摘要
针对目前大部分光伏功率预测模型采用批量离线训练方式,且新建光伏电站训练数据较少的问题,本文提出一种基于增量学习的卷积神经网络(CNN)和长短期记忆(LSTM)网络结合的光伏功率预测模型。首先,采用CNN对气象数据进行特征提取,并通过LSTM网络进行功率预测,以此CNN-LSTM混合模型进行背景学习,训练出可用于增量学习的基准模型。其次,根据不同的时间跨度进行增量学习训练,实现模型的在线更新。针对增量学习中的灾难性遗忘问题,采用弹性权重整合(EWC)算法和在线弹性整合(Online_EWC)算法进行缓解。实验结果表明,相较于无约束的增量学习,采用EWC和Online_EWC方法的增量学习可以明显缓解灾难性遗忘问题,降低预测平均绝对误差(MAE)和均方根误差(RMSE);同时,在保证预测精度的前提下,增量学习的耗时大幅低于传统的批量学习。
Most photovoltaic(PV)power prediction models adopt batch offline training,which poses a challenge on dealing with limited training data for newly established PV power plants.In order to address this issue,a PV power prediction model based on a combination of convolutional neural network(CNN)and long short-term memory(LSTM)network using incremental learning is proposed.Firstly,the CNN is used to extract the features of the meteorological data,and the power prediction is carried out through the LSTM network.The CNN-LSTM hybrid model is used for background learning,to train a baseline model that can be used for incremental learning.Secondly,incremental learning training is carried out according to different time spans to realize the online update of the model.In order to solve the problem of catastrophic forgetting in incremental learning,this paper uses the elastic weight consolidation(EWC)algorithm and the online elastic consolidation(Online_EWC)algorithm.Experimental results show that,compared with unconstrained incremental learning,incremental learning using EWC and Online_EWC methods can significantly alleviate the problem of catastrophic forgetting and reduce the prediction mean absolute error(MAE)and root mean square error(RMSE),up to 21.7%and 18.3%,respectively.At the same time,the time cost of incremental learning is significantly lower than that of traditional batch learning.
作者
严璐晗
林培杰
程树英
陈志聪
卢箫扬
YAN Luhan;LIN Peijie;CHENG Shuying;CHEN Zhicong;LU Xiaoyang(Institute of Micro-Nano Devices and Solar Cells,College of Physics and Information Engineering,Fuzhou University,Fuzhou 350108)
出处
《电气技术》
2024年第5期31-40,共10页
Electrical Engineering
基金
福建省科技厅引导性基金资助项目(2022H0008)
福建省级科技创新重点资助项目(2022G02011)。
