Short-Term Prediction Method of Solar Photovoltaic Power Generation Based on Machine Learning in Smart Grid

In order to improve the accuracy of ultra short-term power prediction of the photovoltaic power generation system, a short-term photovoltaic power prediction method based on an adaptive k-means and Gru machine learning model is proposed. This method first introduces the construction process of the model and then builds a short-term photovoltaic power generation prediction model based on an adaptive k-means and Gru machine learning models. Then, the network structure and key parameters are determined through experiments, and the initial training set of the prediction model is selected according to the short-term photovoltaic power generation characteristics. And the adaptive k-means is used to cluster the initial training set and the photovoltaic power on the forecast day. The Gru model is trained on the initial training set data of each category, and the generated power is predicted in combination with the trained Gru model. Finally, considering three typical weather types, the proposed method is used for simulation analysis and compared with the other three traditional photovoltaic power generation single prediction models. The comparison results show that the proposed short-term photovoltaic power generation prediction method based on an adaptive k-means and Gru network has better effect, better robustness, and less error.

Automated summary

In order to improve the accuracy of ultra short-term power prediction of the photovoltaic power generation system, a short-term photovoltaic power prediction method based on an adaptive k-means and Gru machine learning model is proposed. The method determines the network structure and key parameters through experiments, and clusters the training set and the forecast data using adaptive k-means. The results show that the proposed method has better performance and robustness compared to traditional models.