article · IEEE Access
This study evaluated the effectiveness of various Machine Learning (ML) algorithms for predicting solar power generation, which is crucial for integrating renewable energy into power networks. Six algorithms, including CatBoost, Gradient Boosting Machines (GBMs), Multilayer Perceptron (MLP) regressor, Support Vector Machines (SVMs), XGBoost, and Random Forest (RF), were compared using a dataset of 4213 solar power generation records. Performance was assessed using R-squared (R2) scores for the whole dataset, training set, and test set, alongside consistency metrics. Random Forest achieved the highest overall R2 score of 0.940, while XGBoost showed the best test set performance with an R2 of 0.822. CatBoost and GBMs also performed strongly, but MLP and SVMs struggled with generalisation. The findings highlight the effectiveness of combining XGBoost and RF for improved solar power forecasts.
Accurate forecasting of solar power output is essential for the efficient and reliable integration of renewable energy into electricity grids. This research identifies effective machine learning approaches that can improve the precision of these predictions, supporting the stability and sustainability of modern energy systems.
This research provides insights into effective machine learning models for predicting solar power output. This could be used by energy companies, grid operators, and renewable energy developers to optimise grid integration and manage energy supply more efficiently. This is applied research, identifying effective techniques for a specific problem, and could inform the development of forecasting tools.
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Precise forecasting of solar power output is crucial for integrating renewable energy into power networks, improving efficiency and dependability. This study assesses the efficacy of several Machine Learning (ML) algorithms in predicting solar power generation through a detailed performance comparison. This paper analyzes six algorithms: CatBoost, Gradient Boosting Machines (GBMs), Multilayer Perceptron (MLP) regressor, Support Vector Machines (SVMs), XGBoost, and Random Forest (RF). Using a dataset of 4213 sets of solar power generation data, each model was trained and tested, with performance evaluated based on R-squared (R2) scores for the whole dataset, training set, and test set. Also, this study examined the mean and standard deviation of test set predictions to gauge how consistent each model was. The results showed that RF had the highest overall R2 score of 0.940 and a training set score of 0.971. XGBoost demonstrated exceptional performance on the test set, attaining a high R2 score of 0.822. CatBoost and GBMs exhibited strong performance, albeit with slightly lower R2 values of 0.786 and 0.829, respectively. Although the MLP regressor and SVMs exhibited high training scores, they encountered difficulties in generalizing to unfamiliar data. This paper highlights the effectiveness of combining XGBoost and RF techniques in improving the accuracy of solar power forecasts. The investigation focuses on enhancing the precision and reliability of renewable energy projections through a comprehensive comparison of various contemporary ML techniques.
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DOI: 10.1109/access.2024.3437191
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