Abstract
Energy contributes significantly to national socio-economic and political development, and essentially, there is a restriction on socio-economic activities, development, and quality of life when the energy supply is limited. Hydropower and solar PV are the key contributors to sustainable energy supply among the renewable energy sources in the world. This paper has demonstrated an application of an intelligent algorithm for the modelling and simulation of energy generation for the Shiroro hydroelectric power plant alongside the power output of solar PV in Nigeria. MATLAB software was used to perform the programming by developing two forecasting models. Thus, hydrological data for Shiroro hydropower station spanning from 1990 to 2023 was used for the analysis alongside experimental data for the photovoltaic systems for Minna, Niger State, Nigeria (as a case study). Artificial neural network (ANN) models were developed to mimic and simulate the energy generation outputs for the two scenarios. A reverse energy generation forecast was carried out to assess the complementarity between the two given scenarios concerning power production viability. Hence, based on the lower value of RMSE 0.8% and high correlation value of 1, the artificial neural network (ANN) model for the solar PV generation outperformed the model of Hydro power plant with a reasonable accuracy, which ascertained that the model was reliable and could be used for prediction at 95% confidence level. It is expected that the outcomes from the prediction models in this study will have high economic value in enhancing effective management and planning strategies of the Shiroro hydropower plant station in Nigeria.
KEYWORDS
Artificial Neural Network (ANN), Hydroelectric, Solar Photovoltaic, Renewable Energy, Shiroro Power Plant, Nigeria; forecast.
Mohammed Ghali Aminu1*, Juan José Garcia Pabon2
1,2Institute of Mechanical Engineering, Universidade Federal de Itajubá- 37500-903, Brazil