）

thereby effectively separating multiscale temporal features and suppressing noise interference.The sparrow search algorithm（SSA）is introduced to adaptively optimize the key parameters of the LSTM mode

l.Cascade regression is used to perform parallel error correction on the preliminary prediction results obtained after reconstruction of the components

thereby further reducing the model output bias and improving the overall prediction accuracy.It is shown by simulation results that

based on a weighted average according to the numbers of test samples in the four seasons

the mean absolute error（MAE）and root mean square error（RMSE）are reduced by 69.97% and 67.49%

respectively

compared with those of the conventional LSTM model.Compared with the uncorrected model

the

MAE

and

RMSE

are reduced by 34.01% and 27.78%

respectively

and all coefficients of determination（

）exceed 0.96.The results show that the forecasting bias can be effectively reduced and the stability and adaptability of the model under different seasonal conditions can be improved by the proposed method

providing a highaccuracy and highly robust technical approach for photovoltaic power forecasting.

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