摘要
针对在短期电力负荷预测中,当某区域电力负荷数据较少时,负荷预测精度较差的问题,提出一种基于1DCNN-LSTM(1D Convolutional Neural-Long Short-Term Memory Networks)和参数迁移的短期负荷预测方法,并采用1DCNN-LSTM结合迁移学习针对性提高预测精度。使用美国某地区的实际负荷数据进行仿真分析,实验结果表明,该方法能有效提升区域电力负荷数据缺失时负荷预测的精度。
In the short-term power load forecasting, when the power load data is sufficient, the accuracy of load forecasting is usually high, but when the data is missing or the data quantity is small, the accuracy of load forecasting is often poor. Therefore, when the power load data in a certain region is small, the load prediction accuracy is difficult to meet the prediction accuracy requirements. A short-term load prediction method based on 1DCNN-LSTM(1D Convolutional Neural-Long Short-Term Memory Networks) and parameter transfer is proposed. 1DCNN-LSTM combined with transfer learning is used to solve the problem of low prediction accuracy. The actual load data of a certain area in the United States are used for simulation analysis. Experimental results show that this method can effectively improve the accuracy of load prediction when regional power load data is missing.
作者
姜建国
万成德
陈鹏
郭晓丽
佟麟阁
JIANG Jianguo;WAN Chengde;CHEN Peng;GUO Xiaoli;TONG Linge(School of Electrical and Information Engineering,Northeast Petroleum University,Daqing 163318,China)
出处
《吉林大学学报（信息科学版）》
CAS
2023年第1期124-130,共7页
Journal of Jilin University（Information Science Edition）
关键词
负荷预测
迁移学习
一维卷积神经网络
长短期记忆网络
load forecasting
transfer learning
one-dimensional convolutional neural networks
long and short-term memory networks
