基于VMD-FE-CNN-BiLSTM的短期光伏发电功率预测

姜建国; 杨效岩; 毕洪波

doi:10.19912/j.0254-0096.tynxb.2023-0497

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (7) : 462-473. DOI: 10.19912/j.0254-0096.tynxb.2023-0497

基于VMD-FE-CNN-BiLSTM的短期光伏发电功率预测

姜建国, 杨效岩, 毕洪波

作者信息 +

PHOTOVOLTAIC POWER FORECASTING METHOD BASED ON VMD-FE-CNN-BiLSTM

Jiang Jianguo, Yang Xiaoyan, Bi Hongbo

Author information +

文章历史 +

摘要

为提高光伏功率的预测精度,提出一种变分模态分解（VMD）、模糊熵（FE）、卷积神经网络（CNN）和双向长短期记忆神经网络（BiLSTM）的光伏功率组合预测模型。该方法首先采用VMD将原始光伏序列数据分解成多个子序列,从而减少随机波动分量和噪声干扰对预测模型的影响,通过FE对每个子序列进行重组,使用一维CNN的局部连接及权值共享提取不同分量的特征,将CNN输出的特征融合并输入到BiLSTM模型中;利用BiLSTM模型建立历史数据之间的时间特征关系,得到光伏发电功率预测结果。与BiLSTM、CNN-BiLSTM、EEMD-CNN-BiLSTM、VMD-CNN-BiLSTM这4种模型进行比较,该文提出的VMD-FE-CNN-BiLSTM模型在光伏发电功率预测中具有较高的精确度和稳定性,满足光伏发电短期预测的要求。

Abstract

In order to improve the prediction accuracy of PV power, a hybrid PV power prediction model based on variational mode decomposition, fuzzy entropy, convolution neural network and bidirectional long short-term memory network： VMD-FE-CNN-BiLSTM is proposed in this paper. In view of the randomness and strong fluctuation of photovoltaic power generation, VMD is used to decompose the original photovoltaic sequence data into multiple sub-sequences, so as to reduce the influence of random fluctuation components and noise interference on the prediction model. Fuzzy entropy （FE） is used to reorganize each sub-sequence, and the features and trends of different components are extracted by using local connection and weight sharing of one-dimensional CNN, and the features output by CNN are fused and input into BiLSTM model; BiLSTM model is used to establish the time characteristic relationship between historical data, and the prediction results of photovoltaic power generation are obtained. Simulation and experimental results show that compared with BiLSTM, CNN-BiLSTM, EEMD-CNN-BiLSTM and VMD-CNN-BiLSTM, the proposed VMD-FE-CNN-BiLSTM model has higher accuracy and stability in PV power prediction, and meets the requirements of short-term PV power prediction.

导出引用

姜建国, 杨效岩, 毕洪波. 基于VMD-FE-CNN-BiLSTM的短期光伏发电功率预测[J]. 太阳能学报. 2024, 45(7): 462-473 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0497

Jiang Jianguo, Yang Xiaoyan, Bi Hongbo. PHOTOVOLTAIC POWER FORECASTING METHOD BASED ON VMD-FE-CNN-BiLSTM[J]. Acta Energiae Solaris Sinica. 2024, 45(7): 462-473 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0497

中图分类号： TM615

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