1 Department of Mechanical Engineering, School of Engineering Technology, Federal Polytechnic Nekede, Owerri, Imo State, Nigeria.
2 Department of Agricultural and Bioenvironmental Engineering, School of Engineering Technology, Federal Polytechnic Nekede, Owerri, Imo State, Nigeria.
Research Article
World Journal of Advanced Research and Reviews, 2023, 17(02), 844-852
Article DOI: 10.30574/wjarr.2023.17.2.0335
Received on 17 January 2023; revised on 25 February 2023; accepted on 28 February 2023
The outcome of the study, evaluation of best value of wind speed for maximum wind energy output in Nigeria using neural network was successfully achieved. Wind speed for various months and elevation data gotten from NASA were prepared in excel and imported into neural network toolbox in MATLAB for training and analysis using levenberg maquardt and scaled conjugate gradient algorithms to determine the best performance value. Furthermore, training of the data using levenberg marquardt algorithm at 70% training data, 15% test data and 15% validation data respectively revealed that the best performance level was 1.9046 m/s at 7 epochs and this matches elevation of 7. 24meters. In addition, training of the same data using scaled conjugate gradient algorithm with the stated conditions above, revealed that the best performance level was 9.0376 m/s at 182 epochs and this matches elevation of 34.35meters. Also, training fit coefficients were found to be 0.99984 and 0.99804 respectively which indicated that there is a close and positive relationship between wind speed and wind turbine hub height. Results revealed that the best value of wind speed and elevation for maximum wind energy output in Nigeria ranges from 1.9046 m/s to 9.0376 m/s and 7.2 meters to 34.35 meters respectively. The researchers made the following recommendations: Wind turbine tower and blades should be designed to have wind impact speed capacity beyond the estimated values; further research can also be done in future to establish a more accurate mathematical model between wind speed and wind energy output with other advanced program for generalization, etc.
Wind speed; Wind energy; Elevation; MATLAB; Algorithm; Training data
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CHIMEZIE AGBAFOR NKWOR, EFOSA OBASEKI, ONYINYECHI CHIDIMMA UGBAJA and TENNISON IFECHUKWU EWURUM. Evaluation of best value of wind speed for maximum wind energy output in Nigeria using neural network. World Journal of Advanced Research and Reviews, 2023, 17(2), 844-852. Article DOI: https://doi.org/10.30574/wjarr.2023.17.2.0335