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Abstract
This research is primarily concentrated on predicting the output of photovoitaic power, an essential field in the study of renewable energy. The paper comprehensively reviews various forecasting methodologies, transitioning from conventional physical and statistical methods to advanced machine learning (ML) techniques. A significant shift has been observed from traditional point forecasting to machine learning-based forecasting in solar power. This transition offers a broader and more detailed perspective for power system operators. The core of this research lies in applying and comparing three distinct Machine Learning algorithms for forecasting photovoltaic power output. The primary aim is to evaluate each method's accuracy and to identify the algorithm with the lowest prediction error. This comparative analysis is crucial for determining the most effective machine learning forecasting method, significantly contributing to the more reliable and efficient integration of renewable energy into power systems.
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Details
- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language:
- English
- Publication year:
- 2024
- Bibliographic description:
- Nassereddine K., Turzyński M., Lukianov M., Strzelecki R.: Advancing Solar Energy: Machine Learning Approaches for Predicting Photovoltaic Power Output// / : , 2024,
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/cpe-powereng60842.2024.10604373
- Sources of funding:
-
- COST_FREE
- Verified by:
- Gdańsk University of Technology
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