Research on machine learning-based solar energy production prediction

Jiangtao Liu

doi:10.1117/12.3026841

15 March 2024 Research on machine learning-based solar energy production prediction

Jiangtao Liu

Proceedings Volume 13075, Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023); 130752I (2024) https://doi.org/10.1117/12.3026841
Event: Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023), 2023, Kunming, China

Abstract

With the rapid development of solar energy technology, machine learning based research on solar production prediction has become a key area to address the efficiency of solar power systems and sustainable energy planning. Using a variety of machine learning models, such as decision trees, support vector machines, and neural networks, this research delves deeply into the generation of solar energy. The experimental data covers different photovoltaic power plants, including polycrystalline solar panels and thin film solar cells, with a total generation capacity of 10 MW. The study firstly emphasizes the importance of model selection and performance evaluation, involving consideration of model complexity, computational efficiency and prediction accuracy. For the assessment of model performance, the Root Mean Square Error (RMSE) metric was used. The results show that the integrated model is able to produce accurate predictions on different PV plants, but the model performance may be affected by data clarity and quality. On the other hand, factors such as geographical location, PV plant characteristics and meteorological conditions also play a key role in model performance. The experimental results show that clear and accurate data are essential for improving model performance, while different meteorological conditions and geographical locations may lead to differences in model performance. In addition, the feasibility in terms of practical applications needs to be thoroughly considered in further research. This study provides important insights into machine learning approaches for solar energy production forecasting, which is expected to provide effective guidance for the planning and management of future renewable energy systems.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiangtao Liu "Research on machine learning-based solar energy production prediction", Proc. SPIE 13075, Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023), 130752I (15 March 2024); https://doi.org/10.1117/12.3026841

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KEYWORDS

Solar energy

Data modeling

Machine learning

Performance modeling

Photovoltaics

Solar radiation models

Solar cells

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