Optimization of Solar Energy Harvesting Using Iot-Based Monitoring Systems
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Abstract
The growing demand for clean and sustainable energy has accelerated the development of efficient solar energy harvesting technologies. However, the performance of photovoltaic (PV) systems is often influenced by environmental factors such as solar irradiance, temperature, dust accumulation, and system inefficiencies. This study focuses on the optimization of solar energy harvesting using Internet of Things (IoT)-based monitoring systems. The proposed system integrates smart sensors, microcontrollers, and wireless communication modules to continuously monitor key parameters including solar irradiance, panel temperature, voltage, current, and power output. The collected real-time data are transmitted to a cloud-based platform for analysis, visualization, and remote monitoring. By employing data analytics and automated control strategies, the system enables timely detection of performance losses, fault conditions, and environmental impacts affecting solar panels. The implementation of this smart monitoring approach not only enhances the operational performance of solar photovoltaic installations but also reduces maintenance costs and energy losses. The findings highlight that integrating IoT technologies with solar energy systems can play a crucial role in maximizing energy generation and supporting the transition toward sustainable and intelligent energy infrastructures.
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The system was evaluated under a limited range of climatic conditions, which may affect performance in extreme environments such as heavy snow, sandstorms, or high humidity.
The study focused on small to medium-scale solar installations; large utility-scale deployment may introduce additional technical and data management challenges.
The IoT monitoring system relies on stable internet and cloud infrastructure, which may limit performance in areas with poor connectivity.
Advanced optimization techniques require higher computational resources and periodic retraining, which may be challenging in remote installations.
Battery performance was modeled using generalized assumptions, while real-world behavior may vary due to temperature, cycles, and manufacturer specifications.
Only basic encryption protocols were considered, and comprehensive cybersecurity testing was beyond the scope of the study.
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