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2007 International Conference on Computational Intelligence and Security (CIS 2007)
Case-Based Reasoning Combined with Information Entropy and Principal Component Analysis for Short-Term Load Forecasting
Harbin, Heilongjiang, China
December 15-December 19
ISBN: 0-7695-3072-9
ASCII Text
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Jinsha Yuan, Li Qu, Weihua Zhang, Li Li, "Case-Based Reasoning Combined with Information Entropy and Principal Component Analysis for Short-Term Load Forecasting," 2012 Eighth International Conference on Computational Intelligence and Security, pp. 446-450, 2007 International Conference on Computational Intelligence and Security (CIS 2007), 2007.
 
 
BibTex
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@article{ 10.1109/CIS.2007.13,
author = {Jinsha Yuan and Li Qu and Weihua Zhang and Li Li},
title = {Case-Based Reasoning Combined with Information Entropy and Principal Component Analysis for Short-Term Load Forecasting},
journal ={2012 Eighth International Conference on Computational Intelligence and Security},
volume = {0},
year = {2007},
isbn = {0-7695-3072-9},
pages = {446-450},
doi = {http://doi.ieeecomputersociety.org/10.1109/CIS.2007.13},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - 2012 Eighth International Conference on Computational Intelligence and Security
TI - Case-Based Reasoning Combined with Information Entropy and Principal Component Analysis for Short-Term Load Forecasting
SN - 0-7695-3072-9
SP446
EP450
A1 - Jinsha Yuan,
A1 - Li Qu,
A1 - Weihua Zhang,
A1 - Li Li,
PY - 2007
VL - 0
JA - 2012 Eighth International Conference on Computational Intelligence and Security
ER -
 
Short-term load forecasting (STLF) plays a vital part in the operation of electric power system, and it relates the security, stability, and economic dispatch of the system. In this paper, rough sets information entropy (IE) and principal component analysis (PCA) are applied to the attributes reduction of load cases, and respectively, the significance and relativity of load data are disposed. Thus, not only is the training time in the process of retrieval decreased, but also is effective control implemented aiming at petit factors to essential ones. In the process of revise, some impactful amendments are presented to improve prediction precision. Finally, this scheme is performed on the data of Bao Ding Electric Power Company (BDEPC) during 2000-2004, and the testing result shows that the proposed model is feasible and promising for load forecasting. Index Terms - short-term load forecasting, case-based reasoning, information entropy, principal component analysis, neural network
Citation:
Jinsha Yuan, Li Qu, Weihua Zhang, Li Li, "Case-Based Reasoning Combined with Information Entropy and Principal Component Analysis for Short-Term Load Forecasting," cis, pp.446-450, 2007 International Conference on Computational Intelligence and Security (CIS 2007), 2007
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