摘要
传统的神经网络在时间相关性较强的负荷预测中精度不高。为了有效提高短期电力负荷预测精度,提出了一种基于卷积神经网络CNN和长短时记忆网络LSTM相结合的负荷预测方法。采集5维负荷特征数据,以CNN卷积层和池化层作为特征提取单元,提取数据空间耦合交互特征;将重构数据输入到LSTM网络挖掘负荷时序特征,采用Dropout技术增加模型泛化能力;利用适应性矩估计(Adam)优化器训练模型;将测试数据输入训练后的神经网络模型,预测未来1 h和12 h电负荷。实验结果表明,该负荷预测模型收敛速度和预测精度均优于改进的BP神经网络、LSTM等预测模型,其1 h负荷预测精度达到98.66%,12 h负荷预测精度达到96.81%,提高了短期电力负荷预测精度。
The traditional neural network has low accuracy in load forecasting with strong time dependence.This paper provided a load pre-diction method based on the convolutional neural network(CNN)and the long short-term memory network(LSTM)to improve the accuracy of short-term power load.Moreover,it collected 5-dimensional load characteristic data and extracted spatial coupling interaction features of data by using CNN convolution layer and pooling layers feature extraction units.In addition,it inputted the reconstructed data into the LSTM network to mine the load timing characteristics and used dropout technology to increase the model generalization ability.Besides,it used an adaptive moment estimation(Adam)optimizer to train the model.It entered the test data into the trained neural network model to predict the electric load in the next 1 h and 12 h.The experimental results show that the proposed model is better than the improved neural network forecasting models,such as improved BP neural network and LSTM,from the convergence speed and forecasting accuracy perspective.The prediction accuracy of 1 h load forecasting is 98.66%,and the 12 h load forecasting accuracy is 96.81%,which improves the accuracy of short-term power load forecast-ing.
作者
简定辉
李萍
黄宇航
梁志洋
JIAN Ding-hui;LI Ping;HUANG Yu-hang;LIANG Zhi-yang(School of Physics and Electronic-Electrical Engineering,Ningxia University,Yinchuan 750021,China)
出处
《电工电气》
2022年第9期1-6,共6页
Electrotechnics Electric
基金
宁夏自然科学基金项目(2021AAC03073)。
关键词
长短时记忆网络
短期负荷预测
Dropout技术
卷积神经网络
适应性矩估计
long short-term memory network
short-term load forecasting
Dropout technology
convolutional neural network
adaptive moment estimation
