Author

N. A. Akinrinade

View on Semantic Scholar

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Apr 2026

Modelling and Simulation of Solar Generator Battery Hybrid Systems to Improve Energy Reliability

Hybrid energy systems are essential for addressing unreliable power supply in off-grid healthcare facilities. In Nigeria, frequent grid outages force reliance on costly, polluting diesel generators, compromising healthcare delivery. This study simulated a solar-generator-battery hybrid system to improve energy reliability for medical facilities. The study employed MATLAB/Simulink to model a system integrating solar PV, lithium-ion batteries, and a diesel generator. Component sizing was based on load assessments (kWh/day) and solar irradiance data (kWh/m²/day). The four different scenarios tested in the study were baseline operation, low irradiance, increased demand, and prolonged outages. Four test scenarios showed the honesty into reliability of the hybrid energy system and its versatility. In base scenarios, the system had 95% availability, and the solar PV replaced 72% of the required energy resources, whereas the runtime of the generator was equal to only 2.3 hours/day. Under the low solar irradiance periods, solar contribution was reduced to 38%, which led to an increase in generator run time to 5.1 hours/day. However, the system was exposed to an additional 20% increase in the energy demand; it was steady, and the generator hours slightly increased to 4.7 hours per day. During the low irradiance condition simulated in the seven consecutive days with poor sunlight, the generator gave 68% of the total energy, and the state of charge threshold set to prevent deep discharge, which can damage the battery and reduce its lifespan. The hybrid system ensured continuous power, reduced diesel dependence by up to 60%, and offered resilient support for essential healthcare services in developing regions.

N. A. Akinrinade, E. T. Oladele, M. O. Ajayi et al. · International Journal of Ele... · 0 citations

Open access Aug 2026

Deep learning-based time-series solar power prediction for automatic multisource energy generation systems

The increasing need for energy security and the development of clean energy sources to mitigate the impact of climate change necessitate the development of multisource energy generation systems comprising solar panels, generators, and grids. Four hybrid deep learning models were developed to predict the solar power generation (SPG) from historical solar panel data. These include a time convolutional network with enhanced multilayer perceptron (TCN-EMLP), a convolutional neural network with long short-term memory (CNN-LSTM), an LSTM with AutoEncoder (LSTM-AE), and a transformer model. These models were deployed for the predictions of solar power generation (SPG). All models were applied to a dataset containing 378 observations of solar energy data, allowing for a direct comparison between the hybrid deep learning methods employed. The input variables used include battery level (BL), ambient temperature (Temp), solar Irradiance (Ir). The results indicated that LSTM-AE showed superior performance relative to TCN-EMLP, LSTM-AE, and the transformer model with a strong R 2 of 0.8359, a summary R 2 of 0.8059, a root mean square error (RMSE) of 7.4304 W, and a mean absolute error (MAE) of 5.9322 W. CNN-LSTM achieved a significantly high performance comparable to TCN-EMLP and the transformer model. The utilization of deep learning to build intelligent automated multisource energy systems could lead to enhanced prediction accuracy, better performance, and higher sustainability by lessening reliance on non-renewable backup systems.

N. A. Akinrinade, A. Onawumi, E. Sangotayo et al. · Frontiers of Mechanical Engi... · 0 citations