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@article{188716,
        author = {Vedangi},
        title = {AI-Driven Renewable Energy Security: Smart Intrusion Detection for Solar Panels},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {3211-3216},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188716},
        abstract = {Solar farms are becoming an important part of renewable energy production. At the same time, their remote location and wide coverage make them easy targets for theft, vandalism, and unauthorized entry. Existing surveillance methods such as CCTV depend mainly on human operators, which is not practical when large areas must be monitored continuously.
In this paper, we present a Smart Intrusion Detection System (SIDS) for solar panel farms. The system makes use of artificial intelligence (AI) and edge computing to detect unusual activities in real time. Techniques like pose estimation, feature extraction, and anomaly detection are combined and tested on benchmark datasets. The solution is designed to run on low-cost edge devices such as Raspberry Pi and NVIDIA Jetson boards, making it deployable in remote areas with limited connectivity.
The experiments showed that our system can achieve an accuracy of 95.2%, with precision, recall, and F1-score values of 92.7%, 94.1%, and 93.4% respectively. Since the system does not depend heavily on cloud servers, it is faster, more scalable, and cost-effective for protecting renewable energy installations.},
        keywords = {Solar panel security, Intrusion detection, Renewable energy, Edge AI, Anomaly detection, Pose estimation.},
        month = {December},
        }