Enhancing Maximum Power Point Tracking through Ensemble Techniques

Abstract

Maximum Power Point Tracking (MPPT) plays a pivotal role in photovoltaic (PV) solar systems, streamlining the harnessing of available power and bolstering energy conversion efficiency. Its significance lies in its alignment with the global push to heighten the efficacy of renewable energy sources. This article unfolds a meticulous examination of the predictive modeling specific to solar energy. The investigation spans various machine learning models such as Linear Regression (LR), Support Vector Regression (SVR), XGBoost Regressor, and Ensemble Learning (EL), each dissected to reveal the intricacies involved in solar energy system modeling. The research, conducted across two unique datasets—Solar Power Generation and Solar Radiation Prediction, employed rigorous statistical evaluation to uncover the distinctions in accuracy, unity, and efficacy among the models. A standout finding was the Ensemble Learning model's superior performance, notably through applying techniques like Bagging Regressor. This approach transcended the individual models in both datasets by ingeniously amalgamating the predictions of various underlying models, leading to enhanced predictive precision. This article's insights contribute considerably to the domain of solar energy modeling, elevating Ensemble Learning as a powerful instrument for refining prediction accuracy. Furthermore, the juxtaposition of various modeling methodologies unveils valuable insights into their respective trade-offs, enriching the foundation for future exploration and real-world implementations within the renewable energy landscape. In setting a novel standard in solar energy forecasting, this study also resonates with the broader objectives of sustainable energy governance and ecological preservation.