Optimizing Solar Power Generation: A Gaussian Process Regression Approach to MPPT.

In the realm of sustainable energy, photovoltaic (PV) technology stands as a pivotal solution for clean energy generation. Continual advancements in PV technology have ushered in more efficient and cost-effective components, yet the precise localization and tracking of the maximum power point (MPP) remain critical for enhancing PV system performance. Over the years, various Maximum Power Point Tracking (MPPT) algorithms like Perturb and Observe (P&O) and Incremental Conductance (INC) have been established to address this challenge. In this proposed study, a novel approach using Gaussian Process Regression (GPR) for MPPT in PV systems is introduced. The GPR model is designed to predict the MPP accurately, thereby enhancing system efficiency and contributing to the advancement of sustainable energy generation. Implemented within the MATLAB/Simulink environment, the model utilizes a dataset comprising 1000 observations of solar irradiance, temperature, and corresponding voltages, where 120 datapoints among these were taken as sample for training and evaluation. The results demonstrate promising outcomes with a Mean Squared Error (MSE) of 1.2783 x 10^-5 and a Root Mean Squared Error (RMSE) of 0.0031, indicating high accuracy in predicting the MPP. This study underscores the effectiveness of GPR in optimizing PV system performance, supporting its adoption for sustainable energy applications and paving the way for further advancements in renewable energy technologies. [ABSTRACT FROM AUTHOR]