Pattern Classification and PSO Optimal Weights Based Sky Images Cloud Motion Speed Calculation Method for Solar PV Power Forecasting

The motion of cloud over photovoltaic (PV) power station will directly cause the change of solar irradiance, which indirectly affects the prediction of minute-level PV power, so the tracking of cloud motion is very crucial. In this study, Block-matching algorithm, Optical Flow algorithm and feature matching algorithm are three prevailing methods. However, as a rigid registration method, Block-matching cannot obtain the parameters of cloud deformation or rotation. The accuracy of the optical flow, which is based on the assumption that the image grayscale is not changed, is easily disturbed by noise. When the image texture information is not rich enough, the accuracy of the feature matching will also be reduced. That is, in order to improve their robustness, they must be combined through a certain strategy. Therefore, a pattern classification and PSO optimal weights based sky images cloud motion speed calculation method for solar PV power forecasting (PCPOW) is proposed in this paper. The method consists of two parts. Firstly, we use k-means clustering method and texture features based on Gray-Level Co-occurrence Matrix (GLCM) to classify the clouds. Because texture can adequately reflect image information, compared with other image features, it can better take into account both the macro nature and the fine structure of images. Secondly, for different cloud classes, we build the corresponding combined calculation modeling to obtain cloud motion speed. The Particle Swarm Optimization algorithm is used to give different weights to different methods to adapt to different clouds. The performances of the method are investigated using real data recorded at Yunnan Electric Power Research Institute. Under the measurement of common precision index, the comparisons with various benchmark methods show the effectiveness of the proposed approaches over cloud tracing.