Short-term load forecasting model based on deep neural network

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 abilit...

Full description

Saved in:
Bibliographic Details
Published in2017 2nd International Conference on Power and Renewable Energy (ICPRE) pp. 589 - 591
Main Authors Hui, Xue, Qun, Wang, Yao, Li, Yingbin, Zhang, Lei, Shi, Zhisheng, Zhang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2017
Subjects
Biological neural networks
deep neural network
electric power system
Forecasting
genetic algorithm
Load forecasting
Load modeling
Predictive models
short-term load forecasting
Online AccessGet full text
DOI10.1109/ICPRE.2017.8390603

Cover

More Information
Summary: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.
DOI:10.1109/ICPRE.2017.8390603