Development of a Comprehensive Intelligent Model for Diagnostics and Forecasting of the Energy Efficiency of Photovoltaic Stations for Agricultural Facilities
Abdykadyrov A. Khidolda Y. Bekbayev A. Sarsenbayev Y. Kystaubayev N. Mustafoeva D.
February 2026Multidisciplinary Digital Publishing Institute (MDPI)
Energies
2026#19Issue 3
This study develops an intelligent real-time diagnostic and forecasting model for photovoltaic (PV) stations operating in the rural regions of Kazakhstan. Field measurements show that in Kazakhstan’s rural conditions, daily temperature fluctuations of 18–27 °C reduce PV output by 10–15%, solar irradiance variations of 800–1100 W/m2 cause an additional 8–12% deviation, and mineral-rich dust accumulation of 20–120 μm results in 3–5% power loss per 10 μm layer. Using the Beer–Lambert–Bouguer law, thermal degradation coefficients, and inverter droop models, the physical–mathematical behavior of PV degradation was described. The hybrid AI model (LSTM + XGBoost + Random Forest) achieved 89–91% accuracy in 24 h forecasting and 88–93% accuracy in fault detection. Overall, the proposed system reduces energy losses by 10–15% and shortens maintenance time by 18–24%, improving the reliability of rural PV stations in Kazakhstan.
artificial intelligence , dust accumulation , energy forecasting , inverter degradation , photovoltaic systems , rural Kazakhstan , temperature effects
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Department of Power Engineering, Satbayev University, Almaty, 050013, Kazakhstan
Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, National Research University, Tashkent, 100000, Uzbekistan
Department of Power Engineering
Tashkent Institute of Irrigation and Agricultural Mechanization Engineers
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026