Intelligent Simulation Method and its Application on Risk Analysis

Hua Zheng; Li Xie; Li Zi Zhang

doi:10.4028/www.scientific.net/AMR.217-218.1293

Paper Titles

Effect of Finishing on the Structure and Filtration Property of PPS Filter
p.1272

Temperature Effect on Microstructure and Mechanical Behavior of Ti-15V-3Cr-3Al-3Sn
p.1277

The Effect of Notch Processing on Fracture of High-Carbon Steel (C70S6)
p.1283

Developed Machine Learning Technology and its Application on Electric Power System
p.1289

Intelligent Simulation Method and its Application on Risk Analysis
p.1293

Localization of Partial Magnetization around Artificial Slits in Square Bars of Medium Carbon Low Alloy Steel JIS S45C
p.1297

Structural Strength Evolution of Soil under Dynamic Load
p.1303

The Higher Order Asymptotic Fields for Anti-Plane Oblique Crack with Physical Weak-Discontinuity
p.1309

The Higher Order Asymptotic Fields for Plane Interfacial Crack with Physical Weak-Discontinuity
p.1314

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 217-218Intelligent Simulation Method and its Application...

Intelligent Simulation Method and its Application on Risk Analysis

Abstract:

This article describes an intelligent simulation method for measuring price risk, which is still one of the important problems for various risk managements and need to be studied profoundly. To solve this problem, risk measured in terms of Value at Risk on electricity price is proposed by intelligent simulation. In this work, prices under various market scenarios are produced by intelligent model using fuzzy neural network (FNN). After that, the quantitative model for price risk analysis is built in the form of a function of the estimated probability distribution of price, where price VAR is determined from the distribution according to parameter set, i.e. confidence level. The proposed method is more realistic and effective than variance approach to provide the assessment of the potential loss of electricity price over some period of time.

You might also be interested in these eBooks

High Performance Structures and Materials Engineering

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 217-218)

Pages:

1293-1296

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.217-218.1293

Citation:

Cite this paper

Online since:

March 2011

Authors:

Hua Zheng, Li Xie, Li Zi Zhang

Keywords:

Fuzzy Theory, Neural Network (NN), Risk Analysis

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Claudia P. Rodriguez and George J. Anders: submitted to IEEE Trans. on Power Syst. (2004).

Google Scholar

[2] Nima Amjady: submitted to IEEE Trans. on Power Syst. (2006).

Google Scholar

[3] B.R. Szkuta, L.A. Sanabria and T.S. Dillon: submitted to IEEE Trans. on Power Syst. (1999).

Google Scholar

[4] Jia-yuan Zhu, Bo Ren, Heng-xi Zhang and Zhen-ting Deng: submitted to Proceeding of 2002 International Conference on Machine Learning and Cybernetics (2002).

DOI: 10.1109/icmlc.2002.1176775

Google Scholar

[5] Benini, M., Marracci, M., Pelacchi,P. and Venturini. A: submitted to Proceeding of 2002 IEEE Power Engineering Society Summer Meeting (2002).

DOI: 10.1109/pess.2002.1043596

Google Scholar

[6] Hamidreza Zareipour, Kankar Bhattacharya and Claudio A: submitted to Energy Policy (2007).

Google Scholar

[7] Fernando L. Alvarado, Rajesh Rajaraman: submitted to Proceeding of the 33rd Hawaii International Conference on System Sciences (2000).

Google Scholar