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@article{191659,
        author = {Ketaki Raut and Harshal Runwal},
        title = {AI renewable energy smart intrusion detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {7297-7303},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191659},
        abstract = {Solar farms are being deployed at large scales to meet renewable energy targets, especially in geographically remote locations. However, the physical isolation of these sites increases the risk of unauthorized entry, damage, and theft of installed components. Traditional CCTV-based monitoring systems rely extensively on manual observation, which becomes impractical when coverage spans several acres and continuous monitoring is needed.
In this study, a Smart Intrusion Detection System (SIDS) is proposed, specifically designed for solar farm environments. The system integrates lightweight AI-based video analysis with edge-level deployment to identify suspicious activities in real time. Key approaches such as human-pose estimation, feature extraction and anomaly scoring using learned normal patterns were implemented and evaluated using publicly available datasets.
The developed approach demonstrated promising performance, achieving 95.2% accuracy and strong precision–recall balance. As the computation happens on low-power embedded devices, the system remains operational even with limited network access, making it a viable security solution for remote and large-scale solar installations.},
        keywords = {Solar farms; Edge processing; Intrusion detection; Pose-based analysis; Renewable energy security.},
        month = {January},
        }