IIARD International Institute of Academic Research and Development

International Journal of Engineering and Modern Technology (IJEMT )

E-ISSN 2504-8848
P-ISSN 2695-2149
VOL. 11 NO. 5 2025
DOI: 10.56201/ijemt.vol.11.no5.2025.pg38.50

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Evaluating the Performance of Artificial Neural Network Models for Solar PV Output Forecasting in Nigeria’s Grid-Connected Systems

Dalyop Stephen Choji, Anibaba Olawale Oluwatosin, Adegboye Luqman Adetola, Wasiu Olalekan Idowu, Bakare Akeem Adegoke

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Abstract

The growing reliance on solar photovoltaic (PV) energy as a cleaner alternative to fossil fuels has amplified the need for accurate forecasting mechanisms, especially in developing countries like Nigeria where grid instability and load mismatches are common. This study evaluates the performance of Artificial Neural Network (ANN) models for forecasting solar PV output in grid- connected systems within the Nigerian energy context. Using historical meteorological and load demand data from 2020 to 2025, an ANN model was developed, trained, and simulated using MATLAB R2022a. The model incorporated key variables such as solar irradiance, ambient temperature, and time stamps to predict solar power generation. Simulation results were compared with actual output to assess accuracy using metrics such as Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and the coefficient of determination (R²). The ANN model achieved a MAPE of 6.83%, an RMSE of 12.47 kW, and an R² value of 0.95—demonstrating high predictive accuracy and adaptability to nonlinear solar data variations. The study further presents graphical analyses, including predicted vs actual output curves, error distribution histograms, and regression scatter plots, which confirm the model’s robustness. These results validate the application of ANN for solar PV forecasting and emphasize its potential to enhance energy planning, grid stability, and real-time energy dispatch in Nigeria. Furthermore, the findings advocate for the integration of AI-based forecasting tools into Nigeria’s energy management systems to optimize renewable energy use. This paper contributes to the body of knowledge on intelligent forecasting for smart grid applications and provides a replicable model for similar developing regions.

keywords:

Artificial Neural Network (ANN); Solar Photovoltaic (PV); Forecasting; Grid Integration; Renewable Energy; Nigeria; Smart Grid; Solar Irradiance; MATLAB Simulation; Forecast Accuracy.

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