Artificial Neural Network Modelling Of Wind Energy Profile Of Nsukka And Umudike In Eastern Nigeria

BRIGHT OKEKE | 1 page (21469 words) | Theses
Mechanical Engineering | Co Authors: KALU

ABSTRACT

Artificial neural network (ANN) modelling of wind energy profile of south eastern Nigeria for power generation was studied in this project. 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 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 ( ) 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 has a low error rate in predicting available wind power. Thus, the study recommends that ANN should be employed in wind power prediction in South-Eastern Nigeria. The study thus developed an ANN-Regression equation for the predicting of available wind power density for Nsukka and Umudike.

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APA

BRIGHT, O (2022). Artificial Neural Network Modelling Of Wind Energy Profile Of Nsukka And Umudike In Eastern Nigeria. Repository.mouau.edu.ng: Retrieved Feb 07, 2023, from https://repository.mouau.edu.ng/work/view/artificial-neural-network-modelling-of-wind-energy-profile-of-nsukka-and-umudike-in-eastern-nigeria-7-2

MLA 8th

OKEKE, BRIGHT. "Artificial Neural Network Modelling Of Wind Energy Profile Of Nsukka And Umudike In Eastern Nigeria" Repository.mouau.edu.ng. Repository.mouau.edu.ng, 05 Oct. 2022, https://repository.mouau.edu.ng/work/view/artificial-neural-network-modelling-of-wind-energy-profile-of-nsukka-and-umudike-in-eastern-nigeria-7-2. Accessed 07 Feb. 2023.

MLA7

OKEKE, BRIGHT. "Artificial Neural Network Modelling Of Wind Energy Profile Of Nsukka And Umudike In Eastern Nigeria". Repository.mouau.edu.ng, Repository.mouau.edu.ng, 05 Oct. 2022. Web. 07 Feb. 2023. < https://repository.mouau.edu.ng/work/view/artificial-neural-network-modelling-of-wind-energy-profile-of-nsukka-and-umudike-in-eastern-nigeria-7-2 >.

Chicago

OKEKE, BRIGHT. "Artificial Neural Network Modelling Of Wind Energy Profile Of Nsukka And Umudike In Eastern Nigeria" Repository.mouau.edu.ng (2022). Accessed 07 Feb. 2023. https://repository.mouau.edu.ng/work/view/artificial-neural-network-modelling-of-wind-energy-profile-of-nsukka-and-umudike-in-eastern-nigeria-7-2

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