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@article{176846,
        author = {Gursharan Singh and Lakshay Singhal and Rahul Sharma and Haider Abbas},
        title = {Improving Solar Panel Efficiency with Predictive Alerts from IoT and Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {6168-6172},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176846},
        abstract = {Solar energy is a cornerstone of the global transition toward sustainable power generation, yet the efficiency of solar panels in established installations often declines due to environmental and operational factors. This study presents a machine learning-driven approach to enhance the performance of existing solar infrastructure by predicting optimal maintenance schedules, particularly for panel cleaning. Utilizing real-time environmental data collected through IoT sensors—measuring temperature, humidity, dew point, and precipitation—several machine learning models were implemented to forecast efficiency drops and identify ideal cleaning windows. Models such as Recurrent Neural Networks (RNN), Random Forest, K-Nearest Neighbors (KNN), and Linear Regression were trained and evaluated using historical performance data and weather conditions. The results demonstrate that predictive modeling can significantly improve operational efficiency, reduce resource usage, and extend panel longevity. This approach enables a scalable and intelligent maintenance framework for solar farms, contributing to more sustainable and cost-effective energy generation.},
        keywords = {},
        month = {April},
        }