Omoroghomwan E. A.*, Igbinovia S. O. and Odiase F. O.
Department Of Electrical/Electronic Engineering, Faculty of Engineering, University of Benin, Benin City, Edo State, Nigeria
*Corresponding Author: efosaarnoldomoroghomwan@gmail.com
Vol. 6 No. 2 | October 2022 | Pages 418 – 427 | Cite this article
https://doi.org/10.36263/nijest.2022.02.0377
Abstract
ABSTRACT
The need for the estimation of the future state of electric power supply in the power system can no longer be avoided. This is due to the inevitable operational, maintenance, planning and expansion obligations of the power sector. In this work, the future trend of power supply by the 33kV feeders that supply power to the customers in the central part of Edo State, Nigeria was forecasted from 2020 to 2030 using Artificial Neural Network. The findings showed that there will be a 13.84% reduction in the power supplied by the utility provider by 2030 if the current trend was sustained. To avoid the adverse impact of such a negative performance by the power supplier, there is a need to increase system capacity by constructing mini grids and implementation of other contingency plans within the study area.
Keywords: Forecast, Nigeria Power Sector, ANN, Nigeria, NERC, Electric Power Load Forecasting
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Cite this article as:
Omoroghomwan E.A. Igbinovia S.O and Odiase F.O., 2022. The Artificial Neural Network Approach for Determining the Futuristic Capacity of Power Supply in the Central Parts of Edo State, Nigeria. Nigerian Journal of Environmental Sciences and Technology, 6(2), pp. 418-427. https://doi.org/10.36263/nijest.2022.02.0377
