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小木虫(金币+0.5):给个红包，谢谢回帖交流
飞扬2282(金币+2): 2010-03-12 17:43
Accession number: 20100712710237
Title: Research on monthly electric energy demand forecasting under the influence of two calendars
Authors: Meng, Ming1 ; Niu, Dongxiao1 ; Sun, Wei1 ; Shang, Wei2
Author affiliation: 1 Department of Economics and Management, North China Electric Power University, Baoding, 071003, China
2 School of Economics, Hebei University, Baoding, Hebei 071002, China
Corresponding author: Meng, M. (ncepumm@126.com)
Source title: Applied Mechanics and Materials
Abbreviated source title: Appl. Mech. Mater.
Volume: 20-23
Monograph title: Information Technology for Manufacturing Systems
Issue date: 2010
Publication year: 2010
Pages: 963-968
Language: English
ISSN: 16609336
ISBN-10: 0878492879
ISBN-13: 9780878492879
Document type: Conference article (CA)
Conference name: 2010 International Conference on Information Technology for Manufacturing Systems, ITMS 2010
Conference date: January 30, 2010 - January 31, 2010
Conference location: Macao, China
Conference code: 79273
Sponsor: Intelligent Inf. Technol. Appl. Res. Assoc.; Wuhan Institute of Technology; Nanchang University; Wuhan University; Huazhong Normal University
Publisher: Trans Tech Publications, P.O. Box 1254, Clausthal-Zellerfeld, D-38670, Germany
Abstract: Monthly electric energy demand forecasting plays an important role for the running of power system. China has two tow calendars and they works at the same time. Holidays designed by the lunar calendar affect the regularity of monthly electric load recorded only by the Gregorian one. The normal fuzzy transform is advanced here to quantitatively describe the impact of the Spring Festival and further divided the influence into Jan. and Feb. After excluding the influence, the amended historical data are adopted to training RBF neural network. Experiment results show that because the regularity of raw data is improved, the generalization ability and forecasting precise of RBF neural network are improved. © (2010) Trans Tech Publications.
Number of references: 15
Main heading: Neural networks
Controlled terms: Electric load forecasting - Energy management - Fuzzy systems - Information technology - Radial basis function networks
Uncontrolled terms: Electric energies - Fuzzy transforms - Generalization ability - Historical data - Monthly load - Monthly load forecasting - Power systems - RBF Neural Network
Classification code: 961 Systems Science - 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory - 903 Information Science - 731.1 Control Systems - 723.5 Computer Applications - 723.4 Artificial Intelligence - 706.1 Electric Power Systems - 525 Energy Management and Conversion - 461.1 Biomedical Engineering
Database: Compendex
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