PV power prediction based on feature selection and GMM clustering

Abstract

The increase in the installed capacity of photovoltaics (PV) brings great challenges to the stable operation of the power system. To improve the accuracy of PV power prediction, Spearman’s rank correlation coefficient and Grey Relation analysis (GRA) are combined to analyze and screen each meteorological feature to ensure the effectiveness and reasonableness of the input features. Based on the analysis of meteorological features, the Gaussian Mixture Models (GMM) clustering method is proposed to cluster the historical data sets. The historical data are clustered into two categories: simple PV power change and complex PV power change, which facilitates the model to learn the power change characteristics under different weather conditions. A Bidirectional Gated Recurrent Unit (BiGRU) prediction model is built to mine the timing properties of the two types of data and trained and tested separately. By setting several error evaluation indexes for comprehensive judgment, the methods in this paper are better than several other classical prediction methods and have a strong adaptability to different weather conditions.