Enhancing Solar Power Generation Prediction: A Novel Grading Approach Leveraging Random Forest and Decision Tree Models

The    proposed    framework    represents    a     significant advancement in the field of solar power generation prediction, offering a comprehensive and effective approach to managing smart solar power plants. By integrating ensemble modeling and regression techniques, the framework provides accurate forecasts of solar power generation under varying weather conditions. The experimental results demonstrate the efficacy of the framework in accurately predicting solar power generation levels, with high training and testing accuracies achieved for the ensemble classification model and low error metrics obtained for the RandomForestRegressor model. The applications of the proposed framework extend beyond individual solar power plants to broader energy management strategies,   including    optimizing    production   schedules, enhancing grid integration, and promoting renewable energy penetration. By enabling proactive energy management and reducing reliance on non-renewable energy sources, the framework contributes to the advancement of sustainable energy systems and the mitigation of climate change impacts. Future research could focus on improving the accuracy and generalizability of the model by incorporating additional data sources, such as satellite imagery, atmospheric simulations, or real-time sensor data. This could enhance the model's ability to capture localized weather patterns and short-term fluctuations in solar radiation. Another area for future exploration is the development of more sophisticated modeling techniques, such as machine learning algorithms or hybrid models combining physical principles with data-driven approaches. There is potential for expanding the model's scope to incorporate additional factors influencing solar power generation, such as cloud cover, air pollution, or terrain variations. The proposed framework represents a valuable tool for decision-making in solar energy management, offering insights into future solar power generation potential and facilitating the transition towards a cleaner and greener energy infrastructure.

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