基于深度递归神经网络的电力系统短期负荷预测模型

针对电力负荷非线性动态特性导致的负荷预测困难、预测精度低等问题,本文构建了深度递归神经网络短期负荷预测模型。在深度神经网络多隐层结构的基础上,深度递归神经网络增设了关联层,并以改进粒子群算法作为网络的优化学习算法,对模型权值空间进行深度优化。对某地区电网实际负荷进行预测仿真,结果表明与BP网络、深度神经网络相比,深度递归神经网络的平均绝对误差的周平均值分别降低1.61%和0.56%,验证了深度递归神经网络能够融合前馈与反馈连接,提高网络泛化能力,有效提高负荷预测精度。

Short-term Load Forecasting Model of Power System Based on Deep Recurrent Neural Network

In view of the difficulty in load forecasting and the low prediction accuracy caused by the nonlinear dynamic characteristics of power load , a short-term load forecasting model based on deep recurrent neural network is established in this paper. Based on the deep neural network ' s multi-hidden-layer structure , a connection layer is added to the deep recurrent neural network , and an improved particle swarm algorithm is adopted as the optimization learning algorithm for the network to optimize the model ' s weight space. The actual load of a regional power grid is forecasted through simu-lations , showing that the weekly average values of mean absolute errors of deep recurrent neural network are reduced by 1.61 % and 0.56 % compared with those of BP network and deep neural network , which verifies that the deep recurrent neural network can combine the feedforward and feedback connections , improve the network ' s generalization capability , and effectively improve the accuracy of load forecasting.