基于VMD-FE-CNN-BiLSTM的短期光伏发电功率预测
PHOTOVOLTAIC POWER FORECASTING METHOD BASED ON VMD-FE-CNN-BiLSTM
姜建国 1杨效岩 1毕洪波1
作者信息
- 1. 东北石油大学电气信息工程学院,大庆 163318
- 折叠
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.
关键词
变分模态分解/卷积神经网络/特征提取/模糊熵/光伏发电功率/预测/双向长短期记忆网络Key words
variational mode decomposition/convolutional neural networks/feature selection/fuzzy entropy/photovoltaic power generation/forecasting/bidirectional long short-term memory network引用本文复制引用
基金项目
黑龙江省自然科学基金(LH2022F005)
出版年
2024
NETL
NSTL
万方数据