Short-Term Load Forecasting Model Based on Deep Neural Network

被引：0
|
作者：
Xue Hui [1 ]
Wang Qun [1 ]
Li Yao [1 ]
Zhang Yingbin [1 ]
Shi Lei [1 ]
Zhang Zhisheng [2 ]
机构：
[1] Zaozhuang Elect Power Co, Zaozhuang 277100, Shandong, Peoples R China
[2] Qingdao Univ, Sch Automat Engn, Qingdao, Shandong, Peoples R China
来源：
PROCEEDINGS OF 2017 2ND INTERNATIONAL CONFERENCE ON POWER AND RENEWABLE ENERGY (ICPRE) | 2017年
关键词：
short-term load forecasting; deep neural network; genetic algorithm; electric power system;
D O I：
暂无
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
The short-term load forecasting model based on deep neural network is constructed in this paper. The forecasting model contains multiple hidden layers, which can extract the deep characteristics of the data. This allows the deep neural network to contain more information, extends the modeling ability of neural networks, and improves the predictive ability of the model. Genetic algorithm is used to optimize the weights and the thresholds of deep neural network. Using actual load data of electric power system for simulation test, compared with the conventional BP neural network, the test results show that the short-term load forecasting model based on deep neural network has good prediction effect.
引用
收藏
页码：589 / 591
页数：3
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