Solar Power Output Prediction with AI: Leveraging Meta Learner Ensemble Stacked Regressor

As the world shifts from fossil fuels to renewable energy, solar power has become essential in meeting global energy demands. The efficiency of solar energy depends on effectively capturing the environmental and physical factors that impact electricity production by solar panels. This study aims to leverage available data on power output and environmental conditions to build advanced machine learning models for predicting solar power. Models such as KNN, DNN, LGBM, RF, and a Meta Learner will be rigorously trained and fine-tuned using various data subsets and hyperparameter optimization. By employing the stacking regressor module from Scikit-learn, a Python machine-learning library, we successfully combined four distinct models, achieving an R-squared value of 0.68 in predicting solar power. This method not only improves the accuracy of solar power predictions but also enhances our ability to capture the complex data relationships critical for forecasting renewable energy.