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A hybrid WT-FBPNN optimisation algorithm to identify the investment risk of wind power projects†

Published online by Cambridge University Press:  04 September 2014

ZHIBIN LIU  and
AISHENG REN
ZHIBIN LIU
Affiliation:
Department of Economics and Management, North China Electric Power University, Baoding 071003, China Institute of Agricultural Economics and Development, Chinese Academy of Agricultural Sciences, Beijing 100081, China Email: [email protected]
AISHENG REN
Affiliation:
Institute of Agricultural Economics and Development, Chinese Academy of Agricultural Sciences, Beijing 100081, China Email: [email protected]
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Abstract

Wind power projects face an uncertain external environment, they are complex projects in themselves and the capabilities of the designers, erectors and operators are limited. All this makes the identification of investment risks for wind power projects extremely complicated. In this paper, we propose a method for identifying the investment risk scientifically and accurately using a back propagation (BP) neural network. Specifically, we propose a hybrid wavelet transform fuzzy BP neural network (WT-FBPNN) optimisation model based on the construction of a risk evaluating index system. This improved model can not only exploit the time frequency localisation characteristic of wavelet transforms (WT), but also enhance the fit precision and algorithm convergence speed. The simulation results show that this model is reliable, and that this method of identifying the investment risk of wind power projects is feasible.

Type
Paper
Information
Mathematical Structures in Computer Science , Volume 24 , Special Issue 5: Mathematical Method in Intelligent Computing and Automation , October 2014 , e240503
Copyright
Copyright © Cambridge University Press 2014 

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Footnotes

†

This work was supported by: the Fundamental Research Funds for the Central Universities Soft Science Research Bases in Hebei Province.

References

Ding, W. and Li, X. (2006) Research on investment risk of electricity generation project in electricity market. Modern Electric Power 23 84–88.Google Scholar
Dong, Y., Wang, L. and Fan, P. (2000) Application of neuro-fuzzy network forecasting with wavelet theory. Journal of Henan Normal University (Natural Science) 28 84–87.Google Scholar
Fry, R. L. (1997) Observer-participant model of neural processing. IEEE Transactions on Neural Networks 16 918–928.Google Scholar
Guo, Q., Lu, X., Li, W. and Wang, Y. (2005) Study on hydrological forecasting based on wavelet-fuzzy neural networks. Journal of Tongji University (Natural Science) 33 130–133.Google Scholar
He, J., Jiang, G. and Tian, B. (2006) The credit evaluation system research based on improved BP neural network. Computer Engineering and Design 27 605–611.Google Scholar
Hill, T., Marquez, L., O'Connor, M. and Remus, W. (1994) Artificial neural network models for forecasting and decision making. International Journal of Forecasting 10 5–15.Google Scholar
Karhunen, J., Oja, E. and Wang, L. (1997) A class of neural networks for independent component analysis. IEEE Transactions on Neural Networks 18 486–504.Google Scholar
Li, J. and Sun, Y. (2001) Application of F-ANP in risk evaluation of offshore wind power projects. Journal of Liaoning Technical University (Natural Science) 30 96–99.Google Scholar
Li, X. and Qian, Y. (2005) Evaluation of project investment risk based on BP neural network. Journal of Hohai University Changzhou 55–57.Google Scholar
Li, X. and Xu, W. (2006) The model and application about the enterprise payoff ability evaluation based on the improved BP neural network. Industrial Technology Economy 92–96.Google Scholar
Malliaris, M. and Salchenberger, L. (1993) A neural network model for estimating option prices. Journal of Applied Intelligence 3 193–206.CrossRefGoogle Scholar
Miu, N. K., Chiang, H. D. and Yuan, B. (1998) Fast service restoration for large-scale distribution systems with priority customers and constraints. IEEE Transactions on Power Systems 13 (3)789–795.Google Scholar
Nakagawa, T., Hayashi, Y. and Iwamoto, S. (1991) Neural network application to state estimation computation. IEEE Transactions on Circuits and Systems 9 188–192.Google Scholar
Ren, F., Liu, J. and Niu, D. (2006) Comprehensive evaluation on sustainable development for electric power industry based on improved BP neural network model. Journal of North China Electric Power University 33 80–83.Google Scholar
Ridella, S. and Rovetta, S. (1997) Circular back propagation networks for classification. IEEE Transactions on Neural Networks 18 84–97.Google Scholar
Tai, N. and Hou, Z. (2004) New short-term load forecasting principle with the wavelet transform fuzzy neural network for the power systems. Proceedings of the CSEE 24 24–29.Google Scholar
Tank, D. W. and Hopfield, J. J. (1986) Simple neural optimization networks, an A/D converter, signal decision circuit, and linear programming circuit. IEEE Transactions on Circuits and Systems 13 533–541.Google Scholar
Wang, X. and Dai, L. (2008) Research of fuzzy wavelet neural network and its application. Industry and Mine Automation 43–46.Google Scholar
Wang, K. and Yang, L. (2007) Fuzzy synthetic evaluation for technological innovation capability of an enterprise based on wavelet neural network. Technology Economics 26 34–37.Google Scholar
Zhang, J. and Wang, Y. (2008) Traffic pattern identification of elevator group control system based on GA-BP fuzzy neural network. Micromachine 78–80.Google Scholar
Zhao, F. (2006) The project investment risk evaluation based on the BP neural network. Construction Economy 62–64.Google Scholar
Zhao, D., Liu, J. and Ma, J. (2009) Risk assessment of information security using fuzzy wavelet neural network. Journal Huazhong University of Science and Technology (Natural Science Edition) 37 43–45.Google Scholar
Zhao, Q., Wang, B. and Liang, Z. (2005) The application research and improvement of the enterprise comprehensive performance evaluation based on BP neural network. Industrial Engineering Journal 102–107.Google Scholar