A Novel Hybrid Machine Learning-IoT Framework for Optimizing Solar Energy Efficiency in Arid Regions: A Case Study of Sub-Saharan Africa


Authors : Benjamin Nyabera Kerama

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/3vyda6ha

Scribd : https://tinyurl.com/mwfcayc2

DOI : https://doi.org/10.38124/ijisrt/25apr2310

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Abstract : The efficient harnessing of solar energy in arid regions is critical for closing the electricity access gap in Sub- Saharan Africa, yet installations routinely underperform due to soiling, extreme temperatures, and lack of adaptive control. We introduce a novel hybrid Machine Learning–IoT framework that unifies real-time environmental and electrical sensing, deep-learning prediction of power output and fault risk, and reinforcement-learning–based adjustment of panel tilt and maintenance scheduling. The framework is cast as a constrained optimization problem balancing energy yield, maintenance cost, and reliability, and employs a multi-stage ML pipeline—combining LSTM and XGBoost for generation forecasting and a CNN-based classifier for anomaly detection—together with a Deep Q-Network controller. We validate our approach via a year-long simulation of a 100 kW off-grid PV array in Northern Kenya. Compared to a fixed- tilt, quarterly-cleaning baseline, our method achieves a 20.8 % increase in annual energy output and a 35.5 % reduction in downtime, while respecting practical bounds on tilt angles and service frequency and maintaining fault-risk below a prescribed threshold. These results demonstrate that end-to-end integration of IoT sensing, machine learning, and optimal control can substantially enhance the performance, cost-effectiveness, and reliability of solar deployments in harsh, resource-constrained environments.

Keywords : Machine Learning, Internet of Things (IoT), Solar Energy, Arid Regions, Sub-Saharan Africa, Photovoltaic Systems, Energy Optimization, Predictive Maintenance, Deep Learning.

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The efficient harnessing of solar energy in arid regions is critical for closing the electricity access gap in Sub- Saharan Africa, yet installations routinely underperform due to soiling, extreme temperatures, and lack of adaptive control. We introduce a novel hybrid Machine Learning–IoT framework that unifies real-time environmental and electrical sensing, deep-learning prediction of power output and fault risk, and reinforcement-learning–based adjustment of panel tilt and maintenance scheduling. The framework is cast as a constrained optimization problem balancing energy yield, maintenance cost, and reliability, and employs a multi-stage ML pipeline—combining LSTM and XGBoost for generation forecasting and a CNN-based classifier for anomaly detection—together with a Deep Q-Network controller. We validate our approach via a year-long simulation of a 100 kW off-grid PV array in Northern Kenya. Compared to a fixed- tilt, quarterly-cleaning baseline, our method achieves a 20.8 % increase in annual energy output and a 35.5 % reduction in downtime, while respecting practical bounds on tilt angles and service frequency and maintaining fault-risk below a prescribed threshold. These results demonstrate that end-to-end integration of IoT sensing, machine learning, and optimal control can substantially enhance the performance, cost-effectiveness, and reliability of solar deployments in harsh, resource-constrained environments.

Keywords : Machine Learning, Internet of Things (IoT), Solar Energy, Arid Regions, Sub-Saharan Africa, Photovoltaic Systems, Energy Optimization, Predictive Maintenance, Deep Learning.

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