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@article{172780,
        author = {Rahul Sharma and Lakshay Singhal and Gursharan Singh},
        title = {Enhancing Solar Panel Efficiency in Established Installations},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {9},
        pages = {827-831},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=172780},
        abstract = {Solar energy is an essential component of the global transition to renewable energy. However, the efficiency of solar panels degrades over time due to various environmental and operational factors. While the infrastructure of already established solar installations cannot be altered, efficiency improvements can focus on operational enhancements such as panel cleaning, performance monitoring, and solar forecasting. This paper explores a data-driven approach to optimizing solar panel maintenance by analyzing atmospheric factors and their impact on energy generation. 
By correlating environmental variables such as temperature, humidity, precipitation, and dew point with panel efficiency over an extended period, we aim to develop predictive models that optimize the timing of maintenance activities, particularly panel cleaning, to maximize efficiency while minimizing resource wastage. Our approach leverages machine learning for predictive modeling and utilizes Internet of Things (IoT) devices and weather data integration for real-time performance monitoring and optimization.},
        keywords = {},
        month = {February},
        }