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    An advanced machine learning-based approach for accurate forecasting of solar photovoltaic energy production
    (Springer London Ltd, 2025) Koca, Tarkan; Er, Mehmet Bilal; Kisecok, Busra
    Solar energy has a strategic importance among renewable energy sources due to its high potential and environmental sustainability features. Increasing energy demand and environmental concerns necessitate accurate estimation of solar energy production. In this study, the daily energy output (kWh) of a solar photovoltaic (PV) system is estimated using real operational data obtained from the Solar Power Plant of Baykan Denim Company, located in Malatya province of Turkey, with an installed capacity of 7090.47 kWp. The dataset includes key parameters such as irradiance (Wh/m(2)), temperature (degrees C), performance ratio (%), and temperature-corrected performance ratio (%). Four regression algorithms; Linear Regression, LSTM (Long Short-Term Memory), Random Forest, and Extreme Gradient Boosting (XGBoost) were comparatively analyzed under different data splitting strategies (70-30, 80-20, and 10-fold cross-validation). The results reveal that XGBoost consistently outperforms the other algorithms, achieving the highest accuracy and lowest error values. Specifically, the XGBoost model with 10-fold cross-validation achieved MAE: 0.006628 kWh, MSE: 0.000126 (kWh)(2), RMSE: 0.011222 kWh, and R-2: 0.998277, indicating near-perfect prediction capability. These findings demonstrate the robustness of the proposed framework and highlight its potential to ensure continuity in solar energy production and support efficient energy management processes.
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    PERFORMANCE AND ENVIRONMENTAL IMPACT ANALYSIS OF A GRID-CONNECTED PV POWER PLANT USING AI-BASED REAL-TIME MONITORING
    (Publ House Bulgarian Acad Sci, 2025) Kisecok, Busra; Koca, Tarkan; Citlak, Aydin
    In this study, the performance evaluation of grid-connected photovoltaic systems with a capacity of 4101.12 kWp installed by Baykan Denim company in Malatya province is carried out. The performance ratio, temperature, energy, radiation values used in the evaluation of the analysis are obtained every five minutes with the Solarify artificial intelligence-based performance monitoring network. The system efficiency is 16.53% and the performance ratio is 0.81. In addition, it was determined that 3022.61 tons of CO2 emissions were reduced annually thanks to the installed SPP (Solar power plant). It is concluded that the installation of PV systems provides considerable environmental and economic benefits.