Combinatorial Prediction Method Based on Meteorological Irradiation Data and GSA-optimized VMD-BiLSTM

MA Haiyang; CHENG Guixue; WANG Hanyun; ZHAO Jinbin

doi:10.19725/j.cnki.1007-2322.2024.0014

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Combinatorial Prediction Method Based on Meteorological Irradiation Data and GSA-optimized VMD-BiLSTM

更新时间：2026-04-02

- Combinatorial Prediction Method Based on Meteorological Irradiation Data and GSA-optimized VMD-BiLSTM
- Vol. 43, Issue 2, Pages: 213-222(2026)
- 作者机构：
  
  1. 上海电力大学计算机科学与技术学院, 上海市浦东新区,201306
  2. 国网湖州供电公司,浙江省,湖州市,313000
  3. 上海电力大学电气工程学院, 上海市杨浦区,200090
- 作者简介：
- 基金信息：
- DOI：10.19725/j.cnki.1007-2322.2024.0014
  CLC： 2
- Published：2026
- 稿件说明：
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MA Haiyang, CHENG Guixue, WANG Hanyun, et al. Combinatorial Prediction Method Based on Meteorological Irradiation Data and GSA-optimized VMD-BiLSTM[J]. 2026, 43(2): 213-222.
DOI：

MA Haiyang, CHENG Guixue, WANG Hanyun, et al. Combinatorial Prediction Method Based on Meteorological Irradiation Data and GSA-optimized VMD-BiLSTM[J]. 2026, 43(2): 213-222. DOI： 10.19725/j.cnki.1007-2322.2024.0014.

摘要

为提高光伏发电功率预测精度，保证电力系统安全调度和稳定运行，提出一种基于变分模态分解(variational mode decomposition，VMD)和引力搜索算法(gravitational search algorithm，GSA)优化双向长短期记忆神经网络(bi-directional long short-term memory，BiLSTM)的组合预测方法。针对影响光伏发电功率的时序信号存在非线性和非平稳性特征的问题，选取倾斜辐照度、水平辐照度、温度、湿度等气象数据作为特征输入变量，利用VMD算法将其分解为若干个不同的模态并对每个分量分别进行建模；针对传统BiLSTM模型存在参数选择不确定性和收敛速度较慢的问题，引入GSA算法对模型参数优化，缩短手动调制参数的时间，提高超参数设置的精度和效率。使用南方某园区实际光伏发电数据集对所提模型性能进行验证，结果表明，与传统神经网络预测模型和固定参数组合预测模型相比，该方法具有更高的预测准确性和稳定性。

Abstract

To enhance the prediction accuracy of photovoltaic power generation and ensure the safe scheduling and stable operation of the power system

a new combinatorial prediction method is proposed based on variational mode decomposition (VMD) and long short term memory (BiLSTM) optimized by gravitational search algorithm (GSA). To address the issue that the nonlinear and non-stationary characteristics of the temporal signal affect the photovoltaic power generation

meteorological data such as tilt irradiance

horizontal irradiance

temperature and humidity

are selected as the characteristic input variables. These data are subsequently decomposed into several different modes by the VMD algorithm

and each component is modeled individually. To address the uncertainty of parameter selection and slow convergence of traditional BILSTM model

the GSA algorithm is introduced to optimize model parameters. This approach not only shortens the time of manual parameter modulation

but also improves the accuracy and efficiency of hyperparameter configuration. The performance of the proposed model is verified using the actual PV power generation data set of a park in South China. The results indicate that

in comparison to the traditional neural network prediction model and fixed parameter combination prediction model

the proposed method exhibits superior prediction accuracy and stability.

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Keywords

references

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