DocumentCode :
2135082
Title :
A neural network short-term load forecasting considering human comfort index and its accumulative effect
Author :
Menting Dai ; Zhanqing Yu ; Rong Zeng ; Chijie Zhuang ; Jun Hu ; Tongzhi Li ; Jidong Liu ; Weiyi Zhu
Author_Institution :
Dept. of Electr. Eng., Tsinghua Univ., Beijing, China
fYear :
2013
fDate :
23-25 July 2013
Firstpage :
262
Lastpage :
266
Abstract :
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.
Keywords :
artificial intelligence; load forecasting; neural nets; power engineering computing; ANN structure; China; accumulative effect; artificial intelligence techniques; artificial neural network; data set selection; electricity demand research; human comfort index; load forecasting correction; neural network short-term load forecasting; short-term load forecasting model; Artificial neural networks; Forecasting; Indexes; Load forecasting; Load modeling; Predictive models; Accumulative Effect; Artificial Neural Networks; Human Comfort Index; Load Forecasting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation (ICNC), 2013 Ninth International Conference on
Conference_Location :
Shenyang
Type :
conf
DOI :
10.1109/ICNC.2013.6817982
Filename :
6817982
Link To Document :
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