摘要
短期电力负荷预测作为电力系统运行规划的重要依据,对电力系统的安全经济运行有重要意义。提出一种长期和短期时间序列网络(LSTNet)模型对配电台区的短期负荷变化进行预测。该模型用卷积神经网络(CNN)提取负荷数据间的局部依赖关系,用长短时记忆(LSTM)神经网络提取负荷数据长期变化趋势,再融合传统自回归模型解决神经网络对负荷数据极端值的不敏感问题,最后将某一配电台区的电力负荷数据用于网络的训练和预测过程中。通过仿真实验案例发现,相较于以往LSTM、双向长短时记忆神经网络(Bi-LSTM)和CNN-LSTM的预测模型,LSTNet模型在短期负荷预测方面更具优势、预测精度更高。
As an important basis of power system operation planning,short-term power load forecasting is great significant to the safe and economic operation of power system.A long-term and short-term time series network(LSTNet)model was proposed to predict the short-term load variation of distribution area.The model used convolutional neural network(CNN)to extract local dependencies between load data,and long and short term memory(LSTM)neural network to extract the long-term trend of load data,and then integrated the traditional autoregressive model to solve the problem that the neural network was insensitive to the extreme value of load data.Finally,the power load data of a distribution area was used in the network training and prediction process.Discovered by simulation experiment case,compared with LSTM,Bi-LSTM and CNN-LSTM prediction models,LSTNet model has more advantages and higher prediction accuracy in short-term load forecasting.
作者
顾吉鹏
邵亮
陆垂基
张有兵
张伟杰
杨吉峰
GU Jipeng;SHAO Liang;LU Chuiji;ZHANG Youbing;ZHANG Weijie;YANG Jifeng(College of Information Engineering,Zhejiang University of Technology,Hangzhou 310000,Zhejiang,China;State Grid Zhejiang Cixi Power Supply Co.,Ltd.,Cixi 315300,Zhejiang,China)
出处
《电气传动》
2023年第5期63-70,共8页
Electric Drive
基金
国家自然科学基金(51777193)。
关键词
短期电力负荷预测
长期和短期时间序列网络
长短时记忆神经网络
卷积神经网络
自回归模型
short-term power load forecasting
long-term and short-term time series network(LSTNet)
long and short term memory(LSTM)neural network
convolutional neural network(CNN)
autoregressive model
