基于LSTM-Attention和CNN-BiGRU误差修正的光伏功率预测
PHOTOVOLTAIC POWER PREDICTION BASED ON LSTM-ATTENTION AND CNN-BiGRU ERROR CORRECTION
吐松江·卡日 1雷柯松 2马小晶 1吴现 1余凯峰1
作者信息
- 1. 新疆大学电气工程学院,乌鲁木齐 830049
- 2. 国网新疆电力有限公司昌吉供电公司,昌吉 831100
- 折叠
摘要
为有效分析与利用光伏功率预测模型中以特定规律分布的预测误差,提出基于LSTM-Attention和CNN-BiGRU误差修正的光伏功率预测模型.首先,引入注意力机制(Attention)弥补输入序列长时长短期记忆网络(LSTM)难以保留关键信息的不足,建立LSTM-Attention的预测模型对光伏功率进行初步预测.其次,将卷积神经网络(CNN)在非线性特征提取上的优势与双向门控循环单元(BiGRU)在防止多种特征相互干扰的优势相结合,搭建CNN-BiGRU误差预测模型对可能产生的误差进行预测,从而对初步预测结果进行修正.经过实例分析表明:与未经误差修正的预测结果进行对比,经CNN-BiGRU误差预测模型进行误差修正后在不同天气类型中均能有效提高预测精度.
Abstract
To effectively analyze and utilize the prediction errors distributed in a specific pattern in the photovoltaic power prediction model,a photovoltaic power prediction model based on LSTM-Attention and CNN-BiGRU error correction is proposed.Firstly,the LSTM-Attention mechanism is introduced to compensate for the shortcomings of the long short-term memory(LSTM)network,which is difficult to retain key information in the input sequence.Secondly,the advantages of convolutional neural network(CNN)in non-linear feature extraction are combined with the advantages of Bidirectional gated recurrent unit(BiGRU)in preventing multiple features from interfering with each other to build a CNN-BiGRU error prediction model is used to predict the possible errors and to correct the initial prediction results.The experimental results indicates that the CNN-BiGRU error prediction model can effectively improve the prediction accuracy in different weather types when compared with the prediction results without error correction.
关键词
光伏功率预测/深度学习/误差修正/注意力机制/长短期神经网络/双向门控循环单元Key words
photovoltaic power prediction/deep learning/error correction/attention mechanism/long-short term memory network/bidirectional gated recurrent unit引用本文复制引用
出版年
2024
NETL
NSTL
万方数据