A Neural Network Short-Term Load Forecasting Considering Human Comfort Index and its Accumulative Effect

被引：0

作者：

Dai, Menting ^{[1
]}

Yu, Zhanqing ^{[1
]}

Zeng, Rong ^{[1
]}

Zhuang, Chijie ^{[1
]}

Hu, Jun ^{[1
]}

Li, Tongzhi ^{[2
]}

Liu, Jidong ^{[2
]}

Zhu, Weiyi ^{[2
]}

机构：

[1] Tsinghua Univ, Dept Elect Engn, State Key Lab Power Syst, Beijing 100084, Peoples R China

[2] State Grid Corp China, Shandong Elect Power Grp Corp, Jinan 250001, Peoples R China

来源：

2013 NINTH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION (ICNC) | 2013年

关键词：

Load Forecasting; Artificial Neural Networks; Human Comfort Index; Accumulative Effect; MODEL;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Short-term load forecasting is one of the most important fields of electricity demand research. Many traditional models and artificial intelligence techniques have been evaluated and tested in this task, and the Artificial Neural Network (ANN) is received much attention. In this paper a development of the artificial neural network based short-term load forecasting model considering the impact of human comfort index and its accumulative effect was proposed. The ANN structure and the training data set selection are described in the paper, and holiday load forecasting correction are adapted in this model. The implementation and results in a southeast city of China indicate that the load forecasting model developed carries out accurate forecasts.

引用

页码：262 / 266

页数：5

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