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Title |
A Comparative Study of Machine Learning Approaches for Accurate Predictive Modeling of Solar Energy Generation |
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Author |
Khaled Chaaban and Najf Alfadl |
| Citation |
Vol. 24 No. 12 pp. 121-131 |
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Abstract |
Solar energy prediction poses a challenging task that requires robust models and precise data to accurately forecast solar energy yield, especially in grid areas with a large share of photovoltaics. Existing methods often rely on statistical or physical models, which have limitations in capturing the complex and non-linear relationships between weather variables and solar power generation. In this paper, we address this issue by comparing and evaluating different learning models, ranging from artificial neural networks (ANNs) and random forest models to long- and short-term memory (LSTM) networks, to predict the PV energy yield based on weather forecast data. A methodology has been developed to evaluate various models using real-world datasets from a large-scale industrial solar project, incorporating historical photovoltaic data, meteorological data, and solar irradiation data. The experimental results showed that the Random Forest Algorithm (RFR) consistently outperforms other algorithms, providing a mean absolute error (MAE) of 0.06 and a root mean square error (RMSE) of 0.15 when applied to historical meteorological datasets. The accuracy of the learning model was improved by combining meteorological data with a solar irradiation dataset to obtain an MAE of 0.03 and an RMSE of 0.09. Validation analysis has shown that the proposed model is highly effective in terms of both forecast accuracy and stability. The proposed methodology has the potential to provide valuable information to PV system operators, grid managers, and energy planners, facilitating the optimization of the use of solar energy resources. |
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Keywords |
Machine learning, neural networks, optimization, photovoltaics. |
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