Machine Learning-Based Prediction of Illuminance and Ultraviolet Irradiance in Photovoltaic Systems | ||||
| International Journal for Holistic Research | ||||
| Articles in Press, Accepted Manuscript, Available Online from 21 November 2024 PDF (3.61 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijhr.2024.308523.1025 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Montaser Abdelsattar   1; Ahmed AbdelMoety 1; Ahmed Emad-Eldeen2
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| 1Electrical Engineering Department, Faculty of Engineering, South Valley University, Qena 83523, Egypt | ||||
| 2Renewable Energy Science and Engineering Department, Faculty of Postgraduate Studies for Advanced Sciences (PSAS), Beni-Suef University, Beni-Suef 62511, Egypt | ||||
| Abstract | ||||
| Photovoltaic (PV) systems are indispensable in the renewable energy industry as they convert sunlight into electricity. Accurate determination of important factors such as illuminance and Ultraviolet (UV) irradiation is essential for optimizing the effectiveness and maintenance of these systems. The objective of this work is to evaluate the predictive performance of several Machine Learning (ML) models in estimating the amounts of light and UV radiation in PV systems, by comparing and contrasting their effectiveness. The models that were assessed include Support Vector Classification (SVC), Linear Regression (LR), eXtreme Gradient Boosting (XGBoost), Gradient Boosting (GB), Random Forest (RF), and CatBoost. The study employed a comprehensive dataset that encompassed measurements for temperature, humidity, UV, voltage, current, and illuminance. The data was preprocessed to remove invalid values and align indices. Afterwards, it was divided into separate training and testing sets. The main metrics used to train and evaluate each model were Root Mean Squared Error (RMSE) and the Coefficient of Determination (R²). The findings suggest that the Categorical Boosting (CatBoost) and RF models demonstrate greater performance in comparison to other models. This is evidenced by their ability to obtain the lowest RMSE and highest R² values for both illuminance and UV forecasts. More precisely, CatBoost algorithm obtained a RMSE of 16.088 and a R² of 0.999 for illuminance. Additionally, it achieved a RMSE of 0.228 and a R² of 0.990 for UV. However, LR and SVC had notably inferior results. The results offered valuable perspectives for enhancing decision-making procedures. | ||||
| Keywords | ||||
| Photovoltaic Systems; Machine Learning; Illuminance Prediction; Ultraviolet Irradiance Prediction; Renewable Energy | ||||
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