Topluluk öğrenme teknikleri yoluyla maksimum güç noktası takipini geliştirme

Maximum Power Point Tracking (MPPT) is an essential method in photovoltaic (PV) solar systems for optimizing the extraction of available power. This technique enhances energy conversion efficiency and aligns with ongoing efforts to improve the effectiveness of renewable energy sources. This thesis presents a systematic investigation into the predictive modeling of solar energy phenomena, specifically focusing on solar power generation and solar radiation prediction. Through the comprehensive evaluation of various individual machine learning models, including Linear Regression (LR), Support Vector Regression (SVR), and XGBoost Regressor, as well as an Ensemble Learning (EL) approach, the study elucidates the complexities and nuances of modeling solar energy systems. The analysis was conducted on two distinct datasets: Solar Power Generation and Solar Radiation Prediction. The individual models were rigorously assessed using multiple statistical metrics, revealing varying degrees of accuracy, fit, and performance. A remarkable discovery was the efficacy of the Ensemble Learning model, employing techniques such as Bagging Regressor, which consistently outperformed the individual models across both datasets. By adeptly aggregating the predictions of multiple underlying models, the EL approach achieved superior predictive accuracy, explaining an impressive proportion of the variance in both solar power generation and solar radiation. The findings of this research contribute significantly to the understanding of solar energy modeling, endorsing ensemble learning as a potent and versatile tool for enhancing prediction accuracy. Moreover, the comparative analysis sheds light on the trade-offs between different modeling techniques, offering guidance for future research and practical applications within the renewable energy sector. This thesis not only sets a new benchmark in the field of solar energy forecasting but also aligns with the broader imperatives of sustainable energy management and climate stewardship.