Short-Term Load Combination Forecast Based on Rough Set and Support Vector Machine

Yuan Sheng Huang; Li Ming Yuan

doi:10.4028/www.scientific.net/AMR.201-203.2481

Paper Titles

Analysis on Thermal-Mechanics Coupling of Milling Insert Based on Principle of Cellular Automation
p.2460

Building Energy Consumption Prediction of Housing Industry in China Based on Hybrid Models
p.2466

Fuzzy Synthetic Evaluation of Power Quality Based on Combination Weighting Method
p.2470

Thermal Fatigue and Fracture Mechanics Analysis of Aluminium Alloy
p.2476

Short-Term Load Combination Forecast Based on Rough Set and Support Vector Machine
p.2481

The Digital Ink-Jet Printing Properties of Cashmere Treated by Low-Temperature Plasma
p.2488

Research on the Ink Penetration Depth into Offset Paper
p.2492

Application of Neural Network and Particle Swarm Optimization Algorithm in Slope Runoff and Sediment Yield Calculation
p.2496

Research on the Rice Leaf Morphological Formation and its Visualization
p.2504

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 201-203Short-Term Load Combination Forecast Based on...

Short-Term Load Combination Forecast Based on Rough Set and Support Vector Machine

Abstract:

A short-term load combination forecasting model based on rough set and support vector machine was proposed in this paper, firstly build decision table based on historical data, and data mining the data through attribute reduction algorithms, and then use the results of prediction methods to be the input of the SVM, practical load value to be the output, training according to the algorithm of the SVM. the result shows that the SVM combination forecasting model has a better balance fitting and extrapolation,and its prediction accuracy is better than single prediction model.

You might also be interested in these eBooks

Advanced Manufacturing Systems, ICMSE 2011

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 201-203)

Pages:

2481-2487

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.201-203.2481

Citation:

Cite this paper

Online since:

February 2011

Authors:

Yuan Sheng Huang, Li Ming Yuan

Keywords:

Combination Forecasting, Power Load, Rough Set (RS), Support Vector Machine (SVM)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] D.X. Niu, S.H. Cao and L. Zhao: Load forecasting technology and its applications (China Electric Power Press, China 1998).

Google Scholar

[2] M.H. Wu and J.W. Yang: Guangdong Electric Power in China Vol. 17(1) 2004, p.17.

Google Scholar

[3] Bates J M and Granger C W J: Journal of Operational Research Quarterly Vol. 20 (1969), pp.451-468.

Google Scholar

[4] K.Y. Chen and C.H. Wang: Tourism Manage Vol. 2(1) 2007, pp.15-226.

Google Scholar

[5] Q.B. Zhang, H.Z. Cheng, Q.S. Liu and etc: Power System Technology Vol. 30(8) 2006, pp.56-60.

Google Scholar

[6] J.L. Deng: Grey system theory tutorial (Wuhan: Huazhong University Press, China 1990).

Google Scholar

[7] D.M. Shi, L.C. Li and J.W. Song: Power System Technology Vol. 25(12) 2001, p.14~17.

Google Scholar

[8] K. Guo: Multivariate statistical methods and design (Chengdu: University of Electronic Science and Technology University Press, China 2003).

Google Scholar

[9] Y.X. Liu, H. Hao and Z.Q. Wu: Computer Development Vol. 3(1999), p.3.

Google Scholar

[10] X.W. Tang: Forecasting Vol. 10(4)1991, pp.35-40(in Chinese).

Google Scholar

[11] Vapnik V N.: Statistical learning theory(New York: John Wiley and Sons, Inc, USA1998).

Google Scholar

[12] X.X. Zhang, Q. Zhou and H.J. Ren: Automation of Electric Power Vol. 29(13)2005, P. 40-44.

Google Scholar

[13] L. Y Shen and X. Yu: East China Electric Power Vol. 37(11)2009, P. 1844-1847.

Google Scholar