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Aririguzo, J. C.
Department of Mechanical, Engineering Michael Okpara University of Agriculture Umudike Umuahia, Abia State, Nigeria
Kalu, B. O.
Department of Mechanical, Engineering Michael Okpara University of Agriculture Umudike Umuahia, Abia State, Nigeria
ABSTRACT
Artificial
neural network (ANN) modelling of wind energy profile of south eastern Nigeria
for power generation was studied in this study. Five locations (Nsukka,
Umudike, Owerri, Onitsha and Enugu) were considered whereas two locations
(Nsukka and Umudike) were selected for the ANN modelling. The study investigated the most effective
parameters in estimating generated power from observed wind speed and other
climatological factors in the South eastern region of Nigeria as well as a
comparative analysis between the results from ANN model to linear-model fits.
The data used for this study was sourced from a wind anemometer reading at 2m
hub height for Nsukka, Enugu State and Umudike in Abia State, whereas the data
for Enugu, Owerri and Onitsha were extracted from previous works of Oyedepo et
al., (2015) all in South Eastern Nigeria. Results showed that the minimum wind
speed observed at the various locations were 2.605m/s for Nsukka, and 4.518m/s
for Umudike while the maximum wind speeds were 5.501m/s for Nsukka, and
9.807m/s for Umudike respectively at the extrapolated height of 30m. The
Weibull shape parameter k is between 13.12 to 32.02 for Nsukka and 13.4 to
28.68 for Umudike, while the scale parameter c is between 2.692m/s to 5.987m/s
for Nsukka and 4.682m/s to 9.997m/s for Umudike. The annual mean power density
for Nsukka and Umudike are 35.128 kWh/m2 and 147.39kWh/m2
respectively. The result also showed that model 3 (a combination of wind speed,
pressure and temperature) returned the least error in prediction for Nsukka
while model 2 (a combination of wind speed and pressure.) returned the least
prediction error for Umudike. The coefficient of determination (R2)
of the optimal ANN model is 0.999 for both locations under study. The study
thus concludes that the optimum ANN architecture for predicting the wind power
available in the south east region varies depending on the location and
climatological differences within the said location, hence a common set of
predictors cannot be adopted for all locations. However, the study confirms
that ANN models has a low error rate in predicting available wind power, hence
should be employed in wind power prediction.
Keywords: Wind, Artificial Neural Network, Modelling
https://doi.org/10.33922/j.ujet_v7i1_15
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Published
Tuesday, June 01, 2021
Issue
Vol. 7 No. 1, June 2021
Article Section
GENERAL
The contents of the articles are the sole opinion of the author(s) and not of UJET.
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