基于Resnet-VAE融合模型的光伏功率预测方法
Photovoltaic power prediction method based on Resnet-VAE fusion model
汤倩茹 1张哲扬 1梁子怡 1王悦然 1花卉1
作者信息
- 1. 南京工程学院电力工程学院,南京 211167
- 折叠
摘要
光伏发电由于其出力受太阳辐照强度、气候等因素影响较大,其功率曲线特性往往呈现出非线性特征,传统的预测方法难以进行拟合.为此,提出一种基于残差神经网络(ResNet)与变分自编码器(VAE)相结合的融合模型,采用变分自编码器对输入数据序列进行处理,并基于机组运行历史数据,通过分析不同输入属性间的关联特性,实现了对模型输入属性的降维.模型采用机组实际运行数据进行训练,实现机组功率精准预测.
Abstract
Because the output of photovoltaic power generation is greatly affected by solar irradiation intensity,climate and other factors,its power curve characteristics often show nonlinear characteristics,and traditional prediction methods are difficult to fit.Therefore,a fusion model based on the combination of residual neural network(ResNet)and variational auto-encoder(VAE)is proposed.The variational auto-encoder is used to process the input data sequence.Based on the historical data of unit operation,the dimension reduction of model input attributes is realized by analyzing the correlation characteristics between different input attributes.The model uses the actual operation data of the unit for training to achieve accurate prediction of the unit power.
关键词
深度学习/电力系统/数据降维/变分自编码器/光伏发电功率预测Key words
deep learning/power system/data dimension reduction/variational autoencoder/photovoltaic power prediction引用本文复制引用
出版年
2024
NETL
NSTL
万方数据