Volume 4 Number 12 (Dec. 2009)
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JCP 2009 Vol.4(12): 1188-1194 ISSN: 1796-203X
doi: 10.4304/jcp.4.12.1188-1194

Combination of Text Mining and Corrective Neural Network in Short-term Load Forecasting

DongXiao Niu, JianJun Wang
School of Business Administration, North China Electric Power University, Beijing, China
Abstract—Short-term load forecasting refers to short period load prediction of utility ranging from one hour to several days ahead. It is meaningful in planning and dispatching the load to meet the electricity system demand. The inaccuracy load forecasting can increase the electricity operating costs. In this paper, a novel method is presented and discussed which combines text mining and corrective neural network (TM-CNN) methods. Subsequently, a numeric example of daily maximum load forecasting is used to illustrate the performance of TM-CNN method, and the experiment results also reveal that TM-CNN method outperforms the autoregressive moving average(ARMA) and BP Artificial Neural Network(BPNN) approaches.

Index Terms—Load forecasting, text mining, artificial neural network, Autoregressive moving average (ARMA).

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Cite: DongXiao Niu, JianJun Wang, "Combination of Text Mining and Corrective Neural Network in Short-term Load Forecasting," Journal of Computers vol. 4, no. 12, pp. 1188-1194, 2009.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat,etc
E-mail: jcp@iap.org
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