Machine learning-based estimation of power output in solar photovoltaic systems under real-world conditions
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Từ khóa:
Solar PV syste
power output estimation
time-series data
real-world operating condition
machine learning
Tóm tắt
Precise prediction of DC power output from photovoltaic (PV) systems under real conditions is essential for improving efficiency and detecting degradation. This paper presents a machine learning framework to forecast PV power using electrical signals (e.g., voltage and current) and environmental data (irradiance and temperature). Three regression models -eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and Artificial Neural Networks (ANN) - were trained on 13,923 samples from a 455.4 kWp solar PV plant in central Vietnam. The XGBoost model delivered the best performance with R² = 0.9998, mean absolute error (MAE) = 1.62 kWh, and root mean square error (RMSE) = 2.209 kWh, outperforming conventional methods. Additionally, the low computational demand of the developed model allows implementation on affordable hardware platforms, such as Raspberry Pi 4, enabling practical real-time monitoring and timely detection of PV performance degradation due to factors like panel defects, natural aging, and dust accumulation.
Tài liệu tham khảo
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Sep 30, 2025
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Nguyen, K. A., D. T. H. Do, H. K. Trinh, N. K. Nguyen, và N. B. Doan. “Machine Learning-Based Estimation of Power Output in Solar Photovoltaic Systems under Real-World Conditions”. Tạp Chí Khoa học Và Công nghệ - Đại học Đà Nẵng, vol 23, số p.h 9D, Tháng Chín 2025, tr 79-85, doi:10.31130/ud-jst.2025.23(9D).569E.
