Short-term load forecasting using diagonal recurrent neural network
Short-term load forecasting using diagonal recurrent neural network
复制标题
使用对角递归神经网络进行短期负荷预测
DOI：
10.1109/ann.1993.264286
复制
发表时间：
1993
期刊：
影响因子：
--
通讯作者：
J.H. Park
中科院分区：
文献类型：
--
作者：
K.Y. Lee;T. Choi;C. Ku;J.H. Park
本文提出了一种利用自适应学习率的对角递归神经网络进行短期负荷预测的新方法。全连接递归神经网络（FRNN）的所有神经元都是相互耦合的，其训练难度大，且难以在短时间内收敛。DRNN是对FRNN的改进模型。它比FRNN需要更少的权重，收敛速度快。动态反向传播算法与自适应学习率相结合，保证更快的收敛速度。为了考虑季节负荷变化对预测模型精度的影响，对预测精度进行了全年评估。仿真结果表明，该方法提高了预测精度
This paper presents a new approach for short term load forecasting using a diagonal recurrent neural network with an adaptive learning rate. The fully connected recurrent neural network (FRNN), where all neurons are coupled to one another, is difficult to train and to converge in a short time. The DRNN is a modified model of FRNN. It requires fewer weights than FRNN and rapid convergence has been demonstrated. A dynamic backpropagation algorithm coupled with an adaptive learning rate guarantees even faster convergence. To consider the effect of seasonal load variation on the accuracy of the proposed forecasting model, forecasting accuracy is evaluated throughout a whole year. Simulation results show that the forecast accuracy is improved.<<ETX>>