Comparative Study of Wind Energy Potential Estimation Methods for Wind Sites in Togo and Benin (West Sub-Saharan Africa)

Kwami Senam A. Sedzro, Adekunlé Akim Salami (), Pierre Akuété Agbessi and Mawugno Koffi Kodjo
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Kwami Senam A. Sedzro: Transmission & Distribution Interactions, Grid Planning and Analysis Center, National Renewable Energy Laboratory, 15013 Denver West Pkwy, Golden, CO 80401, USA
Adekunlé Akim Salami: Department of Electrical Engineering, Ecole Nationale Supérieure d’Ingénieurs, Centre d’Excellence Régionale pour la Maîtrise de l’Electricité (CERME), University of Lomé, Lomé P.O. Box 1515, Togo
Pierre Akuété Agbessi: Department of Electrical Engineering, Ecole Nationale Supérieure d’Ingénieurs, Centre d’Excellence Régionale pour la Maîtrise de l’Electricité (CERME), University of Lomé, Lomé P.O. Box 1515, Togo
Mawugno Koffi Kodjo: Department of Electrical Engineering, Ecole Nationale Supérieure d’Ingénieurs, Centre d’Excellence Régionale pour la Maîtrise de l’Electricité (CERME), University of Lomé, Lomé P.O. Box 1515, Togo

Energies, 2022, vol. 15, issue 22, 1-28

Abstract: The characterization of wind speed distribution and the optimal assessment of wind energy potential are critical factors in selecting a suitable site for wind power plants (WPP). The Weibull distribution law has been used extensively to analyze the wind characteristics of candidate WPP sites, and to estimate the available and deliverable energy. This paper presents a comparative study of five wind energy resource assessment methods as they applied to the context of wind sites in West Sub-Saharan Africa. We investigated three numerical approaches, namely, the adaptive neuro-fuzzy inference system (ANFIS), the multilayer perceptron method (MLP), and support vector regression (SVR), to derive the distribution law of wind speeds and to optimally quantify the corresponding wind energy potential. Next, we compared these three approaches to two well-known Weibull distribution law-based methods: the empirical method of Justus (EMJ) and the maximum likelihood method (MLM). Case study results indicated that the neural network-based methods, ANFIS and MLP, yielded the most accurate distribution fits and wind energy potential estimates, and consequently, are the most recommended methods for the wind sites in Togo and Benin. The orders of magnitude of the root mean squared error (RMSE) in estimating the recoverable energy using ANFIS were, respectively, 10-4 and 10-5 for Lomé and Cotonou, while MLP achieved an RMSE order of magnitude of 10-3 for both sites.

Keywords: wind energy potential; ANFIS; multilayer perceptron; support vector regression; probability density function; wind energy in Sub-Saharan Africa (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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