摘要
针对传统神经网络负荷预测方法收敛速度慢、预测误差大的问题,提出一种基于分类识别的深度置信网络的负荷预测算法。对输入的历史负荷数据进行归一化预处理,并对深度置信网络采用层次无监督贪婪预训练方法分层预训练,将得到的结果作为监督学习训练概率模型的初始值。其深度置信网络由多层受限玻尔兹曼机构成,并采用分类识别机制和对比散度的方法训练预权值,来改善分类识别深度置信网络的学习性能。仿真结果显示,在基于200次负荷训练和温度训练的基础上,该负荷预测算法比自组织模糊神经网络和BP神经网络的收敛速度更快,预测精度更高。
A load forecasting method based on classified identification deep belief network is proposed to solve the problem of slow convergence and large prediction error of traditional neural network in load forecasting.In the proposed method,the input historical load data is normalized firstly.Then,the layered pretraining of the deep believe machine is implemented by hierarchical unsupervised greedy pre-training method.Thirdly,the pre-training results are used as the initial value of the supervised learning training model.Deep belief network is composed of multilayer restricted boltzmann machine,and the pre-weight of the restricted boltzmann machine and the classification recognition mechanism is trained by means of contrastive divergence.The learning performance of the classified identification deep belief network can be improved by this way.Simulation result shows that the load forecasting algorithm based on the 200 times of load training and temperature training,the algorithm has faster convergence speed and higher prediction accuracy than theself-organizing fuzzy neural network and the BP neural network.
作者
曹敏
李文云
钱详华
王恩
李博
李坤
唐标
李海铎
练雄
CAO Min;LI Wenyun;QIAN Xianghua;WANG En;LI Bo;LI Kun;TANG Biao;LI Haiduo;LIAN Xiong(Electric Power Research Institute,Yunnan Power Grid Co.,Ltd.,Kunming 650217,China;Yunnan Power Grid Co.,Ltd.,Kunming 650217,China;Ruili Power Supply Bureau,Yunnan Power Grid Co.,Ltd.,Ruili 678400,China)
出处
《电力需求侧管理》
2020年第2期44-50,共7页
Power Demand Side Management
基金
云南电网公司瑞丽配电网科技项目(YNKJXM20170819)。
关键词
分类识别深度置信网络
受限玻尔兹曼机
训练预权值
电力负荷
预测算法
classified identification deep belief network
re stricted boltzmann machine
train the pre-weight
power load
fore casting algorithm
