Estimation and Management of Wind Energy in Ikorodu Lagos Lagoon, Using Weibull Parametric Measurement to Electricity Production in Nigeria


Authors : Abdulwahab Deji; Sherifah Oshioke Musa; Ikhlas Elfadil B. F. E. Salih; Nofisat Toyin Adewale

Volume/Issue : Volume 9 - 2024, Issue 7 - July

Google Scholar : https://tinyurl.com/wkrtsb4f

Scribd : https://tinyurl.com/2w7kk9xa

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUL933

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Abstract : This paper is an approach on the estimation and approximation management of wind energy production around the lagoon axis of Ikorodu, Lagos state, Nigeria. The article concentrates on the availability of renewable resource such as wind to generate electrical energy in the greater Ikorodu metropolis of Lagos state Nigeria. Here, probability distribution function is used to generate the wind data. In this paper, three distinct methods are presented; Data time series analysis, Weibull probability function, and theoretical comparison with analytical concept. This research uses two important parameters for analyzing wind data: shape factor “k” and scale factor “c” from Weibull distribution function. The theoretical uses mathematical equations of popular methods such as: (a) Moment method, (b) Empirical Formula, or statistical standard deviation, (c) Peak likelihood, (d) modified peak likelihood, (e) double modified peak likelihood (f) graphical method or smallest mean square, and (g) energy sequence factor. The results obtained are tested to optimize the value from the Weibull parameters by adopting five techniques: (i) root average square error methodology, X2, power of agreement , MAPE, and RRMSE. The results expatiated on the practical and theoretical techniques design to confront the outcome of wind energy harnessed per 1.5 km2. Here, a differential optimization technique is used to determine the precision report. This serve as the basis of error litmus check existing between the wind energy determined by theory of statistical and mathematical Weibull Parametric function and the practical time-series data analysis in LSTM. Again, the wind data (speed and energy/power) were measured and recorded between January 2020 to December 2023 in the Ijede-Ikorodu Lagoon area of Lagos State. The optimized value for the shape factor k and scale factor c parametric measurement and management for maximizing the output electrical energy are obtained by using a well robust Weibull distribution function techniques and by absolutely determining and selecting the best position and location for installing the wind/wave alternators/generator. These generators come with turbines as a single unit. The measurement of the yearly average wind speed and average wind power are 10.09 ms-1 and 10.1 KWm-2, concurrently.

Keywords : Wind Generator/Turbine, Mathematical Modelling Techniques, Simulation Techniques, Weibull Probability Distribution, Wind Speed Wind Energy/Power, Average Wind Speed, Shape Factor, Scale Factor, Artificial Neural Network, the Absolute Average Wind Speed, the Wind Speed Standard Deviation Model.

References :

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  2. Deji A., Khan S. and Mohammed H.H (May-June 2024). Mathematical Differential Analysis of Atlantic Ocean Wind to Electrical Energy Generation in Lekki Peninsular Lagos Nigeria. International Journal for Multidisciplinary Research (IJFMR). 6(3) Page 1-28. https://doi.org/10.36948/ ijfmr.2024.v06i03.21587
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  16. Deji A., Sheroz K., Musse M.A., (December 2023) “Analytical Modeling of Electrical Frequency and Voltage Signal from a Differential Inductive Transduction for Energy Measurement. International Journal for Multidisciplinary Research. Volume 5 Issue 6 Page 1-19. DOI: 10.36948/ijfmr. 2023.v05i06.8292
  17. Deji A., Sheroz K., Musse M.A., (December 2023) “Kinematic Motion Modelling from Differential Inductive Oscillation Sensing for a Sevomechanism and Electromechanical Devices and Applications.  International Journal for Multidisciplinary Research. Volume 5 Issue 6 Page 1-15. DOI: 10.36948/ijfmr.2023.v05i06.8291
  18. Deji A., Hanifah A.M., Sherifah O.M., (December 2023) “The Adoption of Information System Technology in Piloting the Current State of Health Institution in Tier Three Nations.”  International Journal for Multidisciplinary Research. Volume 5 Issue 6 Page 1-13. DOI: 10.36948/ijfmr.2023.v05i06.8367
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  20. D. Abdulwahab, S. Khan, J. Chebil and A. H. M. Z. Alam, "Symmetrical analysis and evaluation of Differential Resistive Sensor output with GSM/GPRS network," 2011 4th International Conference on Mechatronics (ICOM), Kuala Lumpur, Malaysia, 2011, pp. 1-6, doi: 10.1109/ICOM.2011.5937149.
  21. Khan S., A. Deji, A.H.M Zahirul, J. Chebil, M.M Shobani, A.M Noreha.  (Setember 2012) “Design of a Differential Sensor Circuit for Biomedical Implant Applications”. Australia. Journal of Basic and Applied. Sciences., 6(9): 1-9. 10.1002/9781118329481.ch1.
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  24. Abdulwahab, Deji. Development of Differential Sensor Interface for GSM Communication. Kulliyyah of Engineering, International Islamic University Malaysia, 2011.
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  26. Deji A., Sherifah OM., 2023. The Mediating Effect of Entrepreneur Cash Waqf Intension as means of Planned Behaviour for Business Growth. International Journal for Multidisciplinary Research. Volume 5, Issues 6, page 1-22
  27. Elfaki Ahamed, O.M.H., Musa O.S, Deji A., (2023). Factors Related to Financial Stress Among Muslim Students in Malaysia: A Case Study of Sudanese Students. Academy of Entrepreneurship Journal, 29(6), 1- 15.
  28. Deji A., Khan S., Mohammed H.H., (May-June 2024). Modelling and Simulation of 12MWP Independent Power Plant using Photovoltaic Energy Resources from Grid Network. International Journal for Multidisciplinary Research. 6(3) Page 1-15. https://doi.org/10.36948/ijfmr.2024.v06i03.21590
  29. Hanifah A.M. and Deji A. (May-June 2024). Self-Efficacy and Perceived ease of use as Factors to determine Medical Personnel Readiness to use an Information System Technology. . International Journal for Multidisciplinary Research. 6(3) Page 1-9.  https://doi.org/10.36948/ijfmr.2024.v06i03.21591
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This paper is an approach on the estimation and approximation management of wind energy production around the lagoon axis of Ikorodu, Lagos state, Nigeria. The article concentrates on the availability of renewable resource such as wind to generate electrical energy in the greater Ikorodu metropolis of Lagos state Nigeria. Here, probability distribution function is used to generate the wind data. In this paper, three distinct methods are presented; Data time series analysis, Weibull probability function, and theoretical comparison with analytical concept. This research uses two important parameters for analyzing wind data: shape factor “k” and scale factor “c” from Weibull distribution function. The theoretical uses mathematical equations of popular methods such as: (a) Moment method, (b) Empirical Formula, or statistical standard deviation, (c) Peak likelihood, (d) modified peak likelihood, (e) double modified peak likelihood (f) graphical method or smallest mean square, and (g) energy sequence factor. The results obtained are tested to optimize the value from the Weibull parameters by adopting five techniques: (i) root average square error methodology, X2, power of agreement , MAPE, and RRMSE. The results expatiated on the practical and theoretical techniques design to confront the outcome of wind energy harnessed per 1.5 km2. Here, a differential optimization technique is used to determine the precision report. This serve as the basis of error litmus check existing between the wind energy determined by theory of statistical and mathematical Weibull Parametric function and the practical time-series data analysis in LSTM. Again, the wind data (speed and energy/power) were measured and recorded between January 2020 to December 2023 in the Ijede-Ikorodu Lagoon area of Lagos State. The optimized value for the shape factor k and scale factor c parametric measurement and management for maximizing the output electrical energy are obtained by using a well robust Weibull distribution function techniques and by absolutely determining and selecting the best position and location for installing the wind/wave alternators/generator. These generators come with turbines as a single unit. The measurement of the yearly average wind speed and average wind power are 10.09 ms-1 and 10.1 KWm-2, concurrently.

Keywords : Wind Generator/Turbine, Mathematical Modelling Techniques, Simulation Techniques, Weibull Probability Distribution, Wind Speed Wind Energy/Power, Average Wind Speed, Shape Factor, Scale Factor, Artificial Neural Network, the Absolute Average Wind Speed, the Wind Speed Standard Deviation Model.

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