New energy power prediction of microgrid based on optimized SVM

With the increasing emphasis on renewable energy worldwide, the stable operation and efficient management of microgrids have become particularly important as platforms for integrating and optimizing new energy sources. New energy generation, such as wind and photovoltaic power, is intermittent and uncertain, posing challenges to power balance and scheduling within microgrids. To effectively address these challenges, this paper proposes a new energy power prediction method for microgrids based on Support Vector Machine (SVM). The study begins by analyzing the characteristics of new energy power generation, including the fluctuation patterns of output power from photovoltaic and wind sources, as well as their main influencing factors. Next, the SVM prediction model is constructed, utilizing historical data of new energy generation and its influencing factors as inputs. This model leverages the nonlinear mapping and regression analysis capabilities of SVM to accurately predict future power output. For model construction, radial basis function (RBF) is employed as the kernel function, and model parameters, including the penalty factor and kernel function parameter, which are optimized using cross-validation and grid search methods to enhance prediction accuracy and generalization ability. Experimental results demonstrate that the SVM-based new energy power prediction method achieves high prediction accuracy, providing robust decision support for power balance and scheduling in microgrids.