Short-term Power Prediction Method of Photovoltaic Based on Output Clustering in Smart Grid

Abstract

Accurate photovoltaic power prediction can ensure the smart grid's safe and stable operation, as well as reasonable energy scheduling. Weather influences photovoltaic output, which is irregular and unstable, and photovoltaic output is similar under similar meteorological conditions. The paper proposes a solar power forecast model based on Mean-Shift clustering, support vector machine (SVM), and residual neural network (ResNet) in this regard. Firstly, the Mean-Shift algorithm is used to cluster similar days. Then, the SVM model is constructed to learn the similarity between the data of each meteorological type, and the similar day matching is performed on the forecast day. Finally, the short-term photovoltaic output prediction based on ResNet algorithm is carried out for the corresponding weather type. The suggested method's high forecast accuracy and stability are confirmed by experimental examination of a commercial photovoltaic power station.