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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, ChinaInstitute of Agricultural Economics and Development, Chinese Academy of Agricultural Sciences, Beijing 100081, China Email: liuzhibin771112@126.com
AISHENG REN
Affiliation:
Institute of Agricultural Economics and Development, Chinese Academy of Agricultural Sciences, Beijing 100081, China Email: renaisheng@caas.cn
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Abstract

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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.

Footnotes

†

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

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