基本信息:
- 专利标题： 一种基于深度学习的智能电网短期住宅负荷预测方法
- 专利标题（英）：Intelligent power grid short-term residence load prediction method based on deep learning
- 申请号：CN201911094641.7 申请日：2019-11-11
- 公开(公告)号：CN110837934A 公开(公告)日：2020-02-25
- 发明人： 周颖杰 , 洪晔 , 朱策 , 李子璐 , 李政辉
- 申请人： 四川大学
- 申请人地址： 四川省成都市一环路南一段24号
- 专利权人： 四川大学
- 当前专利权人： 四川大学
- 当前专利权人地址： 四川省成都市一环路南一段24号
- 代理机构： 成都正华专利代理事务所
- 代理人： 李蕊
- 主分类号： G06Q10/04
- IPC分类号： G06Q10/04 ; G06Q50/06
The invention discloses an intelligent power grid short-term residence load prediction method based on deep learning. The load prediction method comprises the steps of collecting load data of electricequipment, carrying out data preprocessing on the load data of the electric equipment, establishing a deep neural network model based on an iterative residual block, carrying out training, optimizinghyper-parameters, and carrying out load prediction by utilizing the trained IRBDNN model. According to the method, the deep neural network model based on the iterative residual block is established for deep learning according to the space-time correlation between the load data of the electric equipment, and parameter optimization is carried out by utilizing a sequence grid search method, so thatthe short-term load prediction performance and prediction precision are remarkably improved.
公开/授权文献:
- CN110837934B 一种基于深度学习的智能电网短期住宅负荷预测方法 公开/授权日：2023-04-07