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About

Enmar Khalis is an emerging researcher at the intersection of artificial intelligence, machine learning, and renewable energy systems, with a focused specialization in predictive maintenance for photovoltaic (PV) technology. His work addresses one of the solar energy sector's most persistent challenges: performance degradation caused by dust accumulation and soiling, particularly in arid and semi-arid environments. Khalis has made notable contributions by developing AI-driven robotic cleaning systems that leverage machine learning algorithms to enable autonomous, real-time monitoring and predictive maintenance of solar panels. A distinctive thread running through his research is the innovative application of data augmentation techniques — including synthetic data generation, time-series transformations, and extreme condition simulation — to strengthen predictive model performance where real-world data may be limited. His three published works, collectively accumulating 14 citations within a remarkably short timeframe since 2024, signal growing interest from the research community in his practical, algorithm-driven approach. For students and researchers exploring smart energy solutions and AI-integrated sustainability systems, Khalis represents a forward-thinking voice pushing the boundaries of autonomous solar energy optimization.