| Abstract: |
Internet of Things (IoT) technology has been advancing rapidly and offers transformative opportunities for improving the performance and efficiency of solar photovoltaic (PV) energy systems. This work reports an empirical study whereby we design, implement, and quantitatively evaluate an IoT-based smart control framework that, when embedded within solar energy systems, ensures the efficiency of the system in harvest and use in an environmental condition of variable change. This work proposes a framework that integrates an eight-class precision IoT sensor network, a real-time edge computing node, adaptive Maximum Power Point Tracking (MPPT) algorithms, and a cloud-based analytics dashboard. To achieve the objective of the study, a dataset of 360 daily observations from a twelve-month field experiment on a 5 kWp rooftop solar installation instrumented with a total of 24 IoT sensor nodes under various meteorological and load conditions was generated. The overall system efficiency showed a significant improvement, from 74.2% to 88.6% for a net gain of 14.4 percentage points(p < 0.001). Annual energy yield improved from 6,772.8 kWh to 8,042.6 kWh, an increase of 18.8%. Accuracy rate of the method at fault detection was 61.3% 94.8%. Multiple regression analysis showed that solar irradiance (β = 0.682), MPPT activation (β = 0.436), and panel temperature (β = -0.318) were the most important predictors of system output (R-squared = 0.924). Differences between four IoT control modes were statistically significant as measured by one-way ANOVA (F = 148.6, p < 0.001). A comparative benchmarking based on six previous studies verified that the proposed framework outperforms all other state-of-the-art approaches in overall efficiency gain with the improvement between 1.3 and 6.2 percentage points. Results provide evidence that IoT-based smart control is a technically matured and scalable solution for solar energy management of the future. |
