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

Kwami Senam Sedzro, Adekunle Salami, Pierre Agbessi, Mawugno Kodjo

Research output: Contribution to journalArticlepeer-review

6 Scopus Citations

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.

Original languageAmerican English
Article number8654
Number of pages28
JournalEnergies
Volume15
Issue number22
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2022 by the authors.

NREL Publication Number

  • NREL/JA-5D00-75425

Keywords

  • ANFIS
  • multilayer perceptron
  • probability density function
  • support vector regression
  • wind energy in Sub-Saharan Africa
  • wind energy potential

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