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

Sedzro, Kwami Senam A.; Salami, Adekunlé Akim; Agbessi, Pierre Akuété; Kodjo, Mawugno Koffi

doi:10.3390/en15228654

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

Journal Article · Fri Nov 18 00:00:00 EST 2022 · Energies

DOI:https://doi.org/10.3390/en15228654· OSTI ID:1906319

^[1]; Salami, Adekunlé Akim ^[2]; Agbessi, Pierre Akuété ^[2]; Kodjo, Mawugno Koffi ^[2]

National Renewable Energy Lab. (NREL), Golden, CO (United States)
University of Lome (Togo)

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.

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Research Organization:: National Renewable Energy Lab. (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: AC36-08GO28308

OSTI ID:: 1906319

Report Number(s):: NREL/JA-5D00-75425; MainId:6036; UUID:2111a73e-7b05-ea11-9c2a-ac162d87dfe5; MainAdminID:68277

Journal Information:: Energies, Vol. 15, Issue 22; ISSN 1996-1073

Publisher:: MDPICopyright Statement

Country of Publication:: United States

Language:: English

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