An Investigation of IoT-Based Smart Solar Panel Efficiency Monitoring in University Campuses in Birnin Kebbi LGA, Kebbi State
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- NGN 5000
Background of the study:
University campuses increasingly rely on renewable energy sources such as solar panels to reduce operational costs and promote sustainability. However, monitoring the efficiency and performance of solar panels can be challenging with traditional methods, which are often manual and periodic. In Birnin Kebbi LGA, universities are adopting solar energy, yet inefficiencies and performance issues remain largely undetected due to the lack of continuous monitoring. IoT-based smart solar panel efficiency monitoring systems offer a solution by integrating sensors that track parameters such as voltage, current, and temperature, providing real-time data on panel performance (Aminu, 2023). These systems transmit data wirelessly to centralized platforms where analytics are applied to identify inefficiencies, predict maintenance needs, and optimize energy output (Ibrahim, 2024). Automated monitoring reduces the risk of manual error, enables timely intervention, and enhances the longevity of solar assets. Furthermore, the system’s predictive maintenance capabilities can help reduce downtime and maximize energy production, thereby supporting sustainable energy goals on campus (Udo, 2025). This study aims to investigate the design and implementation of an IoT-based monitoring system, ensuring that university campuses can optimize solar panel efficiency and achieve better energy management.
Statement of the problem:
University campuses in Birnin Kebbi LGA face challenges in maintaining the efficiency of their solar panel installations due to the limitations of traditional, manual monitoring methods. Inadequate tracking of performance metrics leads to undetected inefficiencies, reduced energy output, and increased maintenance costs (Aminu, 2023). Manual inspections often fail to capture real-time fluctuations in solar panel performance, resulting in delayed maintenance and prolonged system downtime. The absence of a continuous, automated monitoring system hinders the early detection of issues such as dirt accumulation, shading, or component degradation, compromising overall energy production (Ibrahim, 2024). Financial and technical constraints further impede the effective management of solar installations, leaving campuses with suboptimal renewable energy performance. Without an IoT-based solution to provide real-time insights and predictive maintenance alerts, the universities struggle to optimize energy usage and realize the full benefits of their solar investments (Udo, 2025).
Objectives of the study:
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To design an IoT-based system for real-time monitoring of solar panel efficiency.
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To evaluate the system’s effectiveness in detecting performance issues and optimizing energy output.
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To propose recommendations for integrating the system with existing campus energy management practices.
Research questions:
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How effective is the IoT-based system in monitoring solar panel performance in real time?
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What improvements in energy efficiency are observed following system implementation?
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How can the system be integrated with current energy management frameworks to enhance solar performance?
Significance of the study:
This study is significant as it addresses the challenges of maintaining solar panel efficiency on university campuses. By implementing an IoT-based monitoring system, institutions can optimize energy production, reduce maintenance costs, and support sustainability initiatives, ultimately improving campus energy management and promoting renewable energy adoption.
Scope and limitations of the study:
This study is limited to the investigation of IoT-based smart solar panel efficiency monitoring systems in university campuses in Birnin Kebbi LGA, Kebbi State. It does not extend to other renewable energy systems or different regions.
Definitions of terms:
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IoT (Internet of Things): A network of interconnected devices that provide continuous data exchange.
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Solar Panel Efficiency Monitoring: The process of tracking and analyzing the performance of solar panels in real time.
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Predictive Maintenance: The use of data analytics to forecast equipment failures and schedule timely repairs.
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