Research on machine learning-based solar energy production prediction

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.