Photovoltaic Power Generation Prediction Using Data Clustering and Parameter Optimization

With the rapid development of the photovoltaic industry, photovoltaic power forecasting has become an urgent problem to be solved. In this paper, a method for predicting photovoltaic power based on data clustering and parameter optimization is proposed. The proposed method can be implemented as follows: firstly, the meteorological feature to be collected is determined by analyzing the physical model of the photovoltaic cell and the collected numerical weather information is divided into a set of categories by K-means. Then, the BP neural network is adopted and trained for individual categories, and an adaptive parameter optimization method is proposed to prevent model from local optimum. In the end, the proposed method is compared with other models to verify its effectiveness.