@inproceedings{4f397eb9cc544744964943edd8d2dd47,
title = "Short-term load prediction method for power distributing method based on back-propagation neural network",
abstract = "In the power system, as the energy management system plays an increasingly important role, the power load forecasting is an important part of the energy management system, which is the key of the electric power operation planning scheme. This paper introduces the basic principles of artificial neural network to the prediction of the short-term load of the power distributing network (DN). Firstly, the main influencing factors of load forecasting are analyzed, and then the preprocessing and normalization method of sample data are introduced. Finally, based on the BP neural network, the short-term load forecasting model of the distribution network is constructed. Taking the historical load data and the temperature data of a PDN, the effectiveness of the proposed prediction method tested.",
keywords = "BP neural network, data normalization, data preprocessing, load forecast",
author = "Yuyu Liu and Dong Li and Hongyan Pei and Keyan Liu and Yunhua Li and Liman Yang",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017 ; Conference date: 18-06-2017 Through 20-06-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/ICIEA.2017.8282964",
language = "英语",
series = "Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "881--886",
booktitle = "Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017",
address = "美国",
}