Optimization of solar energy systems using GSM-based real-time data acquisition in hilly regions

Solar energy is a promising renewable resource, particularly in hilly regions where conventional energy access remains a challenge. However, optimizing solar energy capture in such terrains requires real-time monitoring and adaptive control mechanisms. This study presents a GSM (Global System for Mobile Communications)-based real-time data acquisition system integrated with IoT (Internet of Things) technology to enhance solar energy utilization in hilly areas. The system continuously monitors key parameters, including solar irradiance, module temperature, and ambient temperature, using high-precision sensors. Data are transmitted to a cloud portal for real-time analysis, enabling continuous performance evaluation and adaptive optimization of solar collectors. Over an 11 day observation period, solar irradiance variations were recorded and analyzed to determine daily average irradiance, peak solar hours, and energy potential. The results reveal a strong correlation between irradiance intensity and module temperature, with thermal efficiency consistently exceeding 99.2%, confirming the solar glass's effective heat retention capabilities. The temperature difference between the module and ambient conditions remained stable, ensuring minimal energy losses and optimized thermal performance. The calculated parameters, such as energy loss coefficient, temperature variations, and relative temperature differences, provide valuable insights into system efficiency. The findings demonstrate that real-time monitoring significantly enhances solar energy optimization, particularly in climate-sensitive and topographically challenging environments. This study underscores the importance of IoT-based energy management for improving collector performance, minimizing heat losses, and ensuring sustainable energy harvesting in hilly terrains. Future research should focus on expanding data collection across seasonal variations, integrating AI (Artificial intelligence)-based optimization algorithms, and exploring hybrid renewable energy solutions to further improve efficiency and adaptability.