Predicting Solar Irradiance Using Machine Learning Approaches: The Case of Duzce, Turkey
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PDF downloads: 15
Keywords:
Photovoltaic panels, Solar irradiance Prediction, Machine learning, Hybrid Gradient BoostingAbstract
This research focuses on predicting the solar irradiance received by a standard photovoltaic
panel in Duzce, Turkey, using various machine learning techniques. The methodologies evaluated include
Bagging Learning, Decision Tree Learning, Gradient Boosting Learning, LightGBM Learning, Random
Forest Learning, Ridge Regression Learning, and XGBoost Learning. Through a comprehensive
comparative analysis, a Hybrid Gradient Boosting Learning approach is proposed for enhanced accuracy.
The study utilizes an extensive dataset comprising meteorological and sensor data, including Temperature
(T), Dew Point (DP), Humidity (H), Wind Speed (W), Pressure (P), Precipitation (PP), Total Feed-in
Time (TFT), Total Operating Time (TOT), Total Energy Produced (TWO), Number of Grid Connections
(OGSC), Environment Temperature Value (ETV), Module Temperature Value (MTV), and Radiation
(RD). The dataset spans from 2019 to 2024, and the RD value is predicted based on the other variables.
Random search was employed for hyperparameter optimization of the machine learning algorithms, with
the data divided into training and testing sets with an 80%-20% split. Performance metrics such as Mean
Squared Error (MSE), Mean Absolute Error (MAE), R² (Coefficient of Determination), Explained
Variance Score (EVS), Median Absolute Error (MedAE), and Mean Absolute Percentage Error (MAPE)
were used to evaluate the models. The results indicate that XGBoost achieved the highest performance,
while the proposed Hybrid Gradient Boosting model showed significant improvement over traditional
Gradient Boosting. The performance evaluations of each method are detailed, with graphical
representations and histograms demonstrating the efficacy of the proposed hybrid approach.
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