Integrated AHP-BPNN Model for Wind Farm Investment Evaluation

Abstract:

The construction of wind farms grows quickly in China. It is necessary for stakeholders to estimate investment costs and to make good decisions about a wind power project by making a budget for the investment. This paper proposed an evaluation method by integrating the analytic hierarchy process (AHP) with back-propagation neural network (BPNN) to evaluate wind farm investment. In the AHP-BPNN model, the AHP method is used to determine the factors of wind farm investment. The factors with high importance are reserved while those with low importance are eliminated, which can decrease the number of inputs of the BPNN. The experiment results show that the integrated model is feasible and effective.

• Periodical: Applied Mechanics and Materials (Volumes 541-542)
• Pages: 966-971
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.541-542.966
• Online since: March 2014
• Authors: Xiang Feng Zhang*, Tian Yu Liu, Bin Jiao
• Keywords: Analytic Hierarchy Process (AHP), BP Neural Network (BPNN), Investment Evaluation, Wind Farm

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