Abstract
Utilizing the advantage of data mining technology in processing large data and eliminating redundant information, the system mines the historical daily loading which has the same meteorological category as the forecasting day in order to compose data sequence with highly similar meteorological features. With this method it can decrease SVM training data and overcome the disadvantage of very large data and slow processing speed when constructing SVM model. Comparing with single SVM and BP neural network in short-term load forecasting, this new method can achieve greater forecasting accuracy. It is denoted that the SVM learning system has advantage when the information preprocessing is based on data mining technology.
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D. Niu, S. Cao, and Y. Zhao, Technology and Application of Power Load Forecasting (China Power Press: Bei**g, PK, 1998).
L.D. Chen and S. Toru, Data mining methods, applications, and tools, Information system management. Volume 17, Number 1, pp.65–70, (2000).
W. Zhang, T. Zeng, and H. Li, Parallel mining association rules based on grou**, Computer engineering. Volume 30, Number 22, pp.84–85, (2004).
Q. Li, L. Yang, X. Zhang, An effective apriori algorithm for association rules in data mining, Computer application and software. Volume 21, Number 12, pp.84–86, (2004).
N.V. Vladimir and X. Zhang, Nature of Statistics Theory (Tinghua University Press: Bei**g, PK, 2000).
N. Deng and Y. Tian, The New Approach in Data Mining-Support Vector Machines (Science Press: Bei**g, PK, 2004).
D. Tan and D. Tan, Small-Sample Machine Learning Theory-Statistical Learning Theory, Journal of nan**g university of science and technology. Volume 25, Number 1, pp.108–112, (2001).
Y. Li, T. Fang, and E. Yu, Study of support vector machines for short-term power load forecasting, in Proc. of the CSEE. Volume 25, Number 1, pp.55–59, (2003).
G. Xu and Y. Shi, Application of genetic algorithm in association rule mining, Computer engineering. Volume 28, Number 7, pp.122–124, (2002).
C. Liu and Y. Zhang, The data mining method based on the model of gm (1, 1) and the gray relation, Changsha aeronaulcal vocational and technical college journal. Volume 5, Number 3, pp.60–62, (2005).
K. Li, C. Gao, and Y. Liu, Support vector machine based hierarchical clustering of spatial databases, Journal of Bei**g institute of technology. Volume 22. Number 4, pp.485–488, (2002).
Z. Zi, S. Zhao, and G. Wang, Study of relationship between fuzzy logic system and support vector machine, Computer engineering. Volume 30, Number 21, pp.117–119, (2004).
W. Chen and T. Xu, The improving and realizing of association rule mining apriori algorithm, Microcomputer development. Volume 15, Number 8, pp.155–157, (2005).
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© 2008 International Federation for Information Processing
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Sun, F., Yang, Y. (2008). A Research on Power Load Forecasting Model Based on Data Mining. In: Xu, L.D., Tjoa, A.M., Chaudhry, S.S. (eds) Research and Practical Issues of Enterprise Information Systems II. IFIP International Federation for Information Processing, vol 255. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-76312-5_65
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DOI: https://doi.org/10.1007/978-0-387-76312-5_65
Publisher Name: Springer, Boston, MA
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