[1]蔡 翔,朱 莉. 改进深度神经网络的用户用电量预测方法[J].湖北工业大学学报,2023,(1):15-20.
　CAI Xiang,ZHU Li. Research on User Electricity Consumption Prediction Method Based on Improved Deep Neural Network[J].,2023,(1):15-20.
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 改进深度神经网络的用户用电量预测方法()
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《湖北工业大学学报》[ISSN:1003-4684/CN:42-1752/Z]

卷:
期数:
2023年第1期
页码:
15-20
栏目:
湖北工业大学学报
出版日期:
2023-03-13

文章信息/Info

Title:
 Research on User Electricity Consumption Prediction Method Based on Improved Deep Neural Network
文章编号:
1003-4684(2023)01-0015-06
作者:
 蔡 翔1; 朱 莉2
 1 湖北工业大学电气与电子工程学院， 湖北 武汉 430068；
 2 太阳能高效利用及储能运行控制湖北省重点实验室，湖北 武汉 430068
Author(s):
 CAI Xiang1; ZHU Li2
 1 School of Electrical and Electronic Engineering, Hubei Univ. of Tech.， Wuhan 430068, China；
 2 Hubei Provincial Key Laboratory of Solar Energy Efficient Utilization and Energy Storage Operation Control,Hubei Univ. of Tech., Wuhan 430068, China
关键词:
 ; 电量预测;  深度神经网络;  粒子群算法;  预测精度
Keywords:
 ; power prediction;  deep neural network;  particle swarm optimization;  prediction accuracy
分类号:
TP391
文献标志码:
A
摘要:
 针对深度神经网络对电能预测精度低的问题，提出正弦粒子群优化神经元数量的深度神经网络算法，通过对粒子群算法进行变异操作和惯性权重改进，来对深度神经网络隐藏层神经元参数进行寻优。实验结果表明，SPSONN-DNN算法相比于DNN、极度梯度提升、线性回归、两种改进PSONN-DNN算法的预测精度分别提高了1.926%、2.820%、1.500%、0.633%、0.582%；SPSONN-DNN算法相比于两种改进PSONN-DNN算法迭代次数分别减少6次、4次。
Abstract:
 With the rise of artificial intelligence, researchers have widely used deep neural networks in the field of electric power. However, due to the uncertainty of the parameters of neurons in the hidden layer of deep neural networks, the prediction accuracy of electric power is low, resulting in an imbalance between the supply and demand of electric power and in power redundancy. To this end, we propose the Sin Particle Swarm Optimization Number of Neurons of Deep Neural Networks (SPSONN-DNN) algorithm, which is based on the mutation operation and inertia weight of the particle swarm algorithm improvement, to optimize the parameters of the hidden layer neurons of the deep neural network. The experimental results show that compared with DNN, extreme gradient boosting, linear regression, and the two improved PSONN-DNN algorithms, the prediction accuracy of the SPSONN-DNN algorithm is improved by 1.926%, 2.820%, 1.500%, 0.633%, and 0.582%, respectively; Compared with the two improved PSONN-DNN algorithms, the DNN algorithm reduces the number of iterations by 6 and 4 times, respectively.

参考文献/References:

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备注/Memo

备注/Memo:
［收稿日期］ 2022-02-10
［基金项目］ 新能源及电网装备安全监测湖北省工程研究中心开放研究基金（HBSKF202124）
［第一作者］ 蔡 翔（1996-），男，湖北仙桃人，湖北工业大学硕士研究生，研究方向为大数据
［通信作者］ 朱 莉（1982-），女，湖北武汉人，湖北工业大学副教授，研究方向为人工智能和大数据
更新日期/Last Update: 2023-03-14