Abstract
Accurate prediction of solar power generation is crucial for optimizing the integration of renewable energy into the grid and promoting its efficient use. In this study, we compare the performance of three machine learning techniques - Random Forest Regression, Gradient Boosting Machine, and Artificial Neural Network - for predicting hourly solar power generation in a region in Germany, using a dataset from the Open Power System Data website. The models were evaluated based on their R-squared value, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). Our findings indicate that Random Forest Regression outperforms the other techniques, demonstrating its robustness to noise, effective feature selection, and ensemble learning approach. The results have practical implications for grid management, energy storage planning, investment decisions, and policy development in the renewable energy sector. We acknowledge the limitations of our study, such as the limited dataset and basic feature engineering techniques and suggest future research directions to further improve solar power prediction accuracy, including exploring additional variables, advanced feature engineering methods, and alternative or hybrid models.
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Murdan, A.P., Armoogum, V. (2023). Comparing Machine Learning Techniques for Hourly Solar Power Generation Prediction. In: Lu, J., et al. Proceedings of the 9th IRC Conference on Science, Engineering, and Technology. IRC-SET 2023. Springer, Singapore. https://doi.org/10.1007/978-981-99-8369-8_34
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DOI: https://doi.org/10.1007/978-981-99-8369-8_34
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