采用级联回归残差校正SSA-LSTM的光伏发电功率预测方法

高淑萍; 张昊洋; 宋国兵; 张警行; 赵萌雨

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采用级联回归残差校正SSA-LSTM的光伏发电功率预测方法

更新时间：2026-07-29

- 采用级联回归残差校正SSA-LSTM的光伏发电功率预测方法
- Photovoltaic Power Generation Prediction Method Employing Cascaded Regression With Residual Correction For SSA-LSTM
- 西安交通大学学报 2026年
- 作者机构：
  
  1.西安科技大学电气与控制工程学院，710054，西安
  2.西安市电气设备状态监测与供电安全重点实验室，710054，西安
  3.西安交通大学电气工程学院，710054，西安
- 作者简介：
- 基金信息：
- DOI：
  中图分类号： TM615
- 收稿：2026-02-23，
  
  修回：2026-05-28，
  
  录用：2026-06-05，
- 稿件说明：
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高淑萍, 张昊洋, 宋国兵, 等. 采用级联回归残差校正SSA-LSTM的光伏发电功率预测方法[J/OL]. 西安交通大学学报, 2026.

Gao Shuping, Zhang Haoyang, Song Guobing, et al. Photovoltaic Power Generation Prediction Method Employing Cascaded Regression With Residual Correction For SSA-LSTM[J/OL]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY, 2026.
高淑萍, 张昊洋, 宋国兵, 等. 采用级联回归残差校正SSA-LSTM的光伏发电功率预测方法[J/OL]. 西安交通大学学报, 2026. DOI：

Gao Shuping, Zhang Haoyang, Song Guobing, et al. Photovoltaic Power Generation Prediction Method Employing Cascaded Regression With Residual Correction For SSA-LSTM[J/OL]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY, 2026. 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ICEEMDAN分解与级联回归残差校正SSA-LSTM的光伏发电功率预测方法。首先，采用改进自适应噪声完备集合经验模态分解（ICEEMDAN）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（CR）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

²均大于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 stochastic fluctuations

nonlinear temporal characteristics

and noise-induced accuracy degradation of photovoltaic power generation sequences

this paper proposes a hybrid photovoltaic power forecasting method integrating improved complete ensemble empirical mode decomposition with adaptive noise ICEEMDAN and cascade-regression-based residual correction for sparrow search algorithm-optimized long short-term memory networks SSA-LSTM. First

（ICEEMDAN） is used to decompose the raw power sequenc

e into several intrinsic mode functions (IMFs)

enabling multi-scale feature extraction and noise attenuation. Then

the sparrow search algorithm (SSA) is employed to optimize the key hyperparameters of the long short-term memory (LSTM) network

and the optimized LSTM model is applied to forecast each IMF component. Finally

cascade regression (CR) is introduced to correct the residuals of the preliminary forecasts reconstructed from the component-wise outputs

thereby reducing systematic prediction bias and improving forecasting accuracy. Empirical evaluation on real-world datasets demonstrates that

when evaluated using a seasonally weighted average based on the number of test samples

the proposed method reduces the mean absolute error (MAE) and root mean square error (RMSE) by 69.97% and 67.49%

respectively

relative to the conventional LSTM model. Compared with the uncorrected model

the MAE and RMSE are further reduced by 34.01% and 27.78%

with coefficients of determination (

) consistently exceeding 0.96 across seasonal variations. These results confirm that the methodology substantially enhances forecasting accuracy

robustness

and adaptability

providing a high-fidelity and reliable solution for power prediction in solar energy systems.

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