Wind Power Economic Feasibility under Uncertainty and the Application of ANN in Sensitivity Analysis

Paulo Rotella Junior, Eugenio Fischetti, Victor G. Araújo, Rogério S. Peruchi, Giancarlo Aquila, Luiz Célio S. Rocha and Liviam S. Lacerda
Additional contact information
Eugenio Fischetti: Department of Production Engineering, Federal University of Paraíba, João Pessoa 58051-970, Brazil
Victor G. Araújo: Department of Production Engineering, Federal University of Paraíba, João Pessoa 58051-970, Brazil
Rogério S. Peruchi: Department of Production Engineering, Federal University of Paraíba, João Pessoa 58051-970, Brazil
Giancarlo Aquila: Institute of Industrial Engineering and Management, Federal University of Itajubá, Itajubá 37500-000, Brazil
Luiz Célio S. Rocha: Department of Management, Federal Institute of Education, Science and Technology Northern of Minas Gerais, Almenara 39900-000, Brazil
Liviam S. Lacerda: Department of Production Engineering, Federal University of Paraíba, João Pessoa 58051-970, Brazil

Energies, 2019, vol. 12, issue 12, 1-10

Abstract: Wind power has grown popular in past recent years due to environmental issues and the search for alternative energy sources. Thus, the viability for wind power generation projects must be studied in order to attend to the environmental concerns and still be attractive and profitable. Therefore, this article aims to perform a sensitive analysis in order to identify the variables that influence most in the viability of a wind power investment for small size companies in the Brazilian northeast. For this, a stochastic analysis of viability through Monte Carlo Simulation (MCS) will be made and afterwards, Artificial Neural Networks (ANN) models will be applied for the most relevant variables identification. Through the sensitivity, it appears that the most relevant factors in the analysis are the speed of wind, energy tariff and the investment amount. Thus, the viability of the investment is straightly tied to the region where the wind turbine is installed, and the government incentives may allow decreasing in the investment amount for wind power. Based on this, incentives programs for the production of clean energy include cheaper purchase of wind turbines, lower taxing and financing rates, can make wind power more profitable and attractive.

Keywords: economic feasibility; net present value; artificial neural networks; wind power; sensitivity analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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